System

Abstract

A system is provided.SOLUTION: A system comprising: means for setting a task; means for generating an interactive artificial intelligence character imitating a historical great person or expert based on the set task; means for conducting a dialogue between the generated interactive artificial intelligence characters to derive a solution; means for receiving feedback from a user in the course of the dialogue and adjusting the contents of the dialogue based on the feedback; and means for generating a final solution based on the adjusted dialogue and presenting the solution to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Modern society faces a wide range of challenges, making it difficult to find effective solutions using only knowledge from a single field of expertise. While multifaceted approaches based on different perspectives are required, it is practically difficult to simultaneously utilize the knowledge of multiple experts and great figures. As a result, problem-solving takes longer and resources are less efficient. The present invention aims to provide a system that brings together the knowledge of historical figures and experts to solve these problems and create new value through dialogue. [Means for solving the problem]

[0005] The present invention provides a system including the following means: First, a means for setting a problem; Second, a means for generating an interactive AI character that imitates a historical figure or an expert based on the set problem; Third, a means for holding a dialogue between the generated interactive AI characters to derive a solution; Third, a means for receiving feedback from the user during the dialogue and adjusting the content of the dialogue based on the feedback; Third, a means for generating a final solution based on the adjusted dialogue and presenting it to the user. This enables effective problem solving from multiple perspectives.

[0006] A "problem" is a specific problem or theme that a user sets out to solve.

[0007] "Historical figures and experts" are people who have made notable achievements in history or who have a high level of knowledge and skill in a particular field of expertise.

[0008] An "interactive artificial intelligence character" is an AI-based character generated based on the way of thinking and recorded speech of a particular great person or expert.

[0009] "Generation" is the process of creating new characters or information using specific data and algorithms.

[0010] "Feedback" refers to comments or additional information that a user provides during the course of an interaction.

[0011] "Key Performance Indicators (KPIs)" are specific standards for measuring results for set goals.

[0012] "Weight adjustment" refers to the process of changing the importance of each item in the evaluation index.

[0013] A "solution" is an effective method or means of dealing with a set problem.

[0014] "User" refers to a person or organization that uses this system to solve a problem. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] System Overview

[0037] This system is an interactive AI system that runs on a server and allows users to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts to derive solutions to problems set by users through dialogue.

[0038] System Operation

[0039] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[0040] First-time setup

[0041] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs), which specify the problem they want the system to solve.

[0042] example:

[0043] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[0044] Submitting a Theme

[0045] The device sends the input tasks, goals, and KPIs to the server. This data becomes the basic information required for subsequent processing.

[0046] AI character generation

[0047] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[0048] example:

[0049] The server selects from a past database great people and experts related to "energy efficiency," such as "a certain inventor," "a certain physicist," or "a certain environmental activist," and generates AI characters based on their respective ways of thinking and recorded statements.

[0050] Starting a conversation

[0051] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues and confirm each character's proposal.

[0052] example:

[0053] Inventor AI: "We should consider new AC technologies for energy efficiency."

[0054] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[0055] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[0056] User Feedback

[0057] The user observes the content of the dialogue and inputs feedback or additional information as needed from the terminal, thereby correcting the dialogue in the direction desired by the user.

[0058] example:

[0059] A user provides feedback saying, "I would like more emphasis on environmental impact."

[0060] Send Feedback

[0061] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0062] Realigning the dialogue

[0063] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[0064] example:

[0065] The server increases the importance of "environmental impact" and generates new dialogue content as a result.

[0066] Re-interaction generation

[0067] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[0068] Deriving the final solution

[0069] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution from multiple perspectives.

[0070] example:

[0071] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[0072] Summary of embodiments

[0073] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on user feedback, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[0074] The processing flow will be explained below.

[0075] Step 1:

[0076] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0077] Step 2:

[0078] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0079] Step 3:

[0080] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[0081] Step 4:

[0082] The server generates interactive AI characters based on data on selected famous people and experts, using a method to model each character's way of thinking and speech patterns.

[0083] Step 5:

[0084] The server initiates an initial dialogue between the generated AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective.

[0085] Step 6:

[0086] The server sends the generated initial dialogue to the user's device, allowing the user to check the suggestions made by each character.

[0087] Step 7:

[0088] The user observes the content of the conversation using the terminal and provides feedback as needed, including information on the direction of the conversation and the evaluation indicators that are important.

[0089] Step 8:

[0090] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[0091] Step 9:

[0092] The server initiates the process of readjusting the dialogue based on the received feedback, changing the weights of the key performance indicators (KPIs) and reflecting their impact on the dialogue.

[0093] Step 10:

[0094] The server generates a new dialogue based on the adjusted KPIs and sends it back to the user's device. The user can review the new proposal and provide further feedback if necessary.

[0095] Step 11:

[0096] This process is repeated several times, and necessary adjustments are made to gradually approach an optimal solution. The server combines the results of each iteration to produce the final solution.

[0097] Step 12:

[0098] The server then sends the final solution to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[0099] Examples:

[0100] When a user sets a challenge related to "developing new energy resources," inputs "discovery of efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts who take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback, ultimately presenting a "prototype of a new energy system that uses sustainable alternating current" as the solution.

[0101] Example 1

[0102] 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."

[0103] In modern problem-solving, it is important to gather opinions and ideas from multiple perspectives. However, conventional systems require users to manually search for information and find the optimal solution from many sources, a time-consuming and labor-intensive process. Furthermore, it is difficult to integrate knowledge from different fields of expertise, making it difficult to derive the optimal solution. To solve this problem, a system is needed that allows users to efficiently gather opinions from multiple perspectives and use them to solve problems.

[0104] 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.

[0105] In this invention, the server includes means for transmitting the task, goal, and evaluation index input by the user from the terminal to the server, means for generating interactive AI characters imitating historical figures or experts based on the set task, and means for the generated interactive AI characters to converse with each other to derive a solution, thereby enabling the user to efficiently collect opinions from multiple perspectives and derive an optimal solution.

[0106] A "problem" is a specific problem or goal that a user wants to solve.

[0107] A "goal" is a specific result or target that a user wants to achieve when solving a problem.

[0108] "Key Performance Indicators (KPIs)" are specific criteria for measuring how effectively activities toward resolving issues are progressing.

[0109] "Terminal" means an electronic device that allows a user to access the system, enter information, and view results.

[0110] A "server" is a central processing unit that receives input data from users and performs calculations or generation based on that data.

[0111] An "interactive AI character" is a virtual character designed to engage in dialogue based on information provided by the user and provide advice and suggestions for solving problems.

[0112] "Feedback" refers to opinions or additional information provided by a user in response to a suggestion or discussion made by an interactive AI character.

[0113] "Dialogue content adjustment" refers to the process of modifying the behavior and speech of an interactive AI character based on user feedback.

[0114] The "final solution" is the optimal solution to the problem, derived through dialogue between the interactive AI character and feedback from the user.

[0115] This system is an interactive AI system that runs on a server, and allows users to use their devices to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts, and derives solutions through dialogue for problems set by the user.

[0116] The implementation of this system uses the following hardware and software:

[0117] Hardware: The device used by the user (computer, smartphone, tablet, etc.)

[0118] Hardware: Server (with a powerful processor and sufficient memory)

[0119] Software: Web browser or mobile application (for the user interface)

[0120] Software: Server-side programs (Python, TensorFlow, PyTorch, NLP algorithms, etc.)

[0121] The specific operation procedure of this system is as follows.

[0122] Users input the problem they want to solve, specific goals, and evaluation indicators (KPIs) into their device. This input operation embodies the problem they want to solve within the system. The input data is sent from the device to the server, which then uses it to generate characters of relevant historical figures and experts. In this process, the server uses an AI model based on each character's thinking style and speech records that have been saved in advance.

[0123] The generated AI characters converse with each other and generate an initial solution proposal. This is sent to the device, where the user observes the dialogue and provides feedback as needed. The feedback is sent back to the server, which adjusts the dialogue based on this feedback. A new dialogue is generated based on the adjusted evaluation index and sent back to the device. This process is repeated multiple times until the best solution is derived, incorporating the user's feedback.

[0124] For example, if a user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and the KPIs as "cost reduction, energy efficiency, and environmental impact," the server selects historical figures and experts related to "energy efficiency" and generates AI characters for each. The user observes the initial dialogue proposal and provides feedback, such as "I would like more emphasis on environmental impact." Based on that feedback, the server adjusts the dialogue content and generates a new dialogue that places greater importance on "environmental impact." Finally, the server can present the user with a "prototype of a new energy system using sustainable alternating current" as a concrete technology proposal.

[0125] In this way, users can efficiently obtain new approaches to solving problems from multiple perspectives that transcend their fields of expertise.

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] Users input the problem they want to solve, their specific goal, and key performance indicators (KPIs) into a terminal. For example, a user might enter information such as "developing new energy resources" as the problem, "discovering efficient and sustainable energy resources" as the goal, and "cost reduction, energy efficiency, and environmental impact" as the KPIs into a web browser form. After inputting this information, it is recorded as digital data on the terminal.

[0129] Step 2:

[0130] The device sends the input tasks, goals, and KPIs to the server. This is done via an HTTP POST request, and the data sent is in JSON format. The server receives this request, parses the data, and converts it into a usable format. The data (tasks, goals, KPIs) are sent as input data, and the server parses this data to extract the information needed for the next step.

[0131] Step 3:

[0132] The server generates characters of relevant historical figures and experts based on the received data. It uses an AI model based on pre-stored thinking patterns and speech records to create characters appropriate for the inquiry. This operation is achieved using deep learning libraries (e.g., TensorFlow and PyTorch). Specifically, it searches for relevant information from a database and provides input data to the model to carry out the generation process. The input for this step is data analyzed by the server itself, and the output is multiple generated AI characters.

[0133] Step 4:

[0134] The server initiates a dialogue between the generated AI characters, generates an initial proposal, and sends it to the device. Specifically, the server uses a natural language processing (NLP) algorithm to simulate a dialogue between the AI ​​characters. The generated dialogue content is sent to the device in JSON format and displayed on the device through a user interface. The input of this step is the generated AI character, and the output is the generated dialogue content.

[0135] Step 5:

[0136] The user observes the dialogue displayed on the terminal and inputs feedback or additional information as needed. Specific feedback can include adding a comment such as "I would like you to place more emphasis on environmental impact." This operation is performed using the terminal's input form and is again recorded as digital data. The input is the user's feedback, and the output is the feedback data recorded within the terminal.

[0137] Step 6:

[0138] The device sends the user's feedback to the server. This operation again uses an HTTP POST request, and the data sent is in JSON format. The server receives this data and parses it. The input is the feedback data, and the output is the feedback information parsed within the server.

[0139] Step 7:

[0140] The server adjusts the weights of the set evaluation indicators (KPIs) based on the received feedback. The server analyzes the feedback content and updates the parameters for generating dialogue based on the new importance. Specifically, it recalculates the weighting of the KPIs according to the feedback and reflects the feedback in the AI ​​model. The input of this step is the analyzed feedback information, and the output is the updated KPI parameters.

[0141] Step 8:

[0142] The server regenerates the dialogue content based on the adjusted KPI and sends it to the device. The server uses the deep learning model to generate a new dialogue and sends it in JSON format to the device. The device analyzes, displays, and provides it to the user. The input of this step is the updated KPI parameters, and the output is the regenerated dialogue content.

[0143] Step 9:

[0144] The user reviews the regenerated dialogue and provides additional feedback. The user can observe the new dialogue and enter further feedback as needed. This is done from the terminal and recorded as feedback data. The input to this step is the regenerated dialogue, and the output is the newly recorded feedback data.

[0145] Step 10:

[0146] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device. The server then compiles the final output of the AI ​​model and generates specific technical proposals and solutions. The generated solutions are sent to the device in JSON format and displayed on the user interface. The input of this step is the dialogue data and analysis results from multiple rounds, and the output is the final solution.

[0147] (Application example 1)

[0148] 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."

[0149] As modern security risks become more sophisticated and diverse, there is a need for effective solutions to the specific security issues faced by companies and individuals from multiple perspectives. However, it is difficult for users without specialized knowledge to understand complex security measures and select appropriate ones. Therefore, there is a need for a system that can make adjustable suggestions based on user feedback and derive optimal security measures.

[0150] 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.

[0151] In this invention, the server includes means for setting a problem, means for generating interactive AI characters that imitate historical figures or experts, means for the generated interactive AI characters to have a dialogue with each other and derive a solution, means for receiving feedback from a user and adjusting the content of the dialogue based on the feedback, means for generating a final solution and presenting it to the user, and means for evaluating security risks and proposing countermeasures, thereby enabling users to effectively obtain optimal solutions for specific security problems obtained from multiple perspectives.

[0152] Below are definitions of important terms included in the claims just rewritten.

[0153] "Means for setting the problem" refers to the interface and processing functions that allow users to input specific problems or goals they want to solve into the system.

[0154] An "interactive AI character" is an AI model that imitates historical figures and experts and makes suggestions for solving problems through dialogue.

[0155] "Means of generation" refers to the algorithms and processing functions for constructing an AI character from relevant characteristics, ways of thinking, and speech records based on the content of the task.

[0156] "Means for conducting dialogue and deriving solutions" refers to a processing function that allows multiple interactive AI characters to dialogue with each other and derive the optimal solution as a result.

[0157] "Means for receiving feedback and adjusting the content of the dialogue based on that feedback" refers to a processing function that allows the system to receive opinions and requests from users and dynamically change the direction and content of the dialogue based on those opinions and requests.

[0158] "Means for generating a final solution and presenting it to the user" refers to a processing function for generating an optimal solution based on the results of the adjusted dialogue and presenting it to the user in an easy-to-understand manner.

[0159] "Means for assessing security risks and proposing countermeasures" refers to a processing function for conducting risk assessments for specific security issues and proposing optimal countermeasures based on the results.

[0160] "Means for adjusting the weights of evaluation indicators" refers to a processing function for dynamically changing the importance and priority of evaluation indicators based on user feedback.

[0161] "Thinking methods and statements" refers to the thought processes and statements of historical figures and experts based on past data and records.

[0162] The system for realizing this invention is configured using the following hardware and software: a smartphone and server as hardware, and a smartphone app (iOS / Android), a server-side application, and a conversational AI system (based on GPT-4) as software.

[0163] System Overview

[0164] This system is designed as a conversational AI system that focuses specifically on solving security issues. When users ask for help identifying security risks and proposing appropriate countermeasures, it uses conversational AI characters that mimic historical figures and experts to provide an approach from multiple perspectives.

[0165] First-time setup

[0166] First, the user uses a smartphone app to input the specific security issues they want to solve, along with setting specific goals and evaluation indicators (KPIs), allowing the system to understand the details of the problem they are trying to solve.

[0167] example:

[0168] A user inputs the task of "preventing phishing attacks," sets the goal as "enable all employees to use email safely," and sets the KPIs as "prevention rate, cost, and feasibility."

[0169] Submitting a Theme

[0170] The terminal sends the input task, goal, and evaluation index to the server. This data serves as the basis for subsequent processing.

[0171] AI character generation

[0172] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[0173] example:

[0174] The server selects "security researchers," "engineers," "law enforcement officers," etc. from a past database and generates AI characters based on each person's way of thinking and recorded speech.

[0175] Starting a conversation

[0176] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues through the app and check each character's proposals.

[0177] example:

[0178] Security researcher AI: Regular employee training and simulations are effective in preventing phishing attacks

[0179] Engineer AI: "It is necessary to install and configure email filtering software."

[0180] Law enforcement AI: "Legal countermeasures for phishing attacks should also be considered"

[0181] User Feedback

[0182] The user observes the content of the dialogue and inputs feedback from the terminal as necessary, which allows the dialogue to be modified in the direction desired by the user.

[0183] example:

[0184] The user provides feedback saying, "I'd like you to narrow the proposals down to a more cost-focused approach."

[0185] Send Feedback

[0186] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0187] Realigning the dialogue

[0188] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[0189] Re-interaction generation

[0190] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[0191] Deriving the final solution

[0192] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain optimal security measures from multiple perspectives.

[0193] Example prompt sentence:

[0194] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[0195] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0196] Step 1:

[0197] Users use a smartphone app to input the security issues they want to solve, specific goals, and evaluation indicators (KPIs). This input includes issues such as "preventing phishing attacks," goals such as "enabling all employees to use email safely," and evaluation indicators such as "prevention rate, cost, and feasibility." The device collects this input data and sends it to the server.

[0198] Step 2:

[0199] The server analyzes the tasks, goals, and evaluation index data received from the device and generates a related interactive AI character. This generation uses the thinking patterns and speech records of historical figures and experts stored in a database. For example, characters such as "security researchers," "engineers," and "law enforcement officers" can be generated based on the thinking patterns and speech records.

[0200] Step 3:

[0201] The server initiates a dialogue between the generated conversational AI characters and derives an initial proposal. In this dialogue, each character expresses their opinion based on the set task, utilizing their respective expertise and experience. For example, the "security researcher AI" may suggest that "regular training and simulations for employees are effective in preventing phishing attacks." This initial proposal is then sent to the terminal.

[0202] Step 4:

[0203] The device receives the initial proposal from the server and displays it to the user. The user can observe the dialogue through the app and check each character's proposal. The user can provide feedback as needed based on the dialogue. For example, the user can input feedback such as, "I would like you to consider the proposal with more emphasis on cost."

[0204] Step 5:

[0205] The terminal sends the user's feedback to the server. The server receives and analyzes this feedback. Based on the analysis, the server prepares to adjust the content of the dialogue. Specifically, it adjusts the weights of each evaluation index based on the feedback and reflects it in the next dialogue.

[0206] Step 6:

[0207] The server regenerates dialogue between the interactive AI characters based on the adjusted evaluation index. The new dialogue content includes suggestions that reflect user feedback. For example, the characters may discuss the specific content of a training program that takes cost constraints into account and generate a new proposal. This regenerated dialogue is then sent to the terminal.

[0208] Step 7:

[0209] The terminal displays the regenerated dialogue content to the user and collects feedback again. The user can provide further feedback based on the new dialogue content. For example, the user can provide feedback such as, "I would like you to show me the prevention rate more specifically."

[0210] Step 8:

[0211] The server repeats the process of feedback and dialogue generation multiple times to finally derive the best solution. The final solution fully reflects the user's feedback and is most suitable for solving the problem. The server generates this final solution and sends it to the terminal.

[0212] Step 9:

[0213] The terminal displays the final solution from the server to the user, who can then implement specific security measures based on the final solution. For example, the terminal can initiate specific actions such as introducing a "sustainable and cost-effective training program."

[0214] Example prompt sentence:

[0215] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[0216] 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.

[0217] System Overview

[0218] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[0219] System Operation

[0220] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[0221] First-time setup

[0222] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0223] example:

[0224] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[0225] Submitting a Theme

[0226] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0227] AI character generation

[0228] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[0229] Utilizing the Emotion Engine

[0230] The server uses data acquired from the user's device to run an emotion engine and analyze the user's emotional state. The emotion engine identifies the user's current emotion through facial expression recognition and voice analysis.

[0231] example:

[0232] If the emotion engine recognizes that the user is feeling particularly anxious or suspicious as the conversation about energy resources progresses, that information is sent to the server.

