System

Abstract

To provide a system for performing a more detailed and highly reliable personality test in order to more accurately identify appropriate personnel required by a company.SOLUTION: The specific processing section 290 of the data processing apparatus 12 in the system includes means for generating an initial question using generative artificial intelligence, receiving an answer from the user via the terminal, analyzing the answer of the user using a natural language processing technique, and estimating the personality traits of the user based on the analysis result, means for generating a next question based on the estimated personality traits, and means for generating a personality diagnosis result of the user based on the repeatedly generated questions and the answer of the user.SELECTED DRAWING: Figure 2

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] Currently, personality tests used in employment exams are often based on standard multiple-choice questions, making it difficult to fully grasp the examinee's deep psychology or essential personality traits. As a result, there is often a gap between the talent companies are looking for and the talent they actually hire. To eliminate this gap and more accurately identify the right talent companies are looking for, a more detailed and reliable personality testing system is needed. [Means for solving the problem]

[0005] The present invention provides a system that uses generative artificial intelligence to analyze a user's personality traits in detail through natural dialogue with the user, thereby improving the reliability of conventional personality tests. This system includes means for generating initial questions using generative artificial intelligence, means for receiving answers from the user via a terminal, means for analyzing the user's answers using natural language processing technology, means for inferring the user's personality traits based on the analysis results, means for generating subsequent questions based on the inferred personality traits, and finally means for generating a personality diagnosis result for the user. This reduces the gap between the actual talent and the talent that companies are looking for.

[0006] "Generative AI" refers to artificial intelligence technology that has the ability to generate new text from given questions and answers.

[0007] "Terminal" refers to a device through which a user provides input and interacts with a system. Examples include a personal computer or smartphone.

[0008] "User" refers to the individual who takes the personality test of this system.

[0009] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and generate human language.

[0010] "Analysis" refers to the process of breaking down received information and understanding and evaluating its content.

[0011] "Personality traits" refer to characteristics related to the personality and behavioral patterns of individual users.

[0012] "Generate" refers to the process of creating new questions, answers, etc.

[0013] A "question" refers to a query made to a user to elicit information.

[0014] "Diagnosis result" refers to the conclusion of information analyzed based on collected data.

[0015] "Report" refers to a written or digital report summarizing diagnostic results and providing them to the user.

[0016] "API" refers to an interface that allows different software programs to communicate with each other and provide each other with functionality. [Brief explanation of the drawings]

[0017] [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

[0018] 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.

[0019] First, the terms used in the following description will be explained.

[0020] 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).

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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."

[0038] This invention relates to a highly accurate personality testing system using generative artificial intelligence. This system analyzes the user's personality traits in detail through natural dialogue with the user, improving the reliability of personality tests in employment examinations.

[0039] System Overview

[0040] The system mainly consists of the following elements:

[0041] 1. Generative AI module: Questions are generated using generative AI such as ChatGPT.

[0042] 2. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[0043] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0044] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0045] 5. Database: Stores user response data and personality trait information.

[0046] System Operation

[0047] The processing of the system will be explained in natural language below.

[0048] 1. Initial Setup

[0049] The server reads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[0050] 2. Start the personality test

[0051] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0052] 3. First question generation

[0053] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[0054] 4. Question and answer exchanges

[0055] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[0056] 5. Analysis of responses

[0057] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This makes it possible to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[0058] 6. Next Question Generation

[0059] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[0060] 7. Iteration

[0061] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[0062] 8. Generating personality test results

[0063] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0064] Specific examples

[0065] For example, if a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[0066] The above is an embodiment of the present invention, which goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring of personnel.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[0070] Step 2:

[0071] A user accesses the system's entry point using a terminal and enters necessary information such as name, age, desired occupation, etc. into the registration form. The terminal then sends this registration information to the server.

[0072] Step 3:

[0073] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[0074] Step 4:

[0075] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[0076] Step 5:

[0077] The server analyzes the user's responses using natural language processing technology, including sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results.

[0078] Step 6:

[0079] The server then calls the ChatGPT API again to generate the next question based on the estimated personality traits, and sends the generated next question to the device, which then displays it to the user.

[0080] Step 7:

[0081] The user answers new questions using the device. The device sends the answers to the server. Steps 5 and 6 are repeated to accumulate the necessary personality trait data.

[0082] Step 8:

[0083] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[0084] Step 9:

[0085] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[0086] This is the specific processing flow of the personality testing system that utilizes generative AI. This process enables detailed and accurate analysis of the user's personality traits and supports appropriate hiring decisions.

[0087] Example 1

[0088] 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."

[0089] Conventional personality tests rely on limited question and answer patterns, making it difficult to grasp the user's personality traits in detail. Furthermore, the exchange of questions and answers is mechanical, lacking naturalness and reliability. As a result, accuracy is insufficient for selecting the right candidates, especially in job interviews.

[0090] 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.

[0091] In this invention, the server includes means for generating initial questions using generative artificial intelligence, means for receiving registration information and answers from the user via the terminal, and means for analyzing the user's answers using natural language processing technology. This allows for detailed analysis of the user's personality traits while maintaining the naturalness of the dialogue, enabling highly accurate personality diagnosis.

[0092] "Generative AI" is an AI system that uses natural language processing technology to generate text and dialogue.

[0093] A "terminal" is a device that allows a user to access a system and input or receive information. Examples of such devices include personal computers, smartphones, and tablets.

[0094] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language. This technology includes sentiment analysis, intent estimation, and keyword extraction.

[0095] "Analysis results" are data and information obtained from users' responses using natural language processing technology.

[0096] "Personality traits" is a concept that refers to specific characteristics and tendencies regarding a user's behavior, reactions, and emotions.

[0097] A "dialogue scenario" is a set of questions and answers designed to achieve a specific purpose.

[0098] A "database" is a system for storing, managing, and retrieving data in an organized and efficient manner.

[0099] The "personality assessment results" are reports that detail the user's personality traits, generated based on the user's answers and the analysis results.

[0100] This invention relates to a highly accurate personality testing system using generative artificial intelligence. The system aims to improve the reliability of personality tests in employment examinations by analyzing the user's personality traits in detail through natural dialogue with the user.

[0101] System Configuration

[0102] The system of the present invention includes the following elements:

[0103] 1. Generative AI module: This is a module that generates questions using a generative AI model, for example, using ChatGPT's API.

[0104] 2. Device: The interface through which the user provides input (e.g., computer, smartphone, tablet).

[0105] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0106] 4. Natural Language Processing (NLP) module: This is a technology for analyzing user responses. Specifically, it performs sentiment analysis, intent estimation, keyword extraction, etc.

[0107] 5. Database: Stores user response data and personality trait information.

[0108] System Operation

[0109] Initial Setup

[0110] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[0111] Starting the personality test

[0112] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0113] First question generation

[0114] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question, such as, "What is the most stressful situation for you?"

[0115] Question and answer exchange

[0116] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which then receives the user's answer.

[0117] Analysis of responses

[0118] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This allows the server to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[0119] Next question generation

[0120] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[0121] Iteration

[0122] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[0123] Generating personality test results

[0124] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0125] Specific examples

[0126] If a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[0127] Prompt Sentence Examples

[0128] Below are some example prompts to be input to the generative AI model:

[0129] "What situations cause you the most stress?"

[0130] "How do you respond when you have to complete many tasks in a short amount of time?"

[0131] "How do you relieve stress?"

[0132] In this way, by combining question generation using generative AI with answer analysis using natural language processing technology, the system can analyze users' personality traits with high accuracy. This system goes beyond the limits of conventional personality tests, enabling more accurate analysis of personality traits and the recruitment of appropriate personnel.