[0233] Starting a conversation

[0234] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[0235] example:

[0236] Inventor AI: "We should explore new AC technologies for energy efficiency, but we should proceed cautiously and consider their environmental impact."

[0237] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[0238] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[0239] User Feedback

[0240] The user observes the content of the dialogue and inputs feedback from the terminal as needed, which is used to adjust the content of the dialogue.

[0241] example:

[0242] If a user provides feedback such as "more emphasis on environmental impact," the emotion recognized by the emotion engine is also reflected.

[0243] Send Feedback

[0244] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0245] Realigning the dialogue

[0246] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine. It changes the weights of the evaluation indicators (KPIs) and reflects their influence on the dialogue. Based on the information from the emotion engine, it makes suggestions that are appropriate for the user's emotional state.

[0247] example:

[0248] The server increases the importance of "environmental impact," which results in new dialogue content being generated.

[0249] Re-interaction generation

[0250] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[0251] Deriving the final solution

[0252] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[0253] example:

[0254] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[0255] Summary of embodiments

[0256] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on the user's feedback and emotional information, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[0257] The processing flow will be explained below.

[0258] Step 1:

[0259] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0260] Examples: "Developing new energy resources," "Discovering efficient and sustainable energy resources," "Cost reduction, energy efficiency, and environmental impact."

[0261] Step 2:

[0262] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0263] Step 3:

[0264] Based on the received data, the server starts the process of selecting the appropriate person or expert, extracting relevant historical data and speech records from the database.

[0265] Step 4:

[0266] The server generates interactive AI characters based on data on selected great people and experts, using a method to model each character's way of thinking and speech patterns.

[0267] Step 5:

[0268] The server runs an emotion engine using data acquired from the user's device to analyze the user's emotional state. The emotion engine identifies the user's emotions through facial expression recognition and voice analysis.

[0269] Step 6:

[0270] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[0271] Step 7:

[0272] The server transmits the generated dialogue content to the user's device, allowing the user to check the suggestions made by each character.

[0273] Step 8:

[0274] The user observes the content of the dialogue and inputs feedback as needed, including information about the direction of the dialogue and the evaluation indicators that are important.

[0275] Step 9:

[0276] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[0277] Step 10:

[0278] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine, changing the weights of the evaluation indicators (KPIs) and reflecting their influence on the dialogue.

[0279] Step 11:

[0280] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[0281] Step 12:

[0282] After repeating this process several times and making any necessary adjustments, the server generates the best solution and presents it to the user's device.

[0283] Specific examples

[0284] Example: If a user sets a task related to "developing new energy resources" and inputs "discovering efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts that take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback. Furthermore, using emotional information recognized by the emotion engine, proposals are made that take into account the user's psychological needs and doubts. Finally, a "prototype of a new energy system using sustainable alternating current" is presented as the solution.

[0285] Example 2

[0286] 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."

[0287] Conventional conversational AI systems make unilateral suggestions without considering the user's emotional state, making it difficult to provide flexible solutions that reflect the user's actual situation and feelings. It is also difficult to derive optimal solutions from multiple perspectives for complex problems. This has resulted in problems where users are unable to obtain satisfactory solutions, undermining the usefulness of the system.

[0288] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input a task, a goal, and an evaluation index using a terminal; means for transmitting the input information from the terminal to the server; means for extracting related history data from a database based on the received task and evaluation index and generating an interactive AI character; means for using an emotion engine to analyze the emotional state of the user; means for conducting a dialogue using the generated interactive AI character and adjusting the content of the proposal; means for receiving feedback from the user during the dialogue and readjusting the content of the dialogue based on the feedback; and means for generating a final solution based on the adjusted dialogue and presenting it to the user. This makes it possible to provide flexible and multifaceted solutions that take into account the user's emotional state and feedback.

[0289] A "terminal" is an electronic device through which a user inputs tasks, goals, and evaluation indicators through an interface and communicates with a server.

[0290] A "server" is a computing device that receives information sent by users, generates interactive AI characters, analyzes emotions, and readjusts the dialogue based on the feedback.

[0291] A "problem" is a specific problem or theme that a user wants to solve.

[0292] A "goal" is a specific objective or purpose that a user aims to achieve.

[0293] "Key performance indicators (KPIs)" are standards or indicators used to measure the degree of achievement of set tasks or goals.

[0294] An "interactive artificial intelligence character" is a virtual intelligent agent that imitates historical figures or experts and engages in dialogue with the user.

[0295] "Historical data" is basic information for generating AI characters, such as the thinking methods and statements of great people and experts from the past.

[0296] An "emotion engine" is a software engine that analyzes a user's facial expressions and voice data to identify their emotional state.

[0297] "Feedback" refers to opinions and requests that users input regarding the content of the dialogue.

[0298] "Dialogue readjustment" is the process of modifying the dialogue based on the user's feedback and emotional state to make more appropriate suggestions.

[0299] A "solution" is a final proposal or response to a set problem.

[0300] System Overview

[0301] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[0302] Hardware and Software

[0303] The system is implemented using the following hardware and software.

[0304] Terminal: The device through which users access the system and enter tasks and goals. Examples include PCs, tablets, and smartphones.

[0305] Server: A machine that processes data, generates AI characters, analyzes emotions, and presents solutions. Apache or Nginx is used as the web server.

[0306] Database: A database for storing historical data and user feedback information. Examples include MySQL and PostgreSQL.

[0307] Emotion engine: Software that analyzes the user's facial expressions and voice data to recognize emotions. For example, Python's OpenCV library and voice analysis library are used.

[0308] Generative AI model: An artificial intelligence model for generating character scripts. Examples include natural language generation models such as GPT-3.

[0309] Examples of specific examples and prompts

[0310] Specific examples

[0311] The specific method of using the system is shown below.

[0312] 1. A user accesses the system using a terminal and enters the task of "developing new energy resources."

[0313] 2. The user sets the goal as "discover efficient and sustainable energy resources" and inputs "cost reduction, energy efficiency, and environmental impact" as evaluation indicators (KPIs).

[0314] 3. The device sends this information to the server.

[0315] 4. Based on the received information, the server extracts relevant historical data from the database and uses the GPT-3 model to generate characters that imitate famous people and experts.

[0316] 5. The server initiates a dialogue with the generated character. The generated script includes suggestions such as, "We should consider new AC technology to pursue energy efficiency."

[0317] 6. The device captures the user's facial expressions and voice and sends the data to the server.

[0318] 7. The server uses an emotion engine to analyze the user's emotional state and adjust the dialogue content.

[0319] Prompt Sentence Examples

[0320] Below is an example of a prompt sentence that is input to the generative AI model.

[0321] "You are a physicist working on the development of new energy resources. Your goal is to discover efficient and sustainable energy resources. Your key performance indicators (KPIs) are cost reduction, energy efficiency, and environmental impact. Please use your expertise to make proposals to achieve this goal."

[0322] In this way, an interactive AI character is generated and makes flexible suggestions that take into account the user's emotional state. This allows the user to obtain new approaches to problem-solving from multiple perspectives. The above is an embodiment of the invention based on the scope of the claims.

[0323] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0324] Step 1:

[0325] The user uses a terminal to input the assignment, goals, and evaluation indicators.

[0326] Input: Problem (e.g., "Develop new energy resources"), Goal (e.g., "Discover efficient and sustainable energy resources"), Evaluation Metrics (e.g., "Cost reduction, energy efficiency, environmental impact").

[0327] What happens: A user opens a web browser and uses the system's interface to enter tasks into text boxes and select goals and metrics from drop-down menus.

[0328] Output: The data entered by the user is displayed in the input field on the terminal.

[0329] Step 2:

[0330] The terminal collects the input information and sends it to the server.

[0331] Inputs: User-entered tasks, goals, and KPIs.

[0332] Specific behavior: The device creates an HTTP request, packages the user-entered data as a JSON object, and sends a POST request to the server's API.

[0333] Output: User input data is sent to the server.

[0334] Step 3:

[0335] The server analyzes the received data and saves it as basic information for generating AI characters.

[0336] Input: User input data sent from the terminal in JSON format.

[0337] Specific operations: The server parses the data received by the parser and saves it to a database. The API is built using the Python Flask framework and the data is stored in a MySQL database.

[0338] Output: User's assignments, goals, and metrics are stored in a database.

[0339] Step 4:

[0340] The server extracts historical data for the appropriate great and specialist characters from the database.

[0341] Input: Saved user assignments, goals, and metrics.

[0342] What it does: The server uses SQL queries to retrieve historical data on past great people and experts from a database, then parses the information using the Natural Language Toolkit (NLTK).

[0343] Output: Historical data is extracted and used to generate an interactive AI character.

[0344] Step 5:

[0345] The server generates traits and scripts for each character based on the extracted data.

[0346] Input: History data, prompt statement.

[0347] Specific operation: The server inputs a prompt into a generative AI model (e.g., GPT-3) to generate a character script. The prompt includes a specific perspective on "developing new energy resources."

[0348] Output: A script is generated for each character.

[0349] Step 6:

[0350] The device captures the user's facial expressions and voice and sends the data to a server.

[0351] Input: User's facial expression data, voice data.

[0352] Specific operation: The device's camera and microphone are used to collect the user's facial expression and voice data, which are then sent to the server in real time.

[0353] Output: Facial expression data and voice data are sent to the server.

[0354] Step 7:

[0355] The server uses an emotion engine to analyze the user's emotional state.

[0356] Input: Facial expression data and voice data sent from the device.

[0357] How it works: The collected data is processed using Python's OpenCV library and voice analysis library. The emotion engine identifies emotions from the user's facial expressions and voice and saves the results.

[0358] Output: The user's emotional state is identified and stored in a database.

[0359] Step 8:

[0360] The server initiates a dialogue with the generated AI character and executes the dialogue script.

[0361] Input: User's task, goal, metrics, and emotional state.

[0362] What it does: The server executes the character script generated earlier and presents interactive suggestions to the user. This process happens in real time.

[0363] Output: The user is presented with a concrete suggestion.

[0364] Step 9:

[0365] The user observes the content of the dialogue and inputs feedback from the terminal.

[0366] Input: User feedback on the proposal.

[0367] Specific action: The user enters "I would like more emphasis on environmental impact" in the feedback box in the dialogue window and clicks the submit button.

[0368] Output: Feedback is sent to the terminal.

[0369] Step 10:

[0370] The terminal transmits the feedback data to the server.

[0371] Input: Feedback entered by the user.

[0372] What happens: The feedback is packaged in JSON format and sent back to the server's API.

[0373] Output: User feedback is sent to the server.

[0374] Step 11:

[0375] The server readjusts the dialogue based on feedback and emotion engine data.

[0376] Input: User feedback, emotional state data.

[0377] What it does: The server analyzes the feedback and emotional data, applies it to each character's script, and inputs new prompts into the generative AI model to regenerate the dialogue.

[0378] Output: A new dialogue is created.

[0379] Step 12:

[0380] The server creates a new dialogue and sends it to the terminal.

[0381] Input: Calibrated performance indicators (KPIs) and sentiment information.

[0382] What it does: The server runs the adjusted script, generates new dialogue, and sends it to the device, where the user can see the new suggestions.

[0383] Output: The new dialogue is displayed on the terminal.

[0384] Step 13:

[0385] After multiple rounds of interaction and feedback, the server generates the best solution and presents it to the user's device.

[0386] Input: History of each interaction and feedback, emotional state data.

[0387] Specific operation: The server comprehensively evaluates each dialogue and feedback and generates a final solution, which involves creating a specific technical proposal using the final prompt sentence of the generative AI model and sending it to the device.

[0388] Output: The best solution is displayed on the user's terminal.

[0389] (Application example 2)

[0390] 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."

[0391] In autonomous vehicles, there is a need to appropriately recognize passenger emotional states and adjust the in-vehicle environment and route accordingly to enhance passenger comfort and safety. However, conventional autonomous vehicle systems lack a means for recognizing passenger emotional states in real time and taking appropriate action based on the state. To solve this problem, it is necessary to accurately grasp passenger emotions and reflect that information in the autonomous driving system. The present invention aims to solve these problems and realize safer and more comfortable autonomous vehicle operation.

[0392] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0393] In this invention, the server includes means for setting a task, means for generating an interactive AI character that imitates a historical figure or an expert based on the set task, means for conducting a dialogue between the generated interactive AI characters and deriving a solution, means for receiving feedback from a user during the dialogue and adjusting the content of the dialogue based on the feedback, means for generating a final solution based on the adjusted dialogue and presenting it to the user, and means for using an emotion recognition engine that recognizes passenger emotions and adjusting the operation and in-vehicle environment of the autonomous vehicle based on the emotions, thereby making it possible to recognize passenger emotional states in real time and dynamically adjust the in-vehicle environment and operating route accordingly.

[0394] The "means for setting the problem" is a function that allows the user to input the problem they want to solve, specific goals, and evaluation indicators into the system, and then the system clearly sets the problem to be solved based on that.

[0395] "Conversational AI characters" refer to AI designed to mimic historical figures or experts and to engage in dialogue based on user input and tasks.

[0396] "Means for deriving solutions" is a system function in which interactive AI characters converse with each other and generate optimal solutions based on collected information and user feedback.

[0397] "Means for receiving feedback" refers to a function for collecting information and opinions provided by users during the dialogue process and reflecting them in adjusting the operation of the entire system and the content of the dialogue.

[0398] "Means for adjusting the content of the dialogue" refers to a system function for optimizing the statements and actions of the interactive AI character based on feedback from the user.

[0399] The "means for generating a final solution and presenting it to the user" is a system function for finally presenting to the user the solution derived through multiple rounds of dialogue and feedback.

[0400] An "emotion recognition engine" is a technology that uses devices such as cameras and microphones to analyze a user's facial expressions and voice and recognize their emotional state in real time.

[0401] "Means for adjusting the operation and in-vehicle environment of an autonomous vehicle based on emotions" refers to a system function that dynamically optimizes the route of an autonomous vehicle and the environment, such as lighting and sound, inside the vehicle, based on the user's emotional information obtained by the emotion recognition engine.

[0402] System Overview

[0403] The system of the present invention is an AI system for autonomous vehicles that combines a conversational AI character and an emotion recognition engine, allowing it to grasp the emotional state of passengers in real time and adjust the in-car environment and route accordingly.

[0404] Hardware and software used

[0405] Hardware:

[0406] camera

[0407] microphone

[0408] lighting adjustment device

[0409] Autonomous driving control systems (e.g., NVIDIA DRIVE)

[0410] Various sensors (temperature, humidity, lighting, etc.)

[0411] software:

[0412] Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[0413] Self-driving APIs (e.g., Waymo APIs)

[0414] Conversational AI systems (such as ChatGPT)

[0415] Emotion Recognition and Data Processing

[0416] The server uses a camera and microphone to capture the passenger's facial expressions and voice, and sends them to an emotion recognition engine, which analyzes this data to determine the passenger's emotional state, for example, whether they are stressed or relaxed.

[0417] Conversational AI character generation

[0418] Based on the passenger's input tasks and evaluation criteria, the server generates an interactive AI character that mimics historical figures and experts and interacts with them according to specific scenarios.

[0419] Dialogue coordination and autonomous driving

[0420] The server dynamically adjusts the autonomous vehicle's in-car environment based on emotion recognition. For example, if a passenger feels stressed, the server will automatically play relaxing music and adjust the lighting to softer levels. The vehicle's route will also be optimized based on the passenger's emotional state. This includes avoiding traffic jams and selecting scenic routes.

[0421] User feedback and review

[0422] The user can provide feedback about the conversation and the in-car environment through the terminal, and the server will receive this feedback and reflect it in the next conversation and in-car environment adjustments.

[0423] Examples of prompt statements

[0424] Below are some examples of prompts that can be passed to a conversational AI system using emotion recognition results:

[0425] text

[0426] The emotions of the passenger next to you are: {"happiness": 0.1, "stress": 0.8}. Suggest how to respond.

[0427] Specific examples

[0428] For example, if a passenger sets the task of "developing new energy resources," the goal of "discovering efficient and sustainable energy resources," and the KPIs of "cost reduction, energy efficiency, and environmental impact," the conversational AI character will begin a dialogue based on this. If the emotion recognition engine detects that the passenger is feeling stressed, the AI ​​character will make relaxing suggestions, and the autonomous driving system will select a scenic route.

[0429] In this way, the system of the present invention can dynamically optimize the operation and interior environment of an autonomous vehicle based on the emotional state of the passengers, providing a safer and more comfortable travel experience.

[0430] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0431] Step 1:

[0432] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs). This allows the system to define a specific problem. The input data includes the problem name, goal, and KPI. The input data is sent to the server in a data structure such as JSON format.

[0433] Step 2:

[0434] The device sends the inputted issues, goals, and evaluation indicators (KPIs) to the server. This data is sent reliably because it is the basic information for the system. The server analyzes the received data and generates a problem-solving scenario. This prepares the information necessary for the next processing step.

[0435] Step 3:

[0436] The server then begins the process of generating a conversational AI character based on the received assignment and evaluation indicators. This process includes extracting relevant historical data and speech records from the database. For example, it references the thinking methods and speech records of specific figures and experts and constructs the character's dialogue script based on that information.

[0437] Step 4:

[0438] The server runs an emotion recognition engine using data acquired from the user's device to analyze the user's emotional state. The emotion recognition engine identifies the user's current emotion through facial expression recognition and voice analysis. The input data is image and voice data, and the output is an evaluation of the user's emotional state. For example, information such as "high stress level" or "relaxed" can be obtained.

[0439] Step 5:

[0440] Based on the output from the emotion recognition engine, the server initiates a dialogue between the generated conversational AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The input data is the evaluation data of the emotional state and the dialogue script, and the output is the content of the dialogue between each character. Specific actions include suggestions made by the AI ​​characters.

[0441] Step 6:

[0442] During the dialogue, the user can input feedback from the terminal, which then sends the feedback to the server. The feedback is used to adjust the dialogue content. The input data is the user's feedback content, and the output data is a dialogue revision proposal based on that feedback.

[0443] Step 7:

[0444] The server readjusts the dialogue based on the received feedback and emotional information from the emotion recognition engine. It changes the weights of the evaluation indicators (KPIs) and reflects their impact on the dialogue. Specific operations include regenerating the dialogue content. The input data are the revised feedback and KPIs, and the output is a new dialogue scenario.

[0445] Step 8:

[0446] The server generates a new dialogue based on the adjusted evaluation indicators (KPIs) and emotional information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary. The input data is the new dialogue scenario, and the output is the proposal to the user.

[0447] Step 9:

[0448] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives. The input data is the accumulated dialogue history and feedback, and the output is the final solution.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] [Second embodiment]

[0453] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0454] 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.

[0455] 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).

[0456] 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.

[0457] 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.

[0458] 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).

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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."

[0465] System Overview

[0466] This system is an interactive AI system that runs on a server and allows users to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts to derive solutions to problems set by users through dialogue.

[0467] System Operation

[0468] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[0469] First-time setup

[0470] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs), which specify the problem they want the system to solve.

[0471] example:

[0472] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[0473] Submitting a Theme

[0474] The device sends the input tasks, goals, and KPIs to the server. This data becomes the basic information required for subsequent processing.

[0475] AI character generation

[0476] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[0477] example:

[0478] The server selects from a past database great people and experts related to "energy efficiency," such as "a certain inventor," "a certain physicist," or "a certain environmental activist," and generates AI characters based on their respective ways of thinking and recorded statements.

[0479] Starting a conversation

[0480] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues and confirm each character's proposal.

[0481] example:

[0482] Inventor AI: "We should consider new AC technologies for energy efficiency."

[0483] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[0484] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[0485] User Feedback

[0486] The user observes the content of the dialogue and inputs feedback or additional information as needed from the terminal, thereby correcting the dialogue in the direction desired by the user.

[0487] example:

[0488] A user provides feedback saying, "I would like more emphasis on environmental impact."

[0489] Send Feedback

[0490] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0491] Realigning the dialogue

[0492] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[0493] example:

[0494] The server increases the importance of "environmental impact" and generates new dialogue content as a result.

[0495] Re-interaction generation

[0496] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[0497] Deriving the final solution

[0498] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution from multiple perspectives.

[0499] example:

[0500] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[0501] Summary of embodiments

[0502] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on user feedback, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[0503] The processing flow will be explained below.

[0504] Step 1:

[0505] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0506] Step 2:

[0507] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0508] Step 3:

[0509] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[0510] Step 4:

[0511] The server generates interactive AI characters based on data on selected famous people and experts, using a method to model each character's way of thinking and speech patterns.

[0512] Step 5:

[0513] The server initiates an initial dialogue between the generated AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective.

[0514] Step 6:

[0515] The server sends the generated initial dialogue to the user's device, allowing the user to check the suggestions made by each character.

[0516] Step 7:

[0517] The user observes the content of the conversation using the terminal and provides feedback as needed, including information on the direction of the conversation and the evaluation indicators that are important.

[0518] Step 8:

[0519] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[0520] Step 9:

[0521] The server initiates the process of readjusting the dialogue based on the received feedback, changing the weights of the key performance indicators (KPIs) and reflecting their impact on the dialogue.

[0522] Step 10:

[0523] The server generates a new dialogue based on the adjusted KPIs and sends it back to the user's device. The user can review the new proposal and provide further feedback if necessary.

[0524] Step 11:

[0525] This process is repeated several times, and necessary adjustments are made to gradually approach an optimal solution. The server combines the results of each iteration to produce the final solution.

[0526] Step 12:

[0527] The server then sends the final solution to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[0528] Examples:

[0529] When a user sets a challenge related to "developing new energy resources," inputs "discovery of efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts who take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback, ultimately presenting a "prototype of a new energy system that uses sustainable alternating current" as the solution.

[0530] Example 1

[0531] 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."

[0532] In modern problem-solving, it is important to gather opinions and ideas from multiple perspectives. However, conventional systems require users to manually search for information and find the optimal solution from many sources, a time-consuming and labor-intensive process. Furthermore, it is difficult to integrate knowledge from different fields of expertise, making it difficult to derive the optimal solution. To solve this problem, a system is needed that allows users to efficiently gather opinions from multiple perspectives and use them to solve problems.

[0533] 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.

[0534] In this invention, the server includes means for transmitting the task, goal, and evaluation index input by the user from the terminal to the server, means for generating interactive AI characters imitating historical figures or experts based on the set task, and means for the generated interactive AI characters to converse with each other to derive a solution, thereby enabling the user to efficiently collect opinions from multiple perspectives and derive an optimal solution.

[0535] A "problem" is a specific problem or goal that a user wants to solve.

[0536] A "goal" is a specific result or target that a user wants to achieve when solving a problem.

[0537] "Key Performance Indicators (KPIs)" are specific criteria for measuring how effectively activities toward resolving issues are progressing.

[0538] "Terminal" means an electronic device that allows a user to access the system, enter information, and view results.

[0539] A "server" is a central processing unit that receives input data from users and performs calculations or generation based on that data.

[0540] An "interactive AI character" is a virtual character designed to engage in dialogue based on information provided by the user and provide advice and suggestions for solving problems.

[0541] "Feedback" refers to opinions or additional information provided by a user in response to a suggestion or discussion made by an interactive AI character.

[0542] "Dialogue content adjustment" refers to the process of modifying the behavior and speech of an interactive AI character based on user feedback.

[0543] The "final solution" is the optimal solution to the problem, derived through dialogue between the interactive AI character and feedback from the user.

[0544] This system is an interactive AI system that runs on a server, and allows users to use their devices to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts, and derives solutions through dialogue for problems set by the user.

[0545] The implementation of this system uses the following hardware and software:

[0546] Hardware: The device used by the user (computer, smartphone, tablet, etc.)

[0547] Hardware: Server (with a powerful processor and sufficient memory)

[0548] Software: Web browser or mobile application (for the user interface)

[0549] Software: Server-side programs (Python, TensorFlow, PyTorch, NLP algorithms, etc.)

[0550] The specific operation procedure of this system is as follows.

[0551] Users input the problem they want to solve, specific goals, and evaluation indicators (KPIs) into their device. This input operation embodies the problem they want to solve within the system. The input data is sent from the device to the server, which then uses it to generate characters of relevant historical figures and experts. In this process, the server uses an AI model based on each character's thinking style and speech records that have been saved in advance.

[0552] The generated AI characters converse with each other and generate an initial solution proposal. This is sent to the device, where the user observes the dialogue and provides feedback as needed. The feedback is sent back to the server, which adjusts the dialogue based on this feedback. A new dialogue is generated based on the adjusted evaluation index and sent back to the device. This process is repeated multiple times until the best solution is derived, incorporating the user's feedback.

[0553] For example, if a user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and the KPIs as "cost reduction, energy efficiency, and environmental impact," the server selects historical figures and experts related to "energy efficiency" and generates AI characters for each. The user observes the initial dialogue proposal and provides feedback, such as "I would like more emphasis on environmental impact." Based on that feedback, the server adjusts the dialogue content and generates a new dialogue that places greater importance on "environmental impact." Finally, the server can present the user with a "prototype of a new energy system using sustainable alternating current" as a concrete technology proposal.

[0554] In this way, users can efficiently obtain new approaches to solving problems from multiple perspectives that transcend their fields of expertise.

[0555] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0556] Step 1:

[0557] Users input the problem they want to solve, their specific goal, and key performance indicators (KPIs) into a terminal. For example, a user might enter information such as "developing new energy resources" as the problem, "discovering efficient and sustainable energy resources" as the goal, and "cost reduction, energy efficiency, and environmental impact" as the KPIs into a web browser form. After inputting this information, it is recorded as digital data on the terminal.

[0558] Step 2:

[0559] The device sends the input tasks, goals, and KPIs to the server. This is done via an HTTP POST request, and the data sent is in JSON format. The server receives this request, parses the data, and converts it into a usable format. The data (tasks, goals, KPIs) are sent as input data, and the server parses this data to extract the information needed for the next step.

[0560] Step 3:

[0561] The server generates characters of relevant historical figures and experts based on the received data. It uses an AI model based on pre-stored thinking patterns and speech records to create characters appropriate for the inquiry. This operation is achieved using deep learning libraries (e.g., TensorFlow and PyTorch). Specifically, it searches for relevant information from a database and provides input data to the model to carry out the generation process. The input for this step is data analyzed by the server itself, and the output is multiple generated AI characters.

[0562] Step 4:

[0563] The server initiates a dialogue between the generated AI characters, generates an initial proposal, and sends it to the device. Specifically, the server uses a natural language processing (NLP) algorithm to simulate a dialogue between the AI ​​characters. The generated dialogue content is sent to the device in JSON format and displayed on the device through a user interface. The input of this step is the generated AI character, and the output is the generated dialogue content.

[0564] Step 5:

[0565] The user observes the dialogue displayed on the terminal and inputs feedback or additional information as needed. Specific feedback can include adding a comment such as "I would like you to place more emphasis on environmental impact." This operation is performed using the terminal's input form and is again recorded as digital data. The input is the user's feedback, and the output is the feedback data recorded within the terminal.

[0566] Step 6:

[0567] The device sends the user's feedback to the server. This operation again uses an HTTP POST request, and the data sent is in JSON format. The server receives this data and parses it. The input is the feedback data, and the output is the feedback information parsed within the server.

[0568] Step 7:

[0569] The server adjusts the weights of the set evaluation indicators (KPIs) based on the received feedback. The server analyzes the feedback content and updates the parameters for generating dialogue based on the new importance. Specifically, it recalculates the weighting of the KPIs according to the feedback and reflects the feedback in the AI ​​model. The input of this step is the analyzed feedback information, and the output is the updated KPI parameters.

[0570] Step 8:

[0571] The server regenerates the dialogue content based on the adjusted KPI and sends it to the device. The server uses the deep learning model to generate a new dialogue and sends it in JSON format to the device. The device analyzes, displays, and provides it to the user. The input of this step is the updated KPI parameters, and the output is the regenerated dialogue content.

[0572] Step 9:

[0573] The user reviews the regenerated dialogue and provides additional feedback. The user can observe the new dialogue and enter further feedback as needed. This is done from the terminal and recorded as feedback data. The input to this step is the regenerated dialogue, and the output is the newly recorded feedback data.

[0574] Step 10:

[0575] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device. The server then compiles the final output of the AI ​​model and generates specific technical proposals and solutions. The generated solutions are sent to the device in JSON format and displayed on the user interface. The input of this step is the dialogue data and analysis results from multiple rounds, and the output is the final solution.

[0576] (Application example 1)

[0577] 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."

[0578] As modern security risks become more sophisticated and diverse, there is a need for effective solutions to the specific security issues faced by companies and individuals from multiple perspectives. However, it is difficult for users without specialized knowledge to understand complex security measures and select appropriate ones. Therefore, there is a need for a system that can make adjustable suggestions based on user feedback and derive optimal security measures.

[0579] 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.

[0580] In this invention, the server includes means for setting a problem, means for generating interactive AI characters that imitate historical figures or experts, means for the generated interactive AI characters to have a dialogue with each other and derive a solution, means for receiving feedback from a user and adjusting the content of the dialogue based on the feedback, means for generating a final solution and presenting it to the user, and means for evaluating security risks and proposing countermeasures, thereby enabling users to effectively obtain optimal solutions for specific security problems obtained from multiple perspectives.

[0581] Below are definitions of important terms included in the claims just rewritten.

[0582] "Means for setting the problem" refers to the interface and processing functions that allow users to input specific problems or goals they want to solve into the system.

[0583] An "interactive AI character" is an AI model that imitates historical figures and experts and makes suggestions for solving problems through dialogue.

[0584] "Means of generation" refers to the algorithms and processing functions for constructing an AI character from relevant characteristics, ways of thinking, and speech records based on the content of the task.

[0585] "Means for conducting dialogue and deriving solutions" refers to a processing function that allows multiple interactive AI characters to dialogue with each other and derive the optimal solution as a result.

[0586] "Means for receiving feedback and adjusting the content of the dialogue based on that feedback" refers to a processing function that allows the system to receive opinions and requests from users and dynamically change the direction and content of the dialogue based on those opinions and requests.

[0587] "Means for generating a final solution and presenting it to the user" refers to a processing function for generating an optimal solution based on the results of the adjusted dialogue and presenting it to the user in an easy-to-understand manner.

[0588] "Means for assessing security risks and proposing countermeasures" refers to a processing function for conducting risk assessments for specific security issues and proposing optimal countermeasures based on the results.

[0589] "Means for adjusting the weights of evaluation indicators" refers to a processing function for dynamically changing the importance and priority of evaluation indicators based on user feedback.

[0590] "Thinking methods and statements" refers to the thought processes and statements of historical figures and experts based on past data and records.

[0591] The system for realizing this invention is configured using the following hardware and software: a smartphone and server as hardware, and a smartphone app (iOS / Android), a server-side application, and a conversational AI system (based on GPT-4) as software.

[0592] System Overview

[0593] This system is designed as a conversational AI system that focuses specifically on solving security issues. When users ask for help identifying security risks and proposing appropriate countermeasures, it uses conversational AI characters that mimic historical figures and experts to provide an approach from multiple perspectives.

[0594] First-time setup

[0595] First, the user uses a smartphone app to input the specific security issues they want to solve, along with setting specific goals and evaluation indicators (KPIs), allowing the system to understand the details of the problem they are trying to solve.

[0596] example:

[0597] A user inputs the task of "preventing phishing attacks," sets the goal as "enable all employees to use email safely," and sets the KPIs as "prevention rate, cost, and feasibility."

[0598] Submitting a Theme

[0599] The terminal sends the input task, goal, and evaluation index to the server. This data serves as the basis for subsequent processing.

[0600] AI character generation

[0601] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[0602] example:

[0603] The server selects "security researchers," "engineers," "law enforcement officers," etc. from a past database and generates AI characters based on each person's way of thinking and recorded speech.

[0604] Starting a conversation

[0605] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues through the app and check each character's proposals.

[0606] example:

[0607] Security researcher AI: Regular employee training and simulations are effective in preventing phishing attacks

[0608] Engineer AI: "It is necessary to install and configure email filtering software."

[0609] Law enforcement AI: "Legal countermeasures for phishing attacks should also be considered"

[0610] User Feedback

[0611] The user observes the content of the dialogue and inputs feedback from the terminal as necessary, which allows the dialogue to be modified in the direction desired by the user.

[0612] example:

[0613] The user provides feedback saying, "I'd like you to narrow the proposals down to a more cost-focused approach."

[0614] Send Feedback

[0615] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0616] Realigning the dialogue

[0617] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[0618] Re-interaction generation

[0619] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[0620] Deriving the final solution

[0621] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain optimal security measures from multiple perspectives.

[0622] Example prompt sentence:

[0623] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[0624] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0625] Step 1:

[0626] Users use a smartphone app to input the security issues they want to solve, specific goals, and evaluation indicators (KPIs). This input includes issues such as "preventing phishing attacks," goals such as "enabling all employees to use email safely," and evaluation indicators such as "prevention rate, cost, and feasibility." The device collects this input data and sends it to the server.

[0627] Step 2:

[0628] The server analyzes the tasks, goals, and evaluation index data received from the device and generates a related interactive AI character. This generation uses the thinking patterns and speech records of historical figures and experts stored in a database. For example, characters such as "security researchers," "engineers," and "law enforcement officers" can be generated based on the thinking patterns and speech records.

[0629] Step 3:

[0630] The server initiates a dialogue between the generated conversational AI characters and derives an initial proposal. In this dialogue, each character expresses their opinion based on the set task, utilizing their respective expertise and experience. For example, the "security researcher AI" may suggest that "regular training and simulations for employees are effective in preventing phishing attacks." This initial proposal is then sent to the terminal.

[0631] Step 4:

[0632] The device receives the initial proposal from the server and displays it to the user. The user can observe the dialogue through the app and check each character's proposal. The user can provide feedback as needed based on the dialogue. For example, the user can input feedback such as, "I would like you to consider the proposal with more emphasis on cost."

[0633] Step 5:

[0634] The terminal sends the user's feedback to the server. The server receives and analyzes this feedback. Based on the analysis, the server prepares to adjust the content of the dialogue. Specifically, it adjusts the weights of each evaluation index based on the feedback and reflects it in the next dialogue.

[0635] Step 6:

[0636] The server regenerates dialogue between the interactive AI characters based on the adjusted evaluation index. The new dialogue content includes suggestions that reflect user feedback. For example, the characters may discuss the specific content of a training program that takes cost constraints into account and generate a new proposal. This regenerated dialogue is then sent to the terminal.

[0637] Step 7:

[0638] The terminal displays the regenerated dialogue content to the user and collects feedback again. The user can provide further feedback based on the new dialogue content. For example, the user can provide feedback such as, "I would like you to show me the prevention rate more specifically."

[0639] Step 8:

[0640] The server repeats the process of feedback and dialogue generation multiple times to finally derive the best solution. The final solution fully reflects the user's feedback and is most suitable for solving the problem. The server generates this final solution and sends it to the terminal.

[0641] Step 9:

[0642] The terminal displays the final solution from the server to the user, who can then implement specific security measures based on the final solution. For example, the terminal can initiate specific actions such as introducing a "sustainable and cost-effective training program."

[0643] Example prompt sentence:

[0644] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[0645] 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.

[0646] System Overview

[0647] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[0648] System Operation

[0649] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[0650] First-time setup

[0651] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0652] example:

[0653] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[0654] Submitting a Theme

[0655] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0656] AI character generation

[0657] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[0658] Utilizing the Emotion Engine

[0659] The server uses data acquired from the user's device to run an emotion engine and analyze the user's emotional state. The emotion engine identifies the user's current emotion through facial expression recognition and voice analysis.

[0660] example:

[0661] If the emotion engine recognizes that the user is feeling particularly anxious or suspicious as the conversation about energy resources progresses, that information is sent to the server.

[0662] Starting a conversation

[0663] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[0664] example:

[0665] Inventor AI: "We should explore new AC technologies for energy efficiency, but we should proceed cautiously and consider their environmental impact."

[0666] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[0667] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[0668] User Feedback

[0669] The user observes the content of the dialogue and inputs feedback from the terminal as needed, which is used to adjust the content of the dialogue.

[0670] example:

[0671] If a user provides feedback such as "more emphasis on environmental impact," the emotion recognized by the emotion engine is also reflected.

[0672] Send Feedback

[0673] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0674] Realigning the dialogue

[0675] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine. It changes the weights of the evaluation indicators (KPIs) and reflects their influence on the dialogue. Based on the information from the emotion engine, it makes suggestions that are appropriate for the user's emotional state.

[0676] example:

[0677] The server increases the importance of "environmental impact," which results in new dialogue content being generated.

[0678] Re-interaction generation

[0679] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[0680] Deriving the final solution

[0681] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[0682] example:

[0683] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[0684] Summary of embodiments

[0685] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on the user's feedback and emotional information, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[0686] The processing flow will be explained below.

[0687] Step 1:

[0688] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0689] Examples: "Developing new energy resources," "Discovering efficient and sustainable energy resources," "Cost reduction, energy efficiency, and environmental impact."

[0690] Step 2:

[0691] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0692] Step 3:

[0693] Based on the received data, the server starts the process of selecting the appropriate person or expert, extracting relevant historical data and speech records from the database.

[0694] Step 4:

[0695] The server generates interactive AI characters based on data on selected great people and experts, using a method to model each character's way of thinking and speech patterns.

[0696] Step 5:

[0697] The server runs an emotion engine using data acquired from the user's device to analyze the user's emotional state. The emotion engine identifies the user's emotions through facial expression recognition and voice analysis.

[0698] Step 6:

[0699] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[0700] Step 7:

[0701] The server transmits the generated dialogue content to the user's device, allowing the user to check the suggestions made by each character.

[0702] Step 8:

[0703] The user observes the content of the dialogue and inputs feedback as needed, including information about the direction of the dialogue and the evaluation indicators that are important.

[0704] Step 9:

[0705] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[0706] Step 10:

[0707] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine, changing the weights of the evaluation indicators (KPIs) and reflecting their influence on the dialogue.

[0708] Step 11:

[0709] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[0710] Step 12:

[0711] After repeating this process several times and making any necessary adjustments, the server generates the best solution and presents it to the user's device.

[0712] Specific examples

[0713] Example: If a user sets a task related to "developing new energy resources" and inputs "discovering efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts that take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback. Furthermore, using emotional information recognized by the emotion engine, proposals are made that take into account the user's psychological needs and doubts. Finally, a "prototype of a new energy system using sustainable alternating current" is presented as the solution.