[0133] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0134] Step 1: Initial Setup

[0135] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules. At this time, it also loads the dialogue scenarios to be used and mapping information for personality traits. The input is the configuration file, and the output is the initialized AI and NLP modules. Specifically, it uses a Python library to load the API key and model.

[0136] Step 2: Enter your registration information

[0137] A user accesses the system using a terminal and enters registration information such as name, age, desired occupation, etc. The terminal sends this information to the server. The input is the registration information entered by the user into the terminal, and the output is the user information sent to the server. Specifically, information is collected using a web form and sent to the server via an HTTP request.

[0138] Step 3: Generate your first question

[0139] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question. The input is the user's registered information, and the output is the generated initial question. Specifically, the server calls the ChatGPT API to generate a question using a specific prompt, such as "What is the most stressful situation for you?"

[0140] Step 4: Display the question and enter the answer

[0141] The terminal displays the initial question received from the server to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the user's answer sent to the server. Specifically, this involves displaying a question on a web page, allowing the user to enter an answer in a text box and press the submit button.

[0142] Step 5: Analyze the answers

[0143] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the analyzed personality traits. Specifically, it uses an NLP library to perform sentiment analysis (e.g., calculating sentiment scores) and keyword extraction (e.g., tokenization and stop word removal).

[0144] Step 6: Generate the next question

[0145] The server generates the next question based on the analysis result. For example, if the user answers "When I have to complete many tasks in a short period of time," the next question generated would be "How do you relieve stress?" The input is the analysis result, and the output is the next question. Specifically, the server calls the ChatGPT API again and generates a new question using the appropriate prompt.

[0146] Step 7: Iterate

[0147] The server repeatedly exchanges questions and answers to collect detailed information about the user's personality. The input is each user's answer and its analysis results, and the output is the accumulated analysis results. Each answer and analysis result is stored in a database. Specifically, the system includes a loop process in which, after each answer is received and analyzed, the next question is generated and sent to the device.

[0148] Step 8: Generate personality results

[0149] Once enough necessary information has been collected, the server generates the final personality assessment results. The results are created in report format and fed back to the user via the terminal. The input is the accumulated analysis results, and the output is a report of the personality assessment results that is fed back to the user. Specifically, the various analysis results are integrated to generate a report of the evaluation of each of the user's personality traits, and the report is provided to the user in a format such as PDF.

[0150] The above is the specific flow of the program processing of this system. Each step processes and calculates data based on the input, and obtains the desired output, thereby achieving an efficient and highly accurate personality test.

[0151] (Application example 1)

[0152] 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."

[0153] Conventional personality testing systems rely on the subjective judgment of human interviewers and often lack reliability and consistency. Furthermore, they lack the means to analyze the personality traits of individual employees in detail to improve stress management and work efficiency. This makes it difficult to assign appropriate work and provide individual support, potentially reducing production efficiency within the factory.

[0154] 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.

[0155] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving a response from a user via a terminal, means for analyzing the user's response using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's responses, means for analyzing the user's personality traits and stress level and suggesting appropriate task assignments, and means for providing actions and support to help workers relax when they are feeling stressed. This makes it possible to analyze the personality traits of employees in detail and achieve accurate task assignments and stress management.

[0156] "Generative AI" is AI that has the ability to generate questions through dialogue with the user.

[0157] A "terminal" is an interface device that allows a user to input information. Examples include personal computers and smartphones.

[0158] "Natural language processing technology" is a technology for handling, analyzing, and processing human language.

[0159] "User's answer" is text data provided by the user in response to a question posed by the generative artificial intelligence.

[0160] "Personality traits" are individual personality characteristics or features that are estimated by analyzing the user's responses.

[0161] The "personality diagnosis result" is a detailed diagnosis report generated based on the user's personality traits.

[0162] The "stress level" is an index that indicates the degree of stress that the user is feeling.