[0714] Example 2

[0715] 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."

[0716] Conventional conversational AI systems make unilateral suggestions without considering the user's emotional state, making it difficult to provide flexible solutions that reflect the user's actual situation and feelings. It is also difficult to derive optimal solutions from multiple perspectives for complex problems. This has resulted in problems where users are unable to obtain satisfactory solutions, undermining the usefulness of the system.

[0717] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input a task, a goal, and an evaluation index using a terminal; means for transmitting the input information from the terminal to the server; means for extracting related history data from a database based on the received task and evaluation index and generating an interactive AI character; means for using an emotion engine to analyze the emotional state of the user; means for conducting a dialogue using the generated interactive AI character and adjusting the content of the proposal; means for receiving feedback from the user during the dialogue and readjusting the content of the dialogue based on the feedback; and means for generating a final solution based on the adjusted dialogue and presenting it to the user. This makes it possible to provide flexible and multifaceted solutions that take into account the user's emotional state and feedback.

[0718] A "terminal" is an electronic device through which a user inputs tasks, goals, and evaluation indicators through an interface and communicates with a server.

[0719] A "server" is a computing device that receives information sent by users, generates interactive AI characters, analyzes emotions, and readjusts the dialogue based on the feedback.

[0720] A "problem" is a specific problem or theme that a user wants to solve.

[0721] A "goal" is a specific objective or purpose that a user aims to achieve.

[0722] "Key performance indicators (KPIs)" are standards or indicators used to measure the degree of achievement of set tasks or goals.

[0723] An "interactive artificial intelligence character" is a virtual intelligent agent that imitates historical figures or experts and engages in dialogue with the user.

[0724] "Historical data" is basic information for generating AI characters, such as the thinking methods and statements of great people and experts from the past.

[0725] An "emotion engine" is a software engine that analyzes a user's facial expressions and voice data to identify their emotional state.

[0726] "Feedback" refers to opinions and requests that users input regarding the content of the dialogue.

[0727] "Dialogue readjustment" is the process of modifying the dialogue based on the user's feedback and emotional state to make more appropriate suggestions.

[0728] A "solution" is a final proposal or response to a set problem.

[0729] System Overview

[0730] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[0731] Hardware and Software

[0732] The system is implemented using the following hardware and software.

[0733] Terminal: The device through which users access the system and enter tasks and goals. Examples include PCs, tablets, and smartphones.

[0734] Server: A machine that processes data, generates AI characters, analyzes emotions, and presents solutions. Apache or Nginx is used as the web server.

[0735] Database: A database for storing historical data and user feedback information. Examples include MySQL and PostgreSQL.

[0736] Emotion engine: Software that analyzes the user's facial expressions and voice data to recognize emotions. For example, Python's OpenCV library and voice analysis library are used.

[0737] Generative AI model: An artificial intelligence model for generating character scripts. Examples include natural language generation models such as GPT-3.

[0738] Examples of specific examples and prompts

[0739] Specific examples

[0740] The specific method of using the system is shown below.

[0741] 1. A user accesses the system using a terminal and enters the task of "developing new energy resources."

[0742] 2. The user sets the goal as "discover efficient and sustainable energy resources" and inputs "cost reduction, energy efficiency, and environmental impact" as evaluation indicators (KPIs).

[0743] 3. The device sends this information to the server.

[0744] 4. Based on the received information, the server extracts relevant historical data from the database and uses the GPT-3 model to generate characters that imitate famous people and experts.

[0745] 5. The server initiates a dialogue with the generated character. The generated script includes suggestions such as, "We should consider new AC technology to pursue energy efficiency."

[0746] 6. The device captures the user's facial expressions and voice and sends the data to the server.

[0747] 7. The server uses an emotion engine to analyze the user's emotional state and adjust the dialogue content.

[0748] Prompt Sentence Examples

[0749] Below is an example of a prompt sentence that is input to the generative AI model.

[0750] "You are a physicist working on the development of new energy resources. Your goal is to discover efficient and sustainable energy resources. Your key performance indicators (KPIs) are cost reduction, energy efficiency, and environmental impact. Please use your expertise to make proposals to achieve this goal."

[0751] In this way, an interactive AI character is generated and makes flexible suggestions that take into account the user's emotional state. This allows the user to obtain new approaches to problem-solving from multiple perspectives. The above is an embodiment of the invention based on the scope of the claims.

[0752] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0753] Step 1:

[0754] The user uses a terminal to input the assignment, goals, and evaluation indicators.

[0755] Input: Problem (e.g., "Develop new energy resources"), Goal (e.g., "Discover efficient and sustainable energy resources"), Evaluation Metrics (e.g., "Cost reduction, energy efficiency, environmental impact").

[0756] What happens: A user opens a web browser and uses the system's interface to enter tasks into text boxes and select goals and metrics from drop-down menus.

[0757] Output: The data entered by the user is displayed in the input field on the terminal.

[0758] Step 2:

[0759] The terminal collects the input information and sends it to the server.

[0760] Inputs: User-entered tasks, goals, and KPIs.

[0761] Specific behavior: The device creates an HTTP request, packages the user-entered data as a JSON object, and sends a POST request to the server's API.

[0762] Output: User input data is sent to the server.

[0763] Step 3:

[0764] The server analyzes the received data and saves it as basic information for generating AI characters.

[0765] Input: User input data sent from the terminal in JSON format.

[0766] Specific operations: The server parses the data received by the parser and saves it to a database. The API is built using the Python Flask framework and the data is stored in a MySQL database.

[0767] Output: User's assignments, goals, and metrics are stored in a database.

[0768] Step 4:

[0769] The server extracts historical data for the appropriate great and specialist characters from the database.

[0770] Input: Saved user assignments, goals, and metrics.

[0771] What it does: The server uses SQL queries to retrieve historical data on past great people and experts from a database, then parses the information using the Natural Language Toolkit (NLTK).

[0772] Output: Historical data is extracted and used to generate an interactive AI character.

[0773] Step 5:

[0774] The server generates traits and scripts for each character based on the extracted data.

[0775] Input: History data, prompt statement.

[0776] Specific operation: The server inputs a prompt into a generative AI model (e.g., GPT-3) to generate a character script. The prompt includes a specific perspective on "developing new energy resources."

[0777] Output: A script is generated for each character.

[0778] Step 6:

[0779] The device captures the user's facial expressions and voice and sends the data to a server.

[0780] Input: User's facial expression data, voice data.

[0781] Specific operation: The device's camera and microphone are used to collect the user's facial expression and voice data, which are then sent to the server in real time.

[0782] Output: Facial expression data and voice data are sent to the server.

[0783] Step 7:

[0784] The server uses an emotion engine to analyze the user's emotional state.

[0785] Input: Facial expression data and voice data sent from the device.

[0786] How it works: The collected data is processed using Python's OpenCV library and voice analysis library. The emotion engine identifies emotions from the user's facial expressions and voice and saves the results.

[0787] Output: The user's emotional state is identified and stored in a database.

[0788] Step 8:

[0789] The server initiates a dialogue with the generated AI character and executes the dialogue script.

[0790] Input: User's task, goal, metrics, and emotional state.

[0791] What it does: The server executes the character script generated earlier and presents interactive suggestions to the user. This process happens in real time.

[0792] Output: The user is presented with a concrete suggestion.

[0793] Step 9:

[0794] The user observes the content of the dialogue and inputs feedback from the terminal.

[0795] Input: User feedback on the proposal.

[0796] Specific action: The user enters "I would like more emphasis on environmental impact" in the feedback box in the dialogue window and clicks the submit button.

[0797] Output: Feedback is sent to the terminal.

[0798] Step 10:

[0799] The terminal transmits the feedback data to the server.

[0800] Input: Feedback entered by the user.

[0801] What happens: The feedback is packaged in JSON format and sent back to the server's API.

[0802] Output: User feedback is sent to the server.

[0803] Step 11:

[0804] The server readjusts the dialogue based on feedback and emotion engine data.

[0805] Input: User feedback, emotional state data.

[0806] What it does: The server analyzes the feedback and emotional data, applies it to each character's script, and inputs new prompts into the generative AI model to regenerate the dialogue.

[0807] Output: A new dialogue is created.

[0808] Step 12:

[0809] The server creates a new dialogue and sends it to the terminal.

[0810] Input: Calibrated performance indicators (KPIs) and sentiment information.

[0811] What it does: The server runs the adjusted script, generates new dialogue, and sends it to the device, where the user can see the new suggestions.

[0812] Output: The new dialogue is displayed on the terminal.

[0813] Step 13:

[0814] After multiple rounds of interaction and feedback, the server generates the best solution and presents it to the user's device.

[0815] Input: History of each interaction and feedback, emotional state data.

[0816] Specific operation: The server comprehensively evaluates each dialogue and feedback and generates a final solution, which involves creating a specific technical proposal using the final prompt sentence of the generative AI model and sending it to the device.

[0817] Output: The best solution is displayed on the user's terminal.

[0818] (Application example 2)

[0819] 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."

[0820] In autonomous vehicles, there is a need to appropriately recognize passenger emotional states and adjust the in-vehicle environment and route accordingly to enhance passenger comfort and safety. However, conventional autonomous vehicle systems lack a means for recognizing passenger emotional states in real time and taking appropriate action based on the state. To solve this problem, it is necessary to accurately grasp passenger emotions and reflect that information in the autonomous driving system. The present invention aims to solve these problems and realize safer and more comfortable autonomous vehicle operation.

[0821] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0822] In this invention, the server includes means for setting a task, means for generating an interactive AI character that imitates a historical figure or an expert based on the set task, means for conducting a dialogue between the generated interactive AI characters and deriving a solution, means for receiving feedback from a user during the dialogue and adjusting the content of the dialogue based on the feedback, means for generating a final solution based on the adjusted dialogue and presenting it to the user, and means for using an emotion recognition engine that recognizes passenger emotions and adjusting the operation and in-vehicle environment of the autonomous vehicle based on the emotions, thereby making it possible to recognize passenger emotional states in real time and dynamically adjust the in-vehicle environment and operating route accordingly.

[0823] The "means for setting the problem" is a function that allows the user to input the problem they want to solve, specific goals, and evaluation indicators into the system, and then the system clearly sets the problem to be solved based on that.

[0824] "Conversational AI characters" refer to AI designed to mimic historical figures or experts and to engage in dialogue based on user input and tasks.

[0825] "Means for deriving solutions" is a system function in which interactive AI characters converse with each other and generate optimal solutions based on collected information and user feedback.

[0826] "Means for receiving feedback" refers to a function for collecting information and opinions provided by users during the dialogue process and reflecting them in adjusting the operation of the entire system and the content of the dialogue.

[0827] "Means for adjusting the content of the dialogue" refers to a system function for optimizing the statements and actions of the interactive AI character based on feedback from the user.

[0828] The "means for generating a final solution and presenting it to the user" is a system function for finally presenting to the user the solution derived through multiple rounds of dialogue and feedback.

[0829] An "emotion recognition engine" is a technology that uses devices such as cameras and microphones to analyze a user's facial expressions and voice and recognize their emotional state in real time.

[0830] "Means for adjusting the operation and in-vehicle environment of an autonomous vehicle based on emotions" refers to a system function that dynamically optimizes the route of an autonomous vehicle and the environment, such as lighting and sound, inside the vehicle, based on the user's emotional information obtained by the emotion recognition engine.

[0831] System Overview

[0832] The system of the present invention is an AI system for autonomous vehicles that combines a conversational AI character and an emotion recognition engine, allowing it to grasp the emotional state of passengers in real time and adjust the in-car environment and route accordingly.

[0833] Hardware and software used

[0834] Hardware:

[0835] camera

[0836] microphone

[0837] lighting adjustment device

[0838] Autonomous driving control systems (e.g., NVIDIA DRIVE)

[0839] Various sensors (temperature, humidity, lighting, etc.)

[0840] software:

[0841] Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[0842] Self-driving APIs (e.g., Waymo APIs)

[0843] Conversational AI systems (such as ChatGPT)

[0844] Emotion Recognition and Data Processing

[0845] The server uses a camera and microphone to capture the passenger's facial expressions and voice, and sends them to an emotion recognition engine, which analyzes this data to determine the passenger's emotional state, for example, whether they are stressed or relaxed.

[0846] Conversational AI character generation

[0847] Based on the passenger's input tasks and evaluation criteria, the server generates an interactive AI character that mimics historical figures and experts and interacts with them according to specific scenarios.

[0848] Dialogue coordination and autonomous driving

[0849] The server dynamically adjusts the autonomous vehicle's in-car environment based on emotion recognition. For example, if a passenger feels stressed, the server will automatically play relaxing music and adjust the lighting to softer levels. The vehicle's route will also be optimized based on the passenger's emotional state. This includes avoiding traffic jams and selecting scenic routes.

[0850] User feedback and review

[0851] The user can provide feedback about the conversation and the in-car environment through the terminal, and the server will receive this feedback and reflect it in the next conversation and in-car environment adjustments.

[0852] Examples of prompt statements

[0853] Below are some examples of prompts that can be passed to a conversational AI system using emotion recognition results:

[0854] text

[0855] The emotions of the passenger next to you are: {"happiness": 0.1, "stress": 0.8}. Suggest how to respond.

[0856] Specific examples

[0857] For example, if a passenger sets the task of "developing new energy resources," the goal of "discovering efficient and sustainable energy resources," and the KPIs of "cost reduction, energy efficiency, and environmental impact," the conversational AI character will begin a dialogue based on this. If the emotion recognition engine detects that the passenger is feeling stressed, the AI ​​character will make relaxing suggestions, and the autonomous driving system will select a scenic route.

[0858] In this way, the system of the present invention can dynamically optimize the operation and interior environment of an autonomous vehicle based on the emotional state of the passengers, providing a safer and more comfortable travel experience.

[0859] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0860] Step 1:

[0861] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs). This allows the system to define a specific problem. The input data includes the problem name, goal, and KPI. The input data is sent to the server in a data structure such as JSON format.

[0862] Step 2:

[0863] The device sends the inputted issues, goals, and evaluation indicators (KPIs) to the server. This data is sent reliably because it is the basic information for the system. The server analyzes the received data and generates a problem-solving scenario. This prepares the information necessary for the next processing step.

[0864] Step 3:

[0865] The server then begins the process of generating a conversational AI character based on the received assignment and evaluation indicators. This process includes extracting relevant historical data and speech records from the database. For example, it references the thinking methods and speech records of specific figures and experts and constructs the character's dialogue script based on that information.

[0866] Step 4:

[0867] The server runs an emotion recognition engine using data acquired from the user's device to analyze the user's emotional state. The emotion recognition engine identifies the user's current emotion through facial expression recognition and voice analysis. The input data is image and voice data, and the output is an evaluation of the user's emotional state. For example, information such as "high stress level" or "relaxed" can be obtained.

[0868] Step 5:

[0869] Based on the output from the emotion recognition engine, the server initiates a dialogue between the generated conversational AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The input data is the evaluation data of the emotional state and the dialogue script, and the output is the content of the dialogue between each character. Specific actions include suggestions made by the AI ​​characters.

[0870] Step 6:

[0871] During the dialogue, the user can input feedback from the terminal, which then sends the feedback to the server. The feedback is used to adjust the dialogue content. The input data is the user's feedback content, and the output data is a dialogue revision proposal based on that feedback.

[0872] Step 7:

[0873] The server readjusts the dialogue based on the received feedback and emotional information from the emotion recognition engine. It changes the weights of the evaluation indicators (KPIs) and reflects their impact on the dialogue. Specific operations include regenerating the dialogue content. The input data are the revised feedback and KPIs, and the output is a new dialogue scenario.

[0874] Step 8:

[0875] The server generates a new dialogue based on the adjusted evaluation indicators (KPIs) and emotional information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary. The input data is the new dialogue scenario, and the output is the proposal to the user.

[0876] Step 9:

[0877] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives. The input data is the accumulated dialogue history and feedback, and the output is the final solution.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] [Third embodiment]

[0882] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0883] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0884] 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).

[0885] 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.

[0886] 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.

[0887] 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).

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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."

[0894] System Overview

[0895] This system is an interactive AI system that runs on a server and allows users to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts to derive solutions to problems set by users through dialogue.

[0896] System Operation

[0897] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[0898] First-time setup

[0899] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs), which specify the problem they want the system to solve.

[0900] example:

[0901] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[0902] Submitting a Theme

[0903] The device sends the input tasks, goals, and KPIs to the server. This data becomes the basic information required for subsequent processing.

[0904] AI character generation

[0905] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[0906] example:

[0907] The server selects from a past database great people and experts related to "energy efficiency," such as "a certain inventor," "a certain physicist," or "a certain environmental activist," and generates AI characters based on their respective ways of thinking and recorded statements.

[0908] Starting a conversation

[0909] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues and confirm each character's proposal.

[0910] example:

[0911] Inventor AI: "We should consider new AC technologies for energy efficiency."

[0912] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[0913] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[0914] User Feedback

[0915] The user observes the content of the dialogue and inputs feedback or additional information as needed from the terminal, thereby correcting the dialogue in the direction desired by the user.

[0916] example:

[0917] A user provides feedback saying, "I would like more emphasis on environmental impact."

[0918] Send Feedback

[0919] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[0920] Realigning the dialogue

[0921] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[0922] example:

[0923] The server increases the importance of "environmental impact" and generates new dialogue content as a result.

[0924] Re-interaction generation

[0925] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[0926] Deriving the final solution

[0927] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution from multiple perspectives.

[0928] example:

[0929] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[0930] Summary of embodiments

[0931] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on user feedback, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[0932] The processing flow will be explained below.

[0933] Step 1:

[0934] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[0935] Step 2:

[0936] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[0937] Step 3:

[0938] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[0939] Step 4:

[0940] The server generates interactive AI characters based on data on selected famous people and experts, using a method to model each character's way of thinking and speech patterns.

[0941] Step 5:

[0942] The server initiates an initial dialogue between the generated AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective.

[0943] Step 6:

[0944] The server sends the generated initial dialogue to the user's device, allowing the user to check the suggestions made by each character.

[0945] Step 7:

[0946] The user observes the content of the conversation using the terminal and provides feedback as needed, including information on the direction of the conversation and the evaluation indicators that are important.

[0947] Step 8:

[0948] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[0949] Step 9:

[0950] The server initiates the process of readjusting the dialogue based on the received feedback, changing the weights of the key performance indicators (KPIs) and reflecting their impact on the dialogue.

[0951] Step 10:

[0952] The server generates a new dialogue based on the adjusted KPIs and sends it back to the user's device. The user can review the new proposal and provide further feedback if necessary.

[0953] Step 11:

[0954] This process is repeated several times, and necessary adjustments are made to gradually approach an optimal solution. The server combines the results of each iteration to produce the final solution.

[0955] Step 12:

[0956] The server then sends the final solution to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[0957] Examples:

[0958] When a user sets a challenge related to "developing new energy resources," inputs "discovery of efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts who take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback, ultimately presenting a "prototype of a new energy system that uses sustainable alternating current" as the solution.

[0959] Example 1

[0960] 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."

[0961] In modern problem-solving, it is important to gather opinions and ideas from multiple perspectives. However, conventional systems require users to manually search for information and find the optimal solution from many sources, a time-consuming and labor-intensive process. Furthermore, it is difficult to integrate knowledge from different fields of expertise, making it difficult to derive the optimal solution. To solve this problem, a system is needed that allows users to efficiently gather opinions from multiple perspectives and use them to solve problems.

[0962] 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.

[0963] In this invention, the server includes means for transmitting the task, goal, and evaluation index input by the user from the terminal to the server, means for generating interactive AI characters imitating historical figures or experts based on the set task, and means for the generated interactive AI characters to converse with each other to derive a solution, thereby enabling the user to efficiently collect opinions from multiple perspectives and derive an optimal solution.

[0964] A "problem" is a specific problem or goal that a user wants to solve.

[0965] A "goal" is a specific result or target that a user wants to achieve when solving a problem.

[0966] "Key Performance Indicators (KPIs)" are specific criteria for measuring how effectively activities toward resolving issues are progressing.

[0967] "Terminal" means an electronic device that allows a user to access the system, enter information, and view results.

[0968] A "server" is a central processing unit that receives input data from users and performs calculations or generation based on that data.

[0969] An "interactive AI character" is a virtual character designed to engage in dialogue based on information provided by the user and provide advice and suggestions for solving problems.

[0970] "Feedback" refers to opinions or additional information provided by a user in response to a suggestion or discussion made by an interactive AI character.

[0971] "Dialogue content adjustment" refers to the process of modifying the behavior and speech of an interactive AI character based on user feedback.

[0972] The "final solution" is the optimal solution to the problem, derived through dialogue between the interactive AI character and feedback from the user.

[0973] This system is an interactive AI system that runs on a server, and allows users to use their devices to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts, and derives solutions through dialogue for problems set by the user.

[0974] The implementation of this system uses the following hardware and software:

[0975] Hardware: The device used by the user (computer, smartphone, tablet, etc.)

[0976] Hardware: Server (with a powerful processor and sufficient memory)

[0977] Software: Web browser or mobile application (for the user interface)

[0978] Software: Server-side programs (Python, TensorFlow, PyTorch, NLP algorithms, etc.)

[0979] The specific operation procedure of this system is as follows.

[0980] Users input the problem they want to solve, specific goals, and evaluation indicators (KPIs) into their device. This input operation embodies the problem they want to solve within the system. The input data is sent from the device to the server, which then uses it to generate characters of relevant historical figures and experts. In this process, the server uses an AI model based on each character's thinking style and speech records that have been saved in advance.

[0981] The generated AI characters converse with each other and generate an initial solution proposal. This is sent to the device, where the user observes the dialogue and provides feedback as needed. The feedback is sent back to the server, which adjusts the dialogue based on this feedback. A new dialogue is generated based on the adjusted evaluation index and sent back to the device. This process is repeated multiple times until the best solution is derived, incorporating the user's feedback.

[0982] For example, if a user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and the KPIs as "cost reduction, energy efficiency, and environmental impact," the server selects historical figures and experts related to "energy efficiency" and generates AI characters for each. The user observes the initial dialogue proposal and provides feedback, such as "I would like more emphasis on environmental impact." Based on that feedback, the server adjusts the dialogue content and generates a new dialogue that places greater importance on "environmental impact." Finally, the server can present the user with a "prototype of a new energy system using sustainable alternating current" as a concrete technology proposal.

[0983] In this way, users can efficiently obtain new approaches to solving problems from multiple perspectives that transcend their fields of expertise.

[0984] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0985] Step 1:

[0986] Users input the problem they want to solve, their specific goal, and key performance indicators (KPIs) into a terminal. For example, a user might enter information such as "developing new energy resources" as the problem, "discovering efficient and sustainable energy resources" as the goal, and "cost reduction, energy efficiency, and environmental impact" as the KPIs into a web browser form. After inputting this information, it is recorded as digital data on the terminal.

[0987] Step 2:

[0988] The device sends the input tasks, goals, and KPIs to the server. This is done via an HTTP POST request, and the data sent is in JSON format. The server receives this request, parses the data, and converts it into a usable format. The data (tasks, goals, KPIs) are sent as input data, and the server parses this data to extract the information needed for the next step.

[0989] Step 3:

[0990] The server generates characters of relevant historical figures and experts based on the received data. It uses an AI model based on pre-stored thinking patterns and speech records to create characters appropriate for the inquiry. This operation is achieved using deep learning libraries (e.g., TensorFlow and PyTorch). Specifically, it searches for relevant information from a database and provides input data to the model to carry out the generation process. The input for this step is data analyzed by the server itself, and the output is multiple generated AI characters.

[0991] Step 4:

[0992] The server initiates a dialogue between the generated AI characters, generates an initial proposal, and sends it to the device. Specifically, the server uses a natural language processing (NLP) algorithm to simulate a dialogue between the AI ​​characters. The generated dialogue content is sent to the device in JSON format and displayed on the device through a user interface. The input of this step is the generated AI character, and the output is the generated dialogue content.

[0993] Step 5:

[0994] The user observes the dialogue displayed on the terminal and inputs feedback or additional information as needed. Specific feedback can include adding a comment such as "I would like you to place more emphasis on environmental impact." This operation is performed using the terminal's input form and is again recorded as digital data. The input is the user's feedback, and the output is the feedback data recorded within the terminal.

[0995] Step 6:

[0996] The device sends the user's feedback to the server. This operation again uses an HTTP POST request, and the data sent is in JSON format. The server receives this data and parses it. The input is the feedback data, and the output is the feedback information parsed within the server.

[0997] Step 7:

[0998] The server adjusts the weights of the set evaluation indicators (KPIs) based on the received feedback. The server analyzes the feedback content and updates the parameters for generating dialogue based on the new importance. Specifically, it recalculates the weighting of the KPIs according to the feedback and reflects the feedback in the AI ​​model. The input of this step is the analyzed feedback information, and the output is the updated KPI parameters.

[0999] Step 8:

[1000] The server regenerates the dialogue content based on the adjusted KPI and sends it to the device. The server uses the deep learning model to generate a new dialogue and sends it in JSON format to the device. The device analyzes, displays, and provides it to the user. The input of this step is the updated KPI parameters, and the output is the regenerated dialogue content.

[1001] Step 9:

[1002] The user reviews the regenerated dialogue and provides additional feedback. The user can observe the new dialogue and enter further feedback as needed. This is done from the terminal and recorded as feedback data. The input to this step is the regenerated dialogue, and the output is the newly recorded feedback data.

[1003] Step 10:

[1004] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device. The server then compiles the final output of the AI ​​model and generates specific technical proposals and solutions. The generated solutions are sent to the device in JSON format and displayed on the user interface. The input of this step is the dialogue data and analysis results from multiple rounds, and the output is the final solution.

[1005] (Application example 1)

[1006] 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."

[1007] As modern security risks become more sophisticated and diverse, there is a need for effective solutions to the specific security issues faced by companies and individuals from multiple perspectives. However, it is difficult for users without specialized knowledge to understand complex security measures and select appropriate ones. Therefore, there is a need for a system that can make adjustable suggestions based on user feedback and derive optimal security measures.

[1008] 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.

[1009] In this invention, the server includes means for setting a problem, means for generating interactive AI characters that imitate historical figures or experts, means for the generated interactive AI characters to have a dialogue with each other and derive a solution, means for receiving feedback from a user and adjusting the content of the dialogue based on the feedback, means for generating a final solution and presenting it to the user, and means for evaluating security risks and proposing countermeasures, thereby enabling users to effectively obtain optimal solutions for specific security problems obtained from multiple perspectives.

[1010] Below are definitions of important terms included in the claims just rewritten.

[1011] "Means for setting the problem" refers to the interface and processing functions that allow users to input specific problems or goals they want to solve into the system.

[1012] An "interactive AI character" is an AI model that imitates historical figures and experts and makes suggestions for solving problems through dialogue.

[1013] "Means of generation" refers to the algorithms and processing functions for constructing an AI character from relevant characteristics, ways of thinking, and speech records based on the content of the task.

[1014] "Means for conducting dialogue and deriving solutions" refers to a processing function that allows multiple interactive AI characters to dialogue with each other and derive the optimal solution as a result.

[1015] "Means for receiving feedback and adjusting the content of the dialogue based on that feedback" refers to a processing function that allows the system to receive opinions and requests from users and dynamically change the direction and content of the dialogue based on those opinions and requests.

[1016] "Means for generating a final solution and presenting it to the user" refers to a processing function for generating an optimal solution based on the results of the adjusted dialogue and presenting it to the user in an easy-to-understand manner.

[1017] "Means for assessing security risks and proposing countermeasures" refers to a processing function for conducting risk assessments for specific security issues and proposing optimal countermeasures based on the results.

[1018] "Means for adjusting the weights of evaluation indicators" refers to a processing function for dynamically changing the importance and priority of evaluation indicators based on user feedback.

[1019] "Thinking methods and statements" refers to the thought processes and statements of historical figures and experts based on past data and records.

[1020] The system for realizing this invention is configured using the following hardware and software: a smartphone and server as hardware, and a smartphone app (iOS / Android), a server-side application, and a conversational AI system (based on GPT-4) as software.

[1021] System Overview

[1022] This system is designed as a conversational AI system that focuses specifically on solving security issues. When users ask for help identifying security risks and proposing appropriate countermeasures, it uses conversational AI characters that mimic historical figures and experts to provide an approach from multiple perspectives.

[1023] First-time setup

[1024] First, the user uses a smartphone app to input the specific security issues they want to solve, along with setting specific goals and evaluation indicators (KPIs), allowing the system to understand the details of the problem they are trying to solve.

[1025] example:

[1026] A user inputs the task of "preventing phishing attacks," sets the goal as "enable all employees to use email safely," and sets the KPIs as "prevention rate, cost, and feasibility."

[1027] Submitting a Theme

[1028] The terminal sends the input task, goal, and evaluation index to the server. This data serves as the basis for subsequent processing.

[1029] AI character generation

[1030] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[1031] example:

[1032] The server selects "security researchers," "engineers," "law enforcement officers," etc. from a past database and generates AI characters based on each person's way of thinking and recorded speech.

[1033] Starting a conversation

[1034] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues through the app and check each character's proposals.

[1035] example:

[1036] Security researcher AI: Regular employee training and simulations are effective in preventing phishing attacks

[1037] Engineer AI: "It is necessary to install and configure email filtering software."

[1038] Law enforcement AI: "Legal countermeasures for phishing attacks should also be considered"

[1039] User Feedback

[1040] The user observes the content of the dialogue and inputs feedback from the terminal as necessary, which allows the dialogue to be modified in the direction desired by the user.

[1041] example:

[1042] The user provides feedback saying, "I'd like you to narrow the proposals down to a more cost-focused approach."

[1043] Send Feedback

[1044] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[1045] Realigning the dialogue

[1046] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[1047] Re-interaction generation

[1048] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[1049] Deriving the final solution

[1050] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain optimal security measures from multiple perspectives.

[1051] Example prompt sentence:

[1052] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[1053] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1054] Step 1:

[1055] Users use a smartphone app to input the security issues they want to solve, specific goals, and evaluation indicators (KPIs). This input includes issues such as "preventing phishing attacks," goals such as "enabling all employees to use email safely," and evaluation indicators such as "prevention rate, cost, and feasibility." The device collects this input data and sends it to the server.

[1056] Step 2:

[1057] The server analyzes the tasks, goals, and evaluation index data received from the device and generates a related interactive AI character. This generation uses the thinking patterns and speech records of historical figures and experts stored in a database. For example, characters such as "security researchers," "engineers," and "law enforcement officers" can be generated based on the thinking patterns and speech records.

[1058] Step 3:

[1059] The server initiates a dialogue between the generated conversational AI characters and derives an initial proposal. In this dialogue, each character expresses their opinion based on the set task, utilizing their respective expertise and experience. For example, the "security researcher AI" may suggest that "regular training and simulations for employees are effective in preventing phishing attacks." This initial proposal is then sent to the terminal.

[1060] Step 4:

[1061] The device receives the initial proposal from the server and displays it to the user. The user can observe the dialogue through the app and check each character's proposal. The user can provide feedback as needed based on the dialogue. For example, the user can input feedback such as, "I would like you to consider the proposal with more emphasis on cost."

[1062] Step 5:

[1063] The terminal sends the user's feedback to the server. The server receives and analyzes this feedback. Based on the analysis, the server prepares to adjust the content of the dialogue. Specifically, it adjusts the weights of each evaluation index based on the feedback and reflects it in the next dialogue.

[1064] Step 6:

[1065] The server regenerates dialogue between the interactive AI characters based on the adjusted evaluation index. The new dialogue content includes suggestions that reflect user feedback. For example, the characters may discuss the specific content of a training program that takes cost constraints into account and generate a new proposal. This regenerated dialogue is then sent to the terminal.

[1066] Step 7:

[1067] The terminal displays the regenerated dialogue content to the user and collects feedback again. The user can provide further feedback based on the new dialogue content. For example, the user can provide feedback such as, "I would like you to show me the prevention rate more specifically."

[1068] Step 8:

[1069] The server repeats the process of feedback and dialogue generation multiple times to finally derive the best solution. The final solution fully reflects the user's feedback and is most suitable for solving the problem. The server generates this final solution and sends it to the terminal.

[1070] Step 9:

[1071] The terminal displays the final solution from the server to the user, who can then implement specific security measures based on the final solution. For example, the terminal can initiate specific actions such as introducing a "sustainable and cost-effective training program."

[1072] Example prompt sentence:

[1073] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[1074] 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.

[1075] System Overview

[1076] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[1077] System Operation

[1078] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[1079] First-time setup

[1080] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[1081] example:

[1082] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[1083] Submitting a Theme

[1084] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[1085] AI character generation

[1086] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[1087] Utilizing the Emotion Engine

[1088] The server uses data acquired from the user's device to run an emotion engine and analyze the user's emotional state. The emotion engine identifies the user's current emotion through facial expression recognition and voice analysis.

[1089] example:

[1090] If the emotion engine recognizes that the user is feeling particularly anxious or suspicious as the conversation about energy resources progresses, that information is sent to the server.

[1091] Starting a conversation

[1092] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[1093] example:

[1094] Inventor AI: "We should explore new AC technologies for energy efficiency, but we should proceed cautiously and consider their environmental impact."

[1095] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[1096] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[1097] User Feedback

[1098] The user observes the content of the dialogue and inputs feedback from the terminal as needed, which is used to adjust the content of the dialogue.

[1099] example:

[1100] If a user provides feedback such as "more emphasis on environmental impact," the emotion recognized by the emotion engine is also reflected.

[1101] Send Feedback

[1102] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[1103] Realigning the dialogue

[1104] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine. It changes the weights of the evaluation indicators (KPIs) and reflects their influence on the dialogue. Based on the information from the emotion engine, it makes suggestions that are appropriate for the user's emotional state.

[1105] example:

[1106] The server increases the importance of "environmental impact," which results in new dialogue content being generated.

[1107] Re-interaction generation

[1108] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[1109] Deriving the final solution

[1110] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[1111] example:

[1112] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[1113] Summary of embodiments

[1114] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on the user's feedback and emotional information, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[1115] The processing flow will be explained below.

[1116] Step 1:

[1117] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[1118] Examples: "Developing new energy resources," "Discovering efficient and sustainable energy resources," "Cost reduction, energy efficiency, and environmental impact."

[1119] Step 2:

[1120] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[1121] Step 3:

[1122] Based on the received data, the server starts the process of selecting the appropriate person or expert, extracting relevant historical data and speech records from the database.

[1123] Step 4:

[1124] The server generates interactive AI characters based on data on selected great people and experts, using a method to model each character's way of thinking and speech patterns.

[1125] Step 5:

[1126] The server runs an emotion engine using data acquired from the user's device to analyze the user's emotional state. The emotion engine identifies the user's emotions through facial expression recognition and voice analysis.

[1127] Step 6:

[1128] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[1129] Step 7:

[1130] The server transmits the generated dialogue content to the user's device, allowing the user to check the suggestions made by each character.

[1131] Step 8:

[1132] The user observes the content of the dialogue and inputs feedback as needed, including information about the direction of the dialogue and the evaluation indicators that are important.

[1133] Step 9:

[1134] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[1135] Step 10:

[1136] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine, changing the weights of the evaluation indicators (KPIs) and reflecting their influence on the dialogue.

[1137] Step 11:

[1138] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[1139] Step 12:

[1140] After repeating this process several times and making any necessary adjustments, the server generates the best solution and presents it to the user's device.

[1141] Specific examples

[1142] Example: If a user sets a task related to "developing new energy resources" and inputs "discovering efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts that take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback. Furthermore, using emotional information recognized by the emotion engine, proposals are made that take into account the user's psychological needs and doubts. Finally, a "prototype of a new energy system using sustainable alternating current" is presented as the solution.

[1143] Example 2

[1144] 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."

[1145] Conventional conversational AI systems make unilateral suggestions without considering the user's emotional state, making it difficult to provide flexible solutions that reflect the user's actual situation and feelings. It is also difficult to derive optimal solutions from multiple perspectives for complex problems. This has resulted in problems where users are unable to obtain satisfactory solutions, undermining the usefulness of the system.

[1146] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input a task, a goal, and an evaluation index using a terminal; means for transmitting the input information from the terminal to the server; means for extracting related history data from a database based on the received task and evaluation index and generating an interactive AI character; means for using an emotion engine to analyze the emotional state of the user; means for conducting a dialogue using the generated interactive AI character and adjusting the content of the proposal; means for receiving feedback from the user during the dialogue and readjusting the content of the dialogue based on the feedback; and means for generating a final solution based on the adjusted dialogue and presenting it to the user. This makes it possible to provide flexible and multifaceted solutions that take into account the user's emotional state and feedback.

[1147] A "terminal" is an electronic device through which a user inputs tasks, goals, and evaluation indicators through an interface and communicates with a server.

[1148] A "server" is a computing device that receives information sent by users, generates interactive AI characters, analyzes emotions, and readjusts the dialogue based on the feedback.

[1149] A "problem" is a specific problem or theme that a user wants to solve.

[1150] A "goal" is a specific objective or purpose that a user aims to achieve.

[1151] "Key performance indicators (KPIs)" are standards or indicators used to measure the degree of achievement of set tasks or goals.

[1152] An "interactive artificial intelligence character" is a virtual intelligent agent that imitates historical figures or experts and engages in dialogue with the user.

[1153] "Historical data" is basic information for generating AI characters, such as the thinking methods and statements of great people and experts from the past.

[1154] An "emotion engine" is a software engine that analyzes a user's facial expressions and voice data to identify their emotional state.

[1155] "Feedback" refers to opinions and requests that users input regarding the content of the dialogue.

[1156] "Dialogue readjustment" is the process of modifying the dialogue based on the user's feedback and emotional state to make more appropriate suggestions.

[1157] A "solution" is a final proposal or response to a set problem.

[1158] System Overview

[1159] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[1160] Hardware and Software

[1161] The system is implemented using the following hardware and software.

[1162] Terminal: The device through which users access the system and enter tasks and goals. Examples include PCs, tablets, and smartphones.

[1163] Server: A machine that processes data, generates AI characters, analyzes emotions, and presents solutions. Apache or Nginx is used as the web server.

[1164] Database: A database for storing historical data and user feedback information. Examples include MySQL and PostgreSQL.

[1165] Emotion engine: Software that analyzes the user's facial expressions and voice data to recognize emotions. For example, Python's OpenCV library and voice analysis library are used.