[0163] "Work assignment" is the assignment of a person or machine to perform a specific task.

[0164] "Actions" are specific movements or behaviors taken to manage or support stress.

[0165] "Support" refers to advice and help provided to users when they are stressed.

[0166] This invention relates to a personality trait analysis system aimed at improving work efficiency in factories and managing worker stress. This system analyzes personality traits through dialogue with users using generative artificial intelligence and natural language processing technology.

[0167] The system consists of the following elements:

[0168] 1. Generative AI module: Used to generate questions.

[0169] 2. Terminal: An interface device through which a user makes input (e.g., a computer or smartphone).

[0170] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0171] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0172] 5. Database: Stores user response data and personality trait information.

[0173] System Operation Overview

[0174] The main processing steps of this system are shown below.

[0175] Hardware and Software Use

[0176] Hardware: Robot terminals and servers in factories

[0177] Software: Generative AI (e.g., OpenAI API), natural language processing module, database module

[0178] Data processing and calculation

[0179] 1. Question generation:

[0180] The server invokes a generative artificial intelligence to generate questions based on appropriate prompts, with the initial prompt being "What was the most stressful thing about work this week?"

[0181] 2. Receiving user responses:

[0182] The user answers the generated questions through the terminal. For example, if the user answers "There was a lot of work, and I was worried about whether I would be able to meet the deadline," the answer is sent from the terminal to the server.

[0183] 3. Analysis of answers:

[0184] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction, to estimate the user's personality traits and stress level.

[0185] 4. Stress and Work Management:

[0186] Based on the analysis results, the system identifies the user's personality traits and stress level, suggests appropriate task assignments, and provides support and actions to help them relax if they are under a high level of stress.

[0187] 5. Data storage:

[0188] Each response and analysis result is stored in a database and used for the next diagnosis and to improve work efficiency.

[0189] Specific examples

[0190] For example, if a user answers the question "What was the most stressful thing about work this week?" with "The workload was heavy and I was worried about whether I would be able to meet the deadline," the server will use this answer to analyze the personality trait of "stress response to deadlines." The next question will then be generated: "When you have a lot of work and a deadline looming, how do you deal with it?" Through this series of conversations, the user's personality traits and stress level are understood in detail.

[0191] This allows for work arrangements that are optimized for the user's characteristics and specific support for stress reduction.

[0192] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0193] Specific processing flow of the system program that realizes the application example

[0194] Step 1: Initial Setup

[0195] The server initializes the generative artificial intelligence module and the natural language processing (NLP) module. The server loads the dialogue scenario and personality trait mapping information.

[0196] Input: Initialization file

[0197] Data processing / calculation: Loading dialogue scenarios and personality trait information into system memory

[0198] Output: Initialized generative AI and NLP modules

[0199] Step 2: Start the personality test

[0200] Users access the system using a terminal and enter the necessary registration information (e.g., name, age, desired occupation), which is then sent to the server.

[0201] Input: User registration information

[0202] Data processing / calculation: Collection of user input data and transmission to the server

[0203] Output: User registration information stored on the server

[0204] Step 3: Generate your first question

[0205] The server selects an appropriate dialogue scenario based on the user's registration information and calls the generative artificial intelligence API to generate the initial question.

[0206] Input: User registration information and interaction scenario

[0207] Data processing / calculation: Generative AI API is used to generate appropriate questions

[0208] Output: The initial question generated

[0209] Step 4: Question and answer exchange

[0210] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which the terminal then sends to the server.

[0211] Input: Generated questions and user answers

[0212] Data processing / calculation: Receive user answers in text format and send them to the server

[0213] Output: User answers stored on the server

[0214] Step 5: Analyze the answers

[0215] The server uses NLP technology to analyze the user's responses, including sentiment analysis, intent estimation, and keyword extraction, to assess the user's personality traits and stress level.