[1166] Generative AI model: An artificial intelligence model for generating character scripts. Examples include natural language generation models such as GPT-3.

[1167] Examples of specific examples and prompts

[1168] Specific examples

[1169] The specific method of using the system is shown below.

[1170] 1. A user accesses the system using a terminal and enters the task of "developing new energy resources."

[1171] 2. The user sets the goal as "discover efficient and sustainable energy resources" and inputs "cost reduction, energy efficiency, and environmental impact" as evaluation indicators (KPIs).

[1172] 3. The device sends this information to the server.

[1173] 4. Based on the received information, the server extracts relevant historical data from the database and uses the GPT-3 model to generate characters that imitate famous people and experts.

[1174] 5. The server initiates a dialogue with the generated character. The generated script includes suggestions such as, "We should consider new AC technology to pursue energy efficiency."

[1175] 6. The device captures the user's facial expressions and voice and sends the data to the server.

[1176] 7. The server uses an emotion engine to analyze the user's emotional state and adjust the dialogue content.

[1177] Prompt Sentence Examples

[1178] Below is an example of a prompt sentence that is input to the generative AI model.

[1179] "You are a physicist working on the development of new energy resources. Your goal is to discover efficient and sustainable energy resources. Your key performance indicators (KPIs) are cost reduction, energy efficiency, and environmental impact. Please use your expertise to make proposals to achieve this goal."

[1180] In this way, an interactive AI character is generated and makes flexible suggestions that take into account the user's emotional state. This allows the user to obtain new approaches to problem-solving from multiple perspectives. The above is an embodiment of the invention based on the scope of the claims.

[1181] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1182] Step 1:

[1183] The user uses a terminal to input the assignment, goals, and evaluation indicators.

[1184] Input: Problem (e.g., "Develop new energy resources"), Goal (e.g., "Discover efficient and sustainable energy resources"), Evaluation Metrics (e.g., "Cost reduction, energy efficiency, environmental impact").

[1185] What happens: A user opens a web browser and uses the system's interface to enter tasks into text boxes and select goals and metrics from drop-down menus.

[1186] Output: The data entered by the user is displayed in the input field on the terminal.

[1187] Step 2:

[1188] The terminal collects the input information and sends it to the server.

[1189] Inputs: User-entered tasks, goals, and KPIs.

[1190] Specific behavior: The device creates an HTTP request, packages the user-entered data as a JSON object, and sends a POST request to the server's API.

[1191] Output: User input data is sent to the server.

[1192] Step 3:

[1193] The server analyzes the received data and saves it as basic information for generating AI characters.

[1194] Input: User input data sent from the terminal in JSON format.

[1195] Specific operations: The server parses the data received by the parser and saves it to a database. The API is built using the Python Flask framework and the data is stored in a MySQL database.

[1196] Output: User's assignments, goals, and metrics are stored in a database.

[1197] Step 4:

[1198] The server extracts historical data for the appropriate great and specialist characters from the database.

[1199] Input: Saved user assignments, goals, and metrics.

[1200] What it does: The server uses SQL queries to retrieve historical data on past great people and experts from a database, then parses the information using the Natural Language Toolkit (NLTK).

[1201] Output: Historical data is extracted and used to generate an interactive AI character.

[1202] Step 5:

[1203] The server generates traits and scripts for each character based on the extracted data.

[1204] Input: History data, prompt statement.

[1205] Specific operation: The server inputs a prompt into a generative AI model (e.g., GPT-3) to generate a character script. The prompt includes a specific perspective on "developing new energy resources."

[1206] Output: A script is generated for each character.

[1207] Step 6:

[1208] The device captures the user's facial expressions and voice and sends the data to a server.

[1209] Input: User's facial expression data, voice data.

[1210] Specific operation: The device's camera and microphone are used to collect the user's facial expression and voice data, which are then sent to the server in real time.

[1211] Output: Facial expression data and voice data are sent to the server.

[1212] Step 7:

[1213] The server uses an emotion engine to analyze the user's emotional state.

[1214] Input: Facial expression data and voice data sent from the device.

[1215] How it works: The collected data is processed using Python's OpenCV library and voice analysis library. The emotion engine identifies emotions from the user's facial expressions and voice and saves the results.

[1216] Output: The user's emotional state is identified and stored in a database.

[1217] Step 8:

[1218] The server initiates a dialogue with the generated AI character and executes the dialogue script.

[1219] Input: User's task, goal, metrics, and emotional state.

[1220] What it does: The server executes the character script generated earlier and presents interactive suggestions to the user. This process happens in real time.

[1221] Output: The user is presented with a concrete suggestion.

[1222] Step 9:

[1223] The user observes the content of the dialogue and inputs feedback from the terminal.

[1224] Input: User feedback on the proposal.

[1225] Specific action: The user enters "I would like more emphasis on environmental impact" in the feedback box in the dialogue window and clicks the submit button.

[1226] Output: Feedback is sent to the terminal.

[1227] Step 10:

[1228] The terminal transmits the feedback data to the server.

[1229] Input: Feedback entered by the user.

[1230] What happens: The feedback is packaged in JSON format and sent back to the server's API.

[1231] Output: User feedback is sent to the server.

[1232] Step 11:

[1233] The server readjusts the dialogue based on feedback and emotion engine data.

[1234] Input: User feedback, emotional state data.

[1235] What it does: The server analyzes the feedback and emotional data, applies it to each character's script, and inputs new prompts into the generative AI model to regenerate the dialogue.

[1236] Output: A new dialogue is created.

[1237] Step 12:

[1238] The server creates a new dialogue and sends it to the terminal.

[1239] Input: Calibrated performance indicators (KPIs) and sentiment information.

[1240] What it does: The server runs the adjusted script, generates new dialogue, and sends it to the device, where the user can see the new suggestions.

[1241] Output: The new dialogue is displayed on the terminal.

[1242] Step 13:

[1243] After multiple rounds of interaction and feedback, the server generates the best solution and presents it to the user's device.

[1244] Input: History of each interaction and feedback, emotional state data.

[1245] Specific operation: The server comprehensively evaluates each dialogue and feedback and generates a final solution, which involves creating a specific technical proposal using the final prompt sentence of the generative AI model and sending it to the device.

[1246] Output: The best solution is displayed on the user's terminal.

[1247] (Application example 2)

[1248] 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."

[1249] In autonomous vehicles, there is a need to appropriately recognize passenger emotional states and adjust the in-vehicle environment and route accordingly to enhance passenger comfort and safety. However, conventional autonomous vehicle systems lack a means for recognizing passenger emotional states in real time and taking appropriate action based on the state. To solve this problem, it is necessary to accurately grasp passenger emotions and reflect that information in the autonomous driving system. The present invention aims to solve these problems and realize safer and more comfortable autonomous vehicle operation.

[1250] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1251] In this invention, the server includes means for setting a task, means for generating an interactive AI character that imitates a historical figure or an expert based on the set task, means for conducting a dialogue between the generated interactive AI characters and deriving a solution, means for receiving feedback from a user during the dialogue and adjusting the content of the dialogue based on the feedback, means for generating a final solution based on the adjusted dialogue and presenting it to the user, and means for using an emotion recognition engine that recognizes passenger emotions and adjusting the operation and in-vehicle environment of the autonomous vehicle based on the emotions, thereby making it possible to recognize passenger emotional states in real time and dynamically adjust the in-vehicle environment and operating route accordingly.

[1252] The "means for setting the problem" is a function that allows the user to input the problem they want to solve, specific goals, and evaluation indicators into the system, and then the system clearly sets the problem to be solved based on that.

[1253] "Conversational AI characters" refer to AI designed to mimic historical figures or experts and to engage in dialogue based on user input and tasks.

[1254] "Means for deriving solutions" is a system function in which interactive AI characters converse with each other and generate optimal solutions based on collected information and user feedback.

[1255] "Means for receiving feedback" refers to a function for collecting information and opinions provided by users during the dialogue process and reflecting them in adjusting the operation of the entire system and the content of the dialogue.

[1256] "Means for adjusting the content of the dialogue" refers to a system function for optimizing the statements and actions of the interactive AI character based on feedback from the user.

[1257] The "means for generating a final solution and presenting it to the user" is a system function for finally presenting to the user the solution derived through multiple rounds of dialogue and feedback.

[1258] An "emotion recognition engine" is a technology that uses devices such as cameras and microphones to analyze a user's facial expressions and voice and recognize their emotional state in real time.

[1259] "Means for adjusting the operation and in-vehicle environment of an autonomous vehicle based on emotions" refers to a system function that dynamically optimizes the route of an autonomous vehicle and the environment, such as lighting and sound, inside the vehicle, based on the user's emotional information obtained by the emotion recognition engine.

[1260] System Overview

[1261] The system of the present invention is an AI system for autonomous vehicles that combines a conversational AI character and an emotion recognition engine, allowing it to grasp the emotional state of passengers in real time and adjust the in-car environment and route accordingly.

[1262] Hardware and software used

[1263] Hardware:

[1264] camera

[1265] microphone

[1266] lighting adjustment device

[1267] Autonomous driving control systems (e.g., NVIDIA DRIVE)

[1268] Various sensors (temperature, humidity, lighting, etc.)

[1269] software:

[1270] Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[1271] Self-driving APIs (e.g., Waymo APIs)

[1272] Conversational AI systems (such as ChatGPT)

[1273] Emotion Recognition and Data Processing

[1274] The server uses a camera and microphone to capture the passenger's facial expressions and voice, and sends them to an emotion recognition engine, which analyzes this data to determine the passenger's emotional state, for example, whether they are stressed or relaxed.

[1275] Conversational AI character generation

[1276] Based on the passenger's input tasks and evaluation criteria, the server generates an interactive AI character that mimics historical figures and experts and interacts with them according to specific scenarios.

[1277] Dialogue coordination and autonomous driving

[1278] The server dynamically adjusts the autonomous vehicle's in-car environment based on emotion recognition. For example, if a passenger feels stressed, the server will automatically play relaxing music and adjust the lighting to softer levels. The vehicle's route will also be optimized based on the passenger's emotional state. This includes avoiding traffic jams and selecting scenic routes.

[1279] User feedback and review

[1280] The user can provide feedback about the conversation and the in-car environment through the terminal, and the server will receive this feedback and reflect it in the next conversation and in-car environment adjustments.

[1281] Examples of prompt statements

[1282] Below are some examples of prompts that can be passed to a conversational AI system using emotion recognition results:

[1283] text

[1284] The emotions of the passenger next to you are: {"happiness": 0.1, "stress": 0.8}. Suggest how to respond.

[1285] Specific examples

[1286] For example, if a passenger sets the task of "developing new energy resources," the goal of "discovering efficient and sustainable energy resources," and the KPIs of "cost reduction, energy efficiency, and environmental impact," the conversational AI character will begin a dialogue based on this. If the emotion recognition engine detects that the passenger is feeling stressed, the AI ​​character will make relaxing suggestions, and the autonomous driving system will select a scenic route.

[1287] In this way, the system of the present invention can dynamically optimize the operation and interior environment of an autonomous vehicle based on the emotional state of the passengers, providing a safer and more comfortable travel experience.

[1288] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1289] Step 1:

[1290] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs). This allows the system to define a specific problem. The input data includes the problem name, goal, and KPI. The input data is sent to the server in a data structure such as JSON format.

[1291] Step 2:

[1292] The device sends the inputted issues, goals, and evaluation indicators (KPIs) to the server. This data is sent reliably because it is the basic information for the system. The server analyzes the received data and generates a problem-solving scenario. This prepares the information necessary for the next processing step.

[1293] Step 3:

[1294] The server then begins the process of generating a conversational AI character based on the received assignment and evaluation indicators. This process includes extracting relevant historical data and speech records from the database. For example, it references the thinking methods and speech records of specific figures and experts and constructs the character's dialogue script based on that information.

[1295] Step 4:

[1296] The server runs an emotion recognition engine using data acquired from the user's device to analyze the user's emotional state. The emotion recognition engine identifies the user's current emotion through facial expression recognition and voice analysis. The input data is image and voice data, and the output is an evaluation of the user's emotional state. For example, information such as "high stress level" or "relaxed" can be obtained.

[1297] Step 5:

[1298] Based on the output from the emotion recognition engine, the server initiates a dialogue between the generated conversational AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The input data is the evaluation data of the emotional state and the dialogue script, and the output is the content of the dialogue between each character. Specific actions include suggestions made by the AI ​​characters.

[1299] Step 6:

[1300] During the dialogue, the user can input feedback from the terminal, which then sends the feedback to the server. The feedback is used to adjust the dialogue content. The input data is the user's feedback content, and the output data is a dialogue revision proposal based on that feedback.

[1301] Step 7:

[1302] The server readjusts the dialogue based on the received feedback and emotional information from the emotion recognition engine. It changes the weights of the evaluation indicators (KPIs) and reflects their impact on the dialogue. Specific operations include regenerating the dialogue content. The input data are the revised feedback and KPIs, and the output is a new dialogue scenario.

[1303] Step 8:

[1304] The server generates a new dialogue based on the adjusted evaluation indicators (KPIs) and emotional information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary. The input data is the new dialogue scenario, and the output is the proposal to the user.

[1305] Step 9:

[1306] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives. The input data is the accumulated dialogue history and feedback, and the output is the final solution.

[1307] 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.

[1308] 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.

[1309] 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.

[1310] [Fourth embodiment]

[1311] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1312] 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.

[1313] 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).

[1314] 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.

[1315] 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.

[1316] 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).

[1317] 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.

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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."

[1324] System Overview

[1325] This system is an interactive AI system that runs on a server and allows users to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts to derive solutions to problems set by users through dialogue.

[1326] System Operation

[1327] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[1328] First-time setup

[1329] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs), which specify the problem they want the system to solve.

[1330] example:

[1331] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[1332] Submitting a Theme

[1333] The device sends the input tasks, goals, and KPIs to the server. This data becomes the basic information required for subsequent processing.

[1334] AI character generation

[1335] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[1336] example:

[1337] The server selects from a past database great people and experts related to "energy efficiency," such as "a certain inventor," "a certain physicist," or "a certain environmental activist," and generates AI characters based on their respective ways of thinking and recorded statements.

[1338] Starting a conversation

[1339] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues and confirm each character's proposal.

[1340] example:

[1341] Inventor AI: "We should consider new AC technologies for energy efficiency."

[1342] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[1343] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[1344] User Feedback

[1345] The user observes the content of the dialogue and inputs feedback or additional information as needed from the terminal, thereby correcting the dialogue in the direction desired by the user.

[1346] example:

[1347] A user provides feedback saying, "I would like more emphasis on environmental impact."

[1348] Send Feedback

[1349] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[1350] Realigning the dialogue

[1351] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[1352] example:

[1353] The server increases the importance of "environmental impact" and generates new dialogue content as a result.

[1354] Re-interaction generation

[1355] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[1356] Deriving the final solution

[1357] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution from multiple perspectives.

[1358] example:

[1359] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[1360] Summary of embodiments

[1361] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on user feedback, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[1362] The processing flow will be explained below.

[1363] Step 1:

[1364] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[1365] Step 2:

[1366] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[1367] Step 3:

[1368] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[1369] Step 4:

[1370] The server generates interactive AI characters based on data on selected famous people and experts, using a method to model each character's way of thinking and speech patterns.

[1371] Step 5:

[1372] The server initiates an initial dialogue between the generated AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective.

[1373] Step 6:

[1374] The server sends the generated initial dialogue to the user's device, allowing the user to check the suggestions made by each character.

[1375] Step 7:

[1376] The user observes the content of the conversation using the terminal and provides feedback as needed, including information on the direction of the conversation and the evaluation indicators that are important.

[1377] Step 8:

[1378] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[1379] Step 9:

[1380] The server initiates the process of readjusting the dialogue based on the received feedback, changing the weights of the key performance indicators (KPIs) and reflecting their impact on the dialogue.

[1381] Step 10:

[1382] The server generates a new dialogue based on the adjusted KPIs and sends it back to the user's device. The user can review the new proposal and provide further feedback if necessary.

[1383] Step 11:

[1384] This process is repeated several times, and necessary adjustments are made to gradually approach an optimal solution. The server combines the results of each iteration to produce the final solution.

[1385] Step 12:

[1386] The server then sends the final solution to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[1387] Examples:

[1388] When a user sets a challenge related to "developing new energy resources," inputs "discovery of efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts who take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback, ultimately presenting a "prototype of a new energy system that uses sustainable alternating current" as the solution.

[1389] Example 1

[1390] 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."

[1391] In modern problem-solving, it is important to gather opinions and ideas from multiple perspectives. However, conventional systems require users to manually search for information and find the optimal solution from many sources, a time-consuming and labor-intensive process. Furthermore, it is difficult to integrate knowledge from different fields of expertise, making it difficult to derive the optimal solution. To solve this problem, a system is needed that allows users to efficiently gather opinions from multiple perspectives and use them to solve problems.

[1392] 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.

[1393] In this invention, the server includes means for transmitting the task, goal, and evaluation index input by the user from the terminal to the server, means for generating interactive AI characters imitating historical figures or experts based on the set task, and means for the generated interactive AI characters to converse with each other to derive a solution, thereby enabling the user to efficiently collect opinions from multiple perspectives and derive an optimal solution.

[1394] A "problem" is a specific problem or goal that a user wants to solve.

[1395] A "goal" is a specific result or target that a user wants to achieve when solving a problem.

[1396] "Key Performance Indicators (KPIs)" are specific criteria for measuring how effectively activities toward resolving issues are progressing.

[1397] "Terminal" means an electronic device that allows a user to access the system, enter information, and view results.

[1398] A "server" is a central processing unit that receives input data from users and performs calculations or generation based on that data.

[1399] An "interactive AI character" is a virtual character designed to engage in dialogue based on information provided by the user and provide advice and suggestions for solving problems.

[1400] "Feedback" refers to opinions or additional information provided by a user in response to a suggestion or discussion made by an interactive AI character.

[1401] "Dialogue content adjustment" refers to the process of modifying the behavior and speech of an interactive AI character based on user feedback.

[1402] The "final solution" is the optimal solution to the problem, derived through dialogue between the interactive AI character and feedback from the user.

[1403] This system is an interactive AI system that runs on a server, and allows users to use their devices to gather opinions from different perspectives and solve problems. Specifically, it utilizes interactive AI characters that mimic historical figures and experts, and derives solutions through dialogue for problems set by the user.

[1404] The implementation of this system uses the following hardware and software:

[1405] Hardware: The device used by the user (computer, smartphone, tablet, etc.)

[1406] Hardware: Server (with a powerful processor and sufficient memory)

[1407] Software: Web browser or mobile application (for the user interface)

[1408] Software: Server-side programs (Python, TensorFlow, PyTorch, NLP algorithms, etc.)

[1409] The specific operation procedure of this system is as follows.

[1410] Users input the problem they want to solve, specific goals, and evaluation indicators (KPIs) into their device. This input operation embodies the problem they want to solve within the system. The input data is sent from the device to the server, which then uses it to generate characters of relevant historical figures and experts. In this process, the server uses an AI model based on each character's thinking style and speech records that have been saved in advance.

[1411] The generated AI characters converse with each other and generate an initial solution proposal. This is sent to the device, where the user observes the dialogue and provides feedback as needed. The feedback is sent back to the server, which adjusts the dialogue based on this feedback. A new dialogue is generated based on the adjusted evaluation index and sent back to the device. This process is repeated multiple times until the best solution is derived, incorporating the user's feedback.

[1412] For example, if a user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and the KPIs as "cost reduction, energy efficiency, and environmental impact," the server selects historical figures and experts related to "energy efficiency" and generates AI characters for each. The user observes the initial dialogue proposal and provides feedback, such as "I would like more emphasis on environmental impact." Based on that feedback, the server adjusts the dialogue content and generates a new dialogue that places greater importance on "environmental impact." Finally, the server can present the user with a "prototype of a new energy system using sustainable alternating current" as a concrete technology proposal.

[1413] In this way, users can efficiently obtain new approaches to solving problems from multiple perspectives that transcend their fields of expertise.

[1414] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1415] Step 1:

[1416] Users input the problem they want to solve, their specific goal, and key performance indicators (KPIs) into a terminal. For example, a user might enter information such as "developing new energy resources" as the problem, "discovering efficient and sustainable energy resources" as the goal, and "cost reduction, energy efficiency, and environmental impact" as the KPIs into a web browser form. After inputting this information, it is recorded as digital data on the terminal.

[1417] Step 2:

[1418] The device sends the input tasks, goals, and KPIs to the server. This is done via an HTTP POST request, and the data sent is in JSON format. The server receives this request, parses the data, and converts it into a usable format. The data (tasks, goals, KPIs) are sent as input data, and the server parses this data to extract the information needed for the next step.

[1419] Step 3:

[1420] The server generates characters of relevant historical figures and experts based on the received data. It uses an AI model based on pre-stored thinking patterns and speech records to create characters appropriate for the inquiry. This operation is achieved using deep learning libraries (e.g., TensorFlow and PyTorch). Specifically, it searches for relevant information from a database and provides input data to the model to carry out the generation process. The input for this step is data analyzed by the server itself, and the output is multiple generated AI characters.

[1421] Step 4:

[1422] The server initiates a dialogue between the generated AI characters, generates an initial proposal, and sends it to the device. Specifically, the server uses a natural language processing (NLP) algorithm to simulate a dialogue between the AI ​​characters. The generated dialogue content is sent to the device in JSON format and displayed on the device through a user interface. The input of this step is the generated AI character, and the output is the generated dialogue content.

[1423] Step 5:

[1424] The user observes the dialogue displayed on the terminal and inputs feedback or additional information as needed. Specific feedback can include adding a comment such as "I would like you to place more emphasis on environmental impact." This operation is performed using the terminal's input form and is again recorded as digital data. The input is the user's feedback, and the output is the feedback data recorded within the terminal.

[1425] Step 6:

[1426] The device sends the user's feedback to the server. This operation again uses an HTTP POST request, and the data sent is in JSON format. The server receives this data and parses it. The input is the feedback data, and the output is the feedback information parsed within the server.

[1427] Step 7:

[1428] The server adjusts the weights of the set evaluation indicators (KPIs) based on the received feedback. The server analyzes the feedback content and updates the parameters for generating dialogue based on the new importance. Specifically, it recalculates the weighting of the KPIs according to the feedback and reflects the feedback in the AI ​​model. The input of this step is the analyzed feedback information, and the output is the updated KPI parameters.

[1429] Step 8:

[1430] The server regenerates the dialogue content based on the adjusted KPI and sends it to the device. The server uses the deep learning model to generate a new dialogue and sends it in JSON format to the device. The device analyzes, displays, and provides it to the user. The input of this step is the updated KPI parameters, and the output is the regenerated dialogue content.

[1431] Step 9:

[1432] The user reviews the regenerated dialogue and provides additional feedback. The user can observe the new dialogue and enter further feedback as needed. This is done from the terminal and recorded as feedback data. The input to this step is the regenerated dialogue, and the output is the newly recorded feedback data.

[1433] Step 10:

[1434] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device. The server then compiles the final output of the AI ​​model and generates specific technical proposals and solutions. The generated solutions are sent to the device in JSON format and displayed on the user interface. The input of this step is the dialogue data and analysis results from multiple rounds, and the output is the final solution.

[1435] (Application example 1)

[1436] 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."

[1437] As modern security risks become more sophisticated and diverse, there is a need for effective solutions to the specific security issues faced by companies and individuals from multiple perspectives. However, it is difficult for users without specialized knowledge to understand complex security measures and select appropriate ones. Therefore, there is a need for a system that can make adjustable suggestions based on user feedback and derive optimal security measures.

[1438] 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.

[1439] In this invention, the server includes means for setting a problem, means for generating interactive AI characters that imitate historical figures or experts, means for the generated interactive AI characters to have a dialogue with each other and derive a solution, means for receiving feedback from a user and adjusting the content of the dialogue based on the feedback, means for generating a final solution and presenting it to the user, and means for evaluating security risks and proposing countermeasures, thereby enabling users to effectively obtain optimal solutions for specific security problems obtained from multiple perspectives.

[1440] Below are definitions of important terms included in the claims just rewritten.

[1441] "Means for setting the problem" refers to the interface and processing functions that allow users to input specific problems or goals they want to solve into the system.

[1442] An "interactive AI character" is an AI model that imitates historical figures and experts and makes suggestions for solving problems through dialogue.

[1443] "Means of generation" refers to the algorithms and processing functions for constructing an AI character from relevant characteristics, ways of thinking, and speech records based on the content of the task.

[1444] "Means for conducting dialogue and deriving solutions" refers to a processing function that allows multiple interactive AI characters to dialogue with each other and derive the optimal solution as a result.

[1445] "Means for receiving feedback and adjusting the content of the dialogue based on that feedback" refers to a processing function that allows the system to receive opinions and requests from users and dynamically change the direction and content of the dialogue based on those opinions and requests.

[1446] "Means for generating a final solution and presenting it to the user" refers to a processing function for generating an optimal solution based on the results of the adjusted dialogue and presenting it to the user in an easy-to-understand manner.

[1447] "Means for assessing security risks and proposing countermeasures" refers to a processing function for conducting risk assessments for specific security issues and proposing optimal countermeasures based on the results.

[1448] "Means for adjusting the weights of evaluation indicators" refers to a processing function for dynamically changing the importance and priority of evaluation indicators based on user feedback.

[1449] "Thinking methods and statements" refers to the thought processes and statements of historical figures and experts based on past data and records.

[1450] The system for realizing this invention is configured using the following hardware and software: a smartphone and server as hardware, and a smartphone app (iOS / Android), a server-side application, and a conversational AI system (based on GPT-4) as software.

[1451] System Overview

[1452] This system is designed as a conversational AI system that focuses specifically on solving security issues. When users ask for help identifying security risks and proposing appropriate countermeasures, it uses conversational AI characters that mimic historical figures and experts to provide an approach from multiple perspectives.

[1453] First-time setup

[1454] First, the user uses a smartphone app to input the specific security issues they want to solve, along with setting specific goals and evaluation indicators (KPIs), allowing the system to understand the details of the problem they are trying to solve.

[1455] example:

[1456] A user inputs the task of "preventing phishing attacks," sets the goal as "enable all employees to use email safely," and sets the KPIs as "prevention rate, cost, and feasibility."

[1457] Submitting a Theme

[1458] The terminal sends the input task, goal, and evaluation index to the server. This data serves as the basis for subsequent processing.

[1459] AI character generation

[1460] The server generates characters of relevant historical figures and experts based on the received data, using pre-stored models based on each character's way of thinking and speech records.

[1461] example:

[1462] The server selects "security researchers," "engineers," "law enforcement officers," etc. from a past database and generates AI characters based on each person's way of thinking and recorded speech.

[1463] Starting a conversation

[1464] The server initiates dialogue between the generated AI characters, generates initial proposals, and sends them to the device. The user can observe these dialogues through the app and check each character's proposals.

[1465] example:

[1466] Security researcher AI: Regular employee training and simulations are effective in preventing phishing attacks

[1467] Engineer AI: "It is necessary to install and configure email filtering software."

[1468] Law enforcement AI: "Legal countermeasures for phishing attacks should also be considered"

[1469] User Feedback

[1470] The user observes the content of the dialogue and inputs feedback from the terminal as necessary, which allows the dialogue to be modified in the direction desired by the user.

[1471] example:

[1472] The user provides feedback saying, "I'd like you to narrow the proposals down to a more cost-focused approach."

[1473] Send Feedback

[1474] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[1475] Realigning the dialogue

[1476] The server takes the received feedback into account and adjusts the weights of the set KPIs, which causes the AI ​​character to start making suggestions based on the new importance.

[1477] Re-interaction generation

[1478] The server generates a new dialogue based on the adjusted KPIs and sends it to the device, where the user can again observe the dialogue and provide feedback if necessary.

[1479] Deriving the final solution

[1480] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain optimal security measures from multiple perspectives.

[1481] Example prompt sentence:

[1482] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[1483] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1484] Step 1:

[1485] Users use a smartphone app to input the security issues they want to solve, specific goals, and evaluation indicators (KPIs). This input includes issues such as "preventing phishing attacks," goals such as "enabling all employees to use email safely," and evaluation indicators such as "prevention rate, cost, and feasibility." The device collects this input data and sends it to the server.

[1486] Step 2:

[1487] The server analyzes the tasks, goals, and evaluation index data received from the device and generates a related interactive AI character. This generation uses the thinking patterns and speech records of historical figures and experts stored in a database. For example, characters such as "security researchers," "engineers," and "law enforcement officers" can be generated based on the thinking patterns and speech records.

[1488] Step 3:

[1489] The server initiates a dialogue between the generated conversational AI characters and derives an initial proposal. In this dialogue, each character expresses their opinion based on the set task, utilizing their respective expertise and experience. For example, the "security researcher AI" may suggest that "regular training and simulations for employees are effective in preventing phishing attacks." This initial proposal is then sent to the terminal.

[1490] Step 4:

[1491] The device receives the initial proposal from the server and displays it to the user. The user can observe the dialogue through the app and check each character's proposal. The user can provide feedback as needed based on the dialogue. For example, the user can input feedback such as, "I would like you to consider the proposal with more emphasis on cost."

[1492] Step 5:

[1493] The terminal sends the user's feedback to the server. The server receives and analyzes this feedback. Based on the analysis, the server prepares to adjust the content of the dialogue. Specifically, it adjusts the weights of each evaluation index based on the feedback and reflects it in the next dialogue.

[1494] Step 6:

[1495] The server regenerates dialogue between the interactive AI characters based on the adjusted evaluation index. The new dialogue content includes suggestions that reflect user feedback. For example, the characters may discuss the specific content of a training program that takes cost constraints into account and generate a new proposal. This regenerated dialogue is then sent to the terminal.

[1496] Step 7:

[1497] The terminal displays the regenerated dialogue content to the user and collects feedback again. The user can provide further feedback based on the new dialogue content. For example, the user can provide feedback such as, "I would like you to show me the prevention rate more specifically."

[1498] Step 8:

[1499] The server repeats the process of feedback and dialogue generation multiple times to finally derive the best solution. The final solution fully reflects the user's feedback and is most suitable for solving the problem. The server generates this final solution and sends it to the terminal.

[1500] Step 9:

[1501] The terminal displays the final solution from the server to the user, who can then implement specific security measures based on the final solution. For example, the terminal can initiate specific actions such as introducing a "sustainable and cost-effective training program."

[1502] Example prompt sentence:

[1503] "Please suggest effective countermeasures against phishing attacks. Please provide specific steps with an emphasis on prevention rate, cost, and feasibility."

[1504] 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.

[1505] System Overview

[1506] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[1507] System Operation

[1508] Below, we will create a program for this system and explain each processing step in natural language, with specific examples included.

[1509] First-time setup

[1510] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[1511] example:

[1512] The user inputs the task of "developing new energy resources," sets the goal as "discovering efficient and sustainable energy resources," and sets the KPIs as "cost reduction, energy efficiency, and environmental impact."

[1513] Submitting a Theme

[1514] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[1515] AI character generation

[1516] The server starts the process of selecting appropriate individuals and experts based on the received assignments and evaluation criteria, and extracts relevant historical data and speech records from the database.

[1517] Utilizing the Emotion Engine

[1518] The server uses data acquired from the user's device to run an emotion engine and analyze the user's emotional state. The emotion engine identifies the user's current emotion through facial expression recognition and voice analysis.

[1519] example:

[1520] If the emotion engine recognizes that the user is feeling particularly anxious or suspicious as the conversation about energy resources progresses, that information is sent to the server.

[1521] Starting a conversation

[1522] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[1523] example:

[1524] Inventor AI: "We should explore new AC technologies for energy efficiency, but we should proceed cautiously and consider their environmental impact."

[1525] Physicist AI: "We need to find a new material that takes into account the mass-energy equivalence of energy."

[1526] Environmentalist AI: "Renewable energy sources should be prioritized to minimize environmental impact."

[1527] User Feedback

[1528] The user observes the content of the dialogue and inputs feedback from the terminal as needed, which is used to adjust the content of the dialogue.

[1529] example:

[1530] If a user provides feedback such as "more emphasis on environmental impact," the emotion recognized by the emotion engine is also reflected.

[1531] Send Feedback

[1532] The terminal sends the user's feedback to the server, which receives the feedback and prepares it for reflection in the next interaction.

[1533] Realigning the dialogue

[1534] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine. It changes the weights of the evaluation indicators (KPIs) and reflects their influence on the dialogue. Based on the information from the emotion engine, it makes suggestions that are appropriate for the user's emotional state.

[1535] example:

[1536] The server increases the importance of "environmental impact," which results in new dialogue content being generated.

[1537] Re-interaction generation

[1538] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[1539] Deriving the final solution

[1540] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives.

[1541] example:

[1542] The server will present users with a concrete technology proposal for a "prototype of a new energy system that uses sustainable alternating current," which aims to reduce costs, increase energy efficiency, and minimize environmental impact.

[1543] Summary of embodiments

[1544] This system allows users to gain new approaches to solving problems from a multifaceted perspective that transcends their field of expertise. The server generates an interactive AI character, adjusts the dialogue based on the user's feedback and emotional information, and derives optimal solutions, which it then presents to the user. This enables effective and efficient problem-solving.

[1545] The processing flow will be explained below.

[1546] Step 1:

[1547] The user inputs the problem they want to solve, specific goals, and evaluation indicators (KPIs) using a terminal, which then defines a specific problem for the system.

[1548] Examples: "Developing new energy resources," "Discovering efficient and sustainable energy resources," "Cost reduction, energy efficiency, and environmental impact."

[1549] Step 2:

[1550] The terminal sends the input tasks, goals, and evaluation indicators (KPIs) to the server. This data is the basic information for the system, so it is transmitted reliably.

[1551] Step 3:

[1552] Based on the received data, the server starts the process of selecting the appropriate person or expert, extracting relevant historical data and speech records from the database.

[1553] Step 4:

[1554] The server generates interactive AI characters based on data on selected great people and experts, using a method to model each character's way of thinking and speech patterns.

[1555] Step 5:

[1556] The server runs an emotion engine using data acquired from the user's device to analyze the user's emotional state. The emotion engine identifies the user's emotions through facial expression recognition and voice analysis.

[1557] Step 6:

[1558] The server initiates dialogue between the generated interactive AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The suggestion content is adjusted taking into account the user's emotional state.

[1559] Step 7:

[1560] The server transmits the generated dialogue content to the user's device, allowing the user to check the suggestions made by each character.

[1561] Step 8:

[1562] The user observes the content of the dialogue and inputs feedback as needed, including information about the direction of the dialogue and the evaluation indicators that are important.

[1563] Step 9:

[1564] The device sends the feedback entered by the user to the server, which allows the content of the interaction to be adjusted.

[1565] Step 10:

[1566] The server readjusts the dialogue based on the received feedback and the emotional information from the emotion engine, changing the weights of the evaluation indicators (KPIs) and reflecting their influence on the dialogue.

[1567] Step 11:

[1568] The server generates a new dialogue based on the adjusted KPIs and emotion information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary.

[1569] Step 12:

[1570] After repeating this process several times and making any necessary adjustments, the server generates the best solution and presents it to the user's device.

[1571] Specific examples

[1572] Example: If a user sets a task related to "developing new energy resources" and inputs "discovering efficient and sustainable energy resources" as the goal and "cost reduction, energy efficiency, and environmental impact" as the evaluation indicators (KPIs), the server generates AI characters of historical figures and experts that take "energy efficiency" into consideration. These characters make initial proposals from their own perspectives, and the dialogue is readjusted based on the user's feedback. Furthermore, using emotional information recognized by the emotion engine, proposals are made that take into account the user's psychological needs and doubts. Finally, a "prototype of a new energy system using sustainable alternating current" is presented as the solution.

[1573] Example 2

[1574] 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."

[1575] Conventional conversational AI systems make unilateral suggestions without considering the user's emotional state, making it difficult to provide flexible solutions that reflect the user's actual situation and feelings. It is also difficult to derive optimal solutions from multiple perspectives for complex problems. This has resulted in problems where users are unable to obtain satisfactory solutions, undermining the usefulness of the system.

[1576] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to input a task, a goal, and an evaluation index using a terminal; means for transmitting the input information from the terminal to the server; means for extracting related history data from a database based on the received task and evaluation index and generating an interactive AI character; means for using an emotion engine to analyze the emotional state of the user; means for conducting a dialogue using the generated interactive AI character and adjusting the content of the proposal; means for receiving feedback from the user during the dialogue and readjusting the content of the dialogue based on the feedback; and means for generating a final solution based on the adjusted dialogue and presenting it to the user. This makes it possible to provide flexible and multifaceted solutions that take into account the user's emotional state and feedback.

[1577] A "terminal" is an electronic device through which a user inputs tasks, goals, and evaluation indicators through an interface and communicates with a server.

[1578] A "server" is a computing device that receives information sent by users, generates interactive AI characters, analyzes emotions, and readjusts the dialogue based on the feedback.

[1579] A "problem" is a specific problem or theme that a user wants to solve.

[1580] A "goal" is a specific objective or purpose that a user aims to achieve.

[1581] "Key performance indicators (KPIs)" are standards or indicators used to measure the degree of achievement of set tasks or goals.

[1582] An "interactive artificial intelligence character" is a virtual intelligent agent that imitates historical figures or experts and engages in dialogue with the user.

[1583] "Historical data" is basic information for generating AI characters, such as the thinking methods and statements of great people and experts from the past.

[1584] An "emotion engine" is a software engine that analyzes a user's facial expressions and voice data to identify their emotional state.

[1585] "Feedback" refers to opinions and requests that users input regarding the content of the dialogue.

[1586] "Dialogue readjustment" is the process of modifying the dialogue based on the user's feedback and emotional state to make more appropriate suggestions.

[1587] A "solution" is a final proposal or response to a set problem.

[1588] System Overview

[1589] This system is an interactive AI system that runs on a server and aims to collect opinions from different perspectives from users using their devices and solve problems. Furthermore, it combines an emotion engine that recognizes the user's emotions and makes suggestions that take into account the user's emotional state.

[1590] Hardware and Software

[1591] The system is implemented using the following hardware and software.

[1592] Terminal: The device through which users access the system and enter tasks and goals. Examples include PCs, tablets, and smartphones.

[1593] Server: A machine that processes data, generates AI characters, analyzes emotions, and presents solutions. Apache or Nginx is used as the web server.