[0216] Input: User's answer

[0217] Data processing / calculation: Using NLP technology to analyze emotions and infer intentions, and calculate personality traits

[0218] Output: User's personality traits and stress level as analysis results

[0219] Step 6: Generate the next question

[0220] The server generates the next question based on the analysis results, such as "When you have a lot of work to do and a deadline looming, how do you handle it?"

[0221] Input: Personality traits as analysis results and previous answers

[0222] Data processing / calculation: Calling a generative AI API to generate the next appropriate question

[0223] Output: Next question to display

[0224] Step 7: Iterate

[0225] The process from step 4 to step 6 is repeated to collect detailed information about the user's personality traits. Each response and analysis result is stored in a database.

[0226] Input: Previous conversation history and analysis results

[0227] Data processing / calculation: Repeating the interactive process and storing the data in a database

[0228] Output: Detailed personality trait data

[0229] Step 8: Generate personality results

[0230] Once enough information is collected, the server generates a final personality test result, which is then presented in the form of a report and fed back to the user.

[0231] Input: Accumulated personality trait data

[0232] Data processing / calculation: Generating personality test results and creating reports

[0233] Output: Personality test results in report format and feedback to the user

[0234] 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.

[0235] This invention relates to a personality testing system that uses generative artificial intelligence and an emotion engine. This system analyzes the personality traits of users in detail through dialogue with them and provides highly reliable personality diagnosis.

[0236] System Overview

[0237] The system mainly consists of the following elements:

[0238] 1. Generative artificial intelligence module: Generates questions using generative AI.

[0239] 2. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[0240] 3. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[0241] 4. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0242] 5. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0243] 6. Database: Stores user response data and personality trait information.

[0244] System Operation

[0245] The processing of the system will be explained in natural language below.

[0246] 1. Initial Setup

[0247] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[0248] 2. Start the personality test

[0249] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0250] 3. First question generation

[0251] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[0252] 4. Question and answer exchanges

[0253] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[0254] 5. Answer Analysis and Emotion Recognition

[0255] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[0256] 6. Next Question Generation

[0257] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, the server generates a next question asking for specific countermeasures, such as "How do you relieve stress?"

[0258] 7. Iteration

[0259] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[0260] 8. Generating personality test results

[0261] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0262] Specific examples

[0263] For example, suppose a user answers, "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits can be understood in detail.

[0264] The above is an embodiment of the present invention. This goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring by combining generative artificial intelligence and an emotion engine.

[0265] The processing flow will be explained below.

[0266] Step 1:

[0267] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. At this stage, it also loads the dialogue scenarios and personality trait mapping information to be used.

[0268] Step 2:

[0269] The user accesses the system's entry point using a terminal and enters the necessary registration information (name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0270] Step 3:

[0271] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[0272] Step 4:

[0273] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[0274] Step 5:

[0275] The server analyzes the user's responses received using natural language processing technology and an emotion engine. The emotion engine recognizes the emotions in the responses and evaluates their impact on the estimation of personality traits.

[0276] Step 6:

[0277] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the server generates the next question, "How do you relieve stress?" The device displays the generated question to the user.

[0278] Step 7:

[0279] The user answers a new question using the device. The device sends the answer to the server. The server analyzes it again, and the emotion engine recognizes the emotion. This process is repeated until the necessary personality trait data is accumulated.

[0280] Step 8:

[0281] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[0282] Step 9:

[0283] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[0284] As a concrete example, consider a case where a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress from this answer. Next, the server generates a question, "How do you respond when a deadline is approaching?" and displays it to the user. The user answers, "I exercise to relieve stress," and the emotion engine recognizes a positive emotion from this answer. Based on this information, the server updates the user's personality trait data.

[0285] This series of processes allows for a detailed understanding of the user's personality traits and enables highly accurate personality diagnosis.

[0286] Example 2

[0287] 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."