[1594] Database: A database for storing historical data and user feedback information. Examples include MySQL and PostgreSQL.

[1595] Emotion engine: Software that analyzes the user's facial expressions and voice data to recognize emotions. For example, Python's OpenCV library and voice analysis library are used.

[1596] Generative AI model: An artificial intelligence model for generating character scripts. Examples include natural language generation models such as GPT-3.

[1597] Examples of specific examples and prompts

[1598] Specific examples

[1599] The specific method of using the system is shown below.

[1600] 1. A user accesses the system using a terminal and enters the task of "developing new energy resources."

[1601] 2. The user sets the goal as "discover efficient and sustainable energy resources" and inputs "cost reduction, energy efficiency, and environmental impact" as evaluation indicators (KPIs).

[1602] 3. The device sends this information to the server.

[1603] 4. Based on the received information, the server extracts relevant historical data from the database and uses the GPT-3 model to generate characters that imitate famous people and experts.

[1604] 5. The server initiates a dialogue with the generated character. The generated script includes suggestions such as, "We should consider new AC technology to pursue energy efficiency."

[1605] 6. The device captures the user's facial expressions and voice and sends the data to the server.

[1606] 7. The server uses an emotion engine to analyze the user's emotional state and adjust the dialogue content.

[1607] Prompt Sentence Examples

[1608] Below is an example of a prompt sentence that is input to the generative AI model.

[1609] "You are a physicist working on the development of new energy resources. Your goal is to discover efficient and sustainable energy resources. Your key performance indicators (KPIs) are cost reduction, energy efficiency, and environmental impact. Please use your expertise to make proposals to achieve this goal."

[1610] In this way, an interactive AI character is generated and makes flexible suggestions that take into account the user's emotional state. This allows the user to obtain new approaches to problem-solving from multiple perspectives. The above is an embodiment of the invention based on the scope of the claims.

[1611] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1612] Step 1:

[1613] The user uses a terminal to input the assignment, goals, and evaluation indicators.

[1614] Input: Problem (e.g., "Develop new energy resources"), Goal (e.g., "Discover efficient and sustainable energy resources"), Evaluation Metrics (e.g., "Cost reduction, energy efficiency, environmental impact").

[1615] What happens: A user opens a web browser and uses the system's interface to enter tasks into text boxes and select goals and metrics from drop-down menus.

[1616] Output: The data entered by the user is displayed in the input field on the terminal.

[1617] Step 2:

[1618] The terminal collects the input information and sends it to the server.

[1619] Inputs: User-entered tasks, goals, and KPIs.

[1620] Specific behavior: The device creates an HTTP request, packages the user-entered data as a JSON object, and sends a POST request to the server's API.

[1621] Output: User input data is sent to the server.

[1622] Step 3:

[1623] The server analyzes the received data and saves it as basic information for generating AI characters.

[1624] Input: User input data sent from the terminal in JSON format.

[1625] Specific operations: The server parses the data received by the parser and saves it to a database. The API is built using the Python Flask framework and the data is stored in a MySQL database.

[1626] Output: User's assignments, goals, and metrics are stored in a database.

[1627] Step 4:

[1628] The server extracts historical data for the appropriate great and specialist characters from the database.

[1629] Input: Saved user assignments, goals, and metrics.

[1630] What it does: The server uses SQL queries to retrieve historical data on past great people and experts from a database, then parses the information using the Natural Language Toolkit (NLTK).

[1631] Output: Historical data is extracted and used to generate an interactive AI character.

[1632] Step 5:

[1633] The server generates traits and scripts for each character based on the extracted data.

[1634] Input: History data, prompt statement.

[1635] Specific operation: The server inputs a prompt into a generative AI model (e.g., GPT-3) to generate a character script. The prompt includes a specific perspective on "developing new energy resources."

[1636] Output: A script is generated for each character.

[1637] Step 6:

[1638] The device captures the user's facial expressions and voice and sends the data to a server.

[1639] Input: User's facial expression data, voice data.

[1640] Specific operation: The device's camera and microphone are used to collect the user's facial expression and voice data, which are then sent to the server in real time.

[1641] Output: Facial expression data and voice data are sent to the server.

[1642] Step 7:

[1643] The server uses an emotion engine to analyze the user's emotional state.

[1644] Input: Facial expression data and voice data sent from the device.

[1645] How it works: The collected data is processed using Python's OpenCV library and voice analysis library. The emotion engine identifies emotions from the user's facial expressions and voice and saves the results.

[1646] Output: The user's emotional state is identified and stored in a database.

[1647] Step 8:

[1648] The server initiates a dialogue with the generated AI character and executes the dialogue script.

[1649] Input: User's task, goal, metrics, and emotional state.

[1650] What it does: The server executes the character script generated earlier and presents interactive suggestions to the user. This process happens in real time.

[1651] Output: The user is presented with a concrete suggestion.

[1652] Step 9:

[1653] The user observes the content of the dialogue and inputs feedback from the terminal.

[1654] Input: User feedback on the proposal.

[1655] Specific action: The user enters "I would like more emphasis on environmental impact" in the feedback box in the dialogue window and clicks the submit button.

[1656] Output: Feedback is sent to the terminal.

[1657] Step 10:

[1658] The terminal transmits the feedback data to the server.

[1659] Input: Feedback entered by the user.

[1660] What happens: The feedback is packaged in JSON format and sent back to the server's API.

[1661] Output: User feedback is sent to the server.

[1662] Step 11:

[1663] The server readjusts the dialogue based on feedback and emotion engine data.

[1664] Input: User feedback, emotional state data.

[1665] What it does: The server analyzes the feedback and emotional data, applies it to each character's script, and inputs new prompts into the generative AI model to regenerate the dialogue.

[1666] Output: A new dialogue is created.

[1667] Step 12:

[1668] The server creates a new dialogue and sends it to the terminal.

[1669] Input: Calibrated performance indicators (KPIs) and sentiment information.

[1670] What it does: The server runs the adjusted script, generates new dialogue, and sends it to the device, where the user can see the new suggestions.

[1671] Output: The new dialogue is displayed on the terminal.

[1672] Step 13:

[1673] After multiple rounds of interaction and feedback, the server generates the best solution and presents it to the user's device.

[1674] Input: History of each interaction and feedback, emotional state data.

[1675] Specific operation: The server comprehensively evaluates each dialogue and feedback and generates a final solution, which involves creating a specific technical proposal using the final prompt sentence of the generative AI model and sending it to the device.

[1676] Output: The best solution is displayed on the user's terminal.

[1677] (Application example 2)

[1678] 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."

[1679] In autonomous vehicles, there is a need to appropriately recognize passenger emotional states and adjust the in-vehicle environment and route accordingly to enhance passenger comfort and safety. However, conventional autonomous vehicle systems lack a means for recognizing passenger emotional states in real time and taking appropriate action based on the state. To solve this problem, it is necessary to accurately grasp passenger emotions and reflect that information in the autonomous driving system. The present invention aims to solve these problems and realize safer and more comfortable autonomous vehicle operation.

[1680] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1681] In this invention, the server includes means for setting a task, means for generating an interactive AI character that imitates a historical figure or an expert based on the set task, means for conducting a dialogue between the generated interactive AI characters and deriving a solution, means for receiving feedback from a user during the dialogue and adjusting the content of the dialogue based on the feedback, means for generating a final solution based on the adjusted dialogue and presenting it to the user, and means for using an emotion recognition engine that recognizes passenger emotions and adjusting the operation and in-vehicle environment of the autonomous vehicle based on the emotions, thereby making it possible to recognize passenger emotional states in real time and dynamically adjust the in-vehicle environment and operating route accordingly.

[1682] The "means for setting the problem" is a function that allows the user to input the problem they want to solve, specific goals, and evaluation indicators into the system, and then the system clearly sets the problem to be solved based on that.

[1683] "Conversational AI characters" refer to AI designed to mimic historical figures or experts and to engage in dialogue based on user input and tasks.

[1684] "Means for deriving solutions" is a system function in which interactive AI characters converse with each other and generate optimal solutions based on collected information and user feedback.

[1685] "Means for receiving feedback" refers to a function for collecting information and opinions provided by users during the dialogue process and reflecting them in adjusting the operation of the entire system and the content of the dialogue.

[1686] "Means for adjusting the content of the dialogue" refers to a system function for optimizing the statements and actions of the interactive AI character based on feedback from the user.

[1687] The "means for generating a final solution and presenting it to the user" is a system function for finally presenting to the user the solution derived through multiple rounds of dialogue and feedback.

[1688] An "emotion recognition engine" is a technology that uses devices such as cameras and microphones to analyze a user's facial expressions and voice and recognize their emotional state in real time.

[1689] "Means for adjusting the operation and in-vehicle environment of an autonomous vehicle based on emotions" refers to a system function that dynamically optimizes the route of an autonomous vehicle and the environment, such as lighting and sound, inside the vehicle, based on the user's emotional information obtained by the emotion recognition engine.

[1690] System Overview

[1691] The system of the present invention is an AI system for autonomous vehicles that combines a conversational AI character and an emotion recognition engine, allowing it to grasp the emotional state of passengers in real time and adjust the in-car environment and route accordingly.

[1692] Hardware and software used

[1693] Hardware:

[1694] camera

[1695] microphone

[1696] lighting adjustment device

[1697] Autonomous driving control systems (e.g., NVIDIA DRIVE)

[1698] Various sensors (temperature, humidity, lighting, etc.)

[1699] software:

[1700] Emotion recognition engine (e.g., Microsoft Azure Emotion API)

[1701] Self-driving APIs (e.g., Waymo APIs)

[1702] Conversational AI systems (such as ChatGPT)

[1703] Emotion Recognition and Data Processing

[1704] The server uses a camera and microphone to capture the passenger's facial expressions and voice, and sends them to an emotion recognition engine, which analyzes this data to determine the passenger's emotional state, for example, whether they are stressed or relaxed.

[1705] Conversational AI character generation

[1706] Based on the passenger's input tasks and evaluation criteria, the server generates an interactive AI character that mimics historical figures and experts and interacts with them according to specific scenarios.

[1707] Dialogue coordination and autonomous driving

[1708] The server dynamically adjusts the autonomous vehicle's in-car environment based on emotion recognition. For example, if a passenger feels stressed, the server will automatically play relaxing music and adjust the lighting to softer levels. The vehicle's route will also be optimized based on the passenger's emotional state. This includes avoiding traffic jams and selecting scenic routes.

[1709] User feedback and review

[1710] The user can provide feedback about the conversation and the in-car environment through the terminal, and the server will receive this feedback and reflect it in the next conversation and in-car environment adjustments.

[1711] Examples of prompt statements

[1712] Below are some examples of prompts that can be passed to a conversational AI system using emotion recognition results:

[1713] text

[1714] The emotions of the passenger next to you are: {"happiness": 0.1, "stress": 0.8}. Suggest how to respond.

[1715] Specific examples

[1716] For example, if a passenger sets the task of "developing new energy resources," the goal of "discovering efficient and sustainable energy resources," and the KPIs of "cost reduction, energy efficiency, and environmental impact," the conversational AI character will begin a dialogue based on this. If the emotion recognition engine detects that the passenger is feeling stressed, the AI ​​character will make relaxing suggestions, and the autonomous driving system will select a scenic route.

[1717] In this way, the system of the present invention can dynamically optimize the operation and interior environment of an autonomous vehicle based on the emotional state of the passengers, providing a safer and more comfortable travel experience.

[1718] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1719] Step 1:

[1720] The user uses a terminal to input the problem they want to solve, specific goals, and evaluation indicators (KPIs). This allows the system to define a specific problem. The input data includes the problem name, goal, and KPI. The input data is sent to the server in a data structure such as JSON format.

[1721] Step 2:

[1722] The device sends the inputted issues, goals, and evaluation indicators (KPIs) to the server. This data is sent reliably because it is the basic information for the system. The server analyzes the received data and generates a problem-solving scenario. This prepares the information necessary for the next processing step.

[1723] Step 3:

[1724] The server then begins the process of generating a conversational AI character based on the received assignment and evaluation indicators. This process includes extracting relevant historical data and speech records from the database. For example, it references the thinking methods and speech records of specific figures and experts and constructs the character's dialogue script based on that information.

[1725] Step 4:

[1726] The server runs an emotion recognition engine using data acquired from the user's device to analyze the user's emotional state. The emotion recognition engine identifies the user's current emotion through facial expression recognition and voice analysis. The input data is image and voice data, and the output is an evaluation of the user's emotional state. For example, information such as "high stress level" or "relaxed" can be obtained.

[1727] Step 5:

[1728] Based on the output from the emotion recognition engine, the server initiates a dialogue between the generated conversational AI characters and analyzes the results. Each character executes a script that makes suggestions from its own perspective. The input data is the evaluation data of the emotional state and the dialogue script, and the output is the content of the dialogue between each character. Specific actions include suggestions made by the AI ​​characters.

[1729] Step 6:

[1730] During the dialogue, the user can input feedback from the terminal, which then sends the feedback to the server. The feedback is used to adjust the dialogue content. The input data is the user's feedback content, and the output data is a dialogue revision proposal based on that feedback.

[1731] Step 7:

[1732] The server readjusts the dialogue based on the received feedback and emotional information from the emotion recognition engine. It changes the weights of the evaluation indicators (KPIs) and reflects their impact on the dialogue. Specific operations include regenerating the dialogue content. The input data are the revised feedback and KPIs, and the output is a new dialogue scenario.

[1733] Step 8:

[1734] The server generates a new dialogue based on the adjusted evaluation indicators (KPIs) and emotional information and sends it to the device. The user confirms the new proposal and provides further feedback if necessary. The input data is the new dialogue scenario, and the output is the proposal to the user.

[1735] Step 9:

[1736] After multiple rounds of dialogue and feedback, the server generates the best solution and presents it to the user's device, allowing the user to obtain the optimal solution to the problem from multiple perspectives. The input data is the accumulated dialogue history and feedback, and the output is the final solution.

[1737] 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.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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).

[1744] 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.

[1745] 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."

[1746] 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.

[1747] 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).

[1748] 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.

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] The following is further disclosed regarding the above embodiment.

[1759] (Claim 1)

[1760] A means of setting the task;

[1761] A means for generating an interactive AI character that imitates a historical figure or an expert based on the set task;

[1762] A means for deriving a solution by having the generated interactive AI characters interact with each other;

[1763] means for receiving feedback from the user during the interaction and adjusting the content of the interaction based on the feedback;

[1764] means for generating and presenting a final solution to a user based on the coordinated dialogue;

[1765] A system including:

[1766] (Claim 2)

[1767] 2. The system according to claim 1, further comprising means for adjusting weights of the set evaluation indexes based on feedback from the user.

[1768] (Claim 3)

[1769] The system according to claim 1, further comprising means for generating an interactive AI character that imitates the historical figure or expert based on pre-stored thinking patterns and speech records.

[1770] "Example 1"

[1771] (Claim 1)

[1772] A means of setting the task;

[1773] A means for generating an interactive AI character that imitates a historical figure or an expert based on the set task;

[1774] A means for deriving a solution by having the generated interactive AI characters interact with each other;

[1775] means for receiving feedback from the user during the interaction and adjusting the content of the interaction based on the feedback;

[1776] means for generating and presenting a final solution to a user based on the coordinated dialogue;

[1777] A means for transmitting the assignment, goal, and evaluation index input by the user from the terminal to the server;

[1778] means for generating a re-interaction based on the adjusted evaluation index and transmitting the re-interaction to the terminal;

[1779] A means for observing the generated dialogue and providing feedback again; and

[1780] A system including:

[1781] (Claim 2)

[1782] 2. The system according to claim 1, further comprising means for adjusting weights of the set evaluation indexes based on feedback from the user.

[1783] (Claim 3)

[1784] The system according to claim 1, further comprising means for generating an interactive AI character that imitates the historical figure or expert based on pre-stored thinking patterns and speech records.

[1785] "Application Example 1"

[1786] (Claim 1)

[1787] A means of setting the task;

[1788] A means for generating an interactive AI character that imitates a historical figure or an expert based on the set task;

[1789] A means for deriving a solution by having the generated interactive AI characters interact with each other;

[1790] means for receiving feedback from the user during the course of the dialogue and adjusting the content of the dialogue based on the feedback;

[1791] means for generating and presenting a final solution to a user based on the coordinated dialogue;

[1792] A means of assessing security risks and proposing countermeasures;

[1793] A system including:

[1794] (Claim 2)

[1795] 2. The system according to claim 1, further comprising means for adjusting weights of the set evaluation indexes based on feedback from the users.

[1796] (Claim 3)

[1797] The system according to claim 1, further comprising means for generating an interactive AI character that imitates the historical figure or expert based on pre-stored thinking patterns and speech records.

[1798] "Example 2: Combining Emotion Engines"

[1799] (Claim 1)

[1800] a means for a user to input tasks, goals, and evaluation indicators using a terminal;

[1801] means for transmitting the input information from the terminal to a server;

[1802] A means for extracting relevant historical data from the database and generating an interactive artificial intelligence character based on the received assignment and evaluation indicators;

[1803] means for using an emotion engine to analyze the emotional state of the user;

[1804] a means for carrying out a dialogue using the generated interactive AI character and adjusting the content of the proposal;

[1805] means for receiving feedback from the user during the interaction and readjusting the content of the interaction based on the feedback;

[1806] means for generating and presenting a final solution to a user based on the coordinated dialogue;

[1807] A system including:

[1808] (Claim 2)

[1809] 10. The system of claim 1, further comprising means for adjusting in real time the suggestions of the generated interactive AI character based on the emotional state of the user.

[1810] (Claim 3)

[1811] 2. The system according to claim 1, further comprising means for generating an interactive AI character based on the pre-stored history data and speech records.

[1812] "Application example 2 when combining emotion engines"

[1813] (Claim 1)

[1814] A means of setting the task;

[1815] A means for generating an interactive AI character that imitates a historical figure or an expert based on the set task;

[1816] A means for deriving a solution by having the generated interactive AI characters interact with each other;

[1817] means for receiving feedback from the user during the interaction and adjusting the content of the interaction based on the feedback;

[1818] means for generating and presenting a final solution to a user based on the coordinated dialogue;

[1819] a means for using an emotion recognition engine to recognize passenger emotions and adjust the operation and in-vehicle environment of the autonomous vehicle based on the emotions;

[1820] A system including:

[1821] (Claim 2)

[1822] 2. The system according to claim 1, further comprising means for adjusting weights of the set evaluation indexes based on feedback from the user.

[1823] (Claim 3)

[1824] The system according to claim 1, further comprising means for generating an interactive AI character that imitates the historical figure or expert based on pre-stored thinking patterns and speech records. [Explanation of symbols]

[1825] 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 of setting the task; A means for generating an interactive AI character that imitates a historical figure or an expert based on the set task; A means for deriving a solution by having the generated interactive AI characters interact with each other; means for receiving feedback from the user during the interaction and adjusting the content of the interaction based on the feedback; means for generating and presenting a final solution to a user based on the coordinated dialogue; A system including:

2. The system according to claim 1 , further comprising means for adjusting weights of the set evaluation indexes based on feedback from the user.

3. 2. The system according to claim 1, further comprising means for generating an interactive AI character that imitates the historical figure or expert based on pre-stored thinking patterns and speech records.

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