[0288] Conventional personality testing systems have a problem with low accuracy of personality diagnosis results because the question generation and answer analysis are fixed and do not fully take into account the user's emotional state. Furthermore, they simply ask many questions one-sidedly and are unable to respond flexibly to the user's real-time emotional changes. This tends to create a stressful experience for users and result in low-quality information.

[0289] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0290] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving answers from a user via a terminal, means for analyzing the user's answer using natural language processing technology, means for recognizing the user's emotion using an emotion engine and correcting the analysis result based on the emotion, means for generating a next question based on the estimated personality traits, and means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers. This enables a highly accurate personality diagnosis that takes the user's emotional state into consideration.

[0291] "Generative AI" is an AI technology used to generate questions and dialogue.

[0292] A "terminal" is a device that has an interface for users to input data, and specifically includes personal computers and smartphones.

[0293] A "user" is a person who accesses the system and enters information or answers questions.

[0294] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and infers intent.

[0295] An "emotion engine" is a technology that recognizes emotions from user input and corrects the results of information analysis based on those emotions.

[0296] "Analysis results" are the analysis results of user responses obtained using natural language processing technology and an emotion engine.

[0297] "Personality traits" are characteristics that indicate the user's personality and behavioral patterns, and are estimated based on the analysis results.

[0298] A "question" is a question created by generative artificial intelligence and presented to the user.

[0299] "Personality test results" are detailed information about the user's personality that is created based on repeated question and answer exchanges.

[0300] This invention relates to a personality testing system using generative artificial intelligence and an emotion engine. To implement the system, the following elements are combined and operated:

[0301] System Components

[0302] The system mainly consists of the following hardware and software elements:

[0303] 1. Generative AI module: Software for generating questions using generative AI. Specifically, ChatGPT is used.

[0304] 2. Emotion engine: Software that recognizes emotions from user responses and corrects the analysis results.

[0305] 3. Terminal: An interface through which users input information, including computers, smartphones, etc.

[0306] 4. Server: This is the hardware that generates questions, receives and analyzes answers, generates the next questions, and creates the final diagnosis results.

[0307] 5. Natural Language Processing (NLP) module: Software with technology for analyzing user responses.

[0308] 6. Database: A storage for saving user response data and personality trait information.

[0309] System Operation

[0310] The overall processing of this system will be explained below.

[0311] Initial Setup

[0312] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. It also loads the dialogue scenarios and personality trait mapping information to be used.

[0313] Starting the personality test

[0314] Users access the system using a terminal and enter the necessary registration information (name, age, desired occupation, etc.), which is then sent from the terminal to the server.

[0315] First question generation

[0316] The server selects an appropriate dialogue scenario based on the user's registered information and calls the ChatGPT API to generate the initial question, for example, "What is the most stressful situation for you?"

[0317] Question and answer exchange

[0318] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which is then sent from the terminal to the server.

[0319] Answer analysis and emotion recognition

[0320] The server uses natural language processing technology and an emotion engine to analyze the user's responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. Based on the results, the user's personality traits are estimated. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[0321] Next question generation

[0322] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes high stress, it generates the question "How do you relieve stress?"

[0323] Iteration

[0324] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[0325] Generating personality test results

[0326] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0327] Specific examples

[0328] For example, if a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress, it will then generate the next question: "How do you respond when a deadline is approaching?" If the user answers "I exercise to relieve stress," the emotion engine will recognize the positive emotion in that answer, and the server will update the personality traits based on that data.

[0329] Prompt Sentence Examples

[0330] 1. First question generation:

[0331] "The user's name is Taro Yamada, he is 30 years old, and he wants to be an engineer. Please generate the following question as the first question for the user: 'What is the most stressful situation for you?'"

[0332] 2. Generate the following question:

[0333] "The user answers, 'When I have to accomplish many tasks in a short amount of time,' and the emotion engine recognizes this as a high level of stress. Next, generate a question that asks specific ways to deal with stress: 'How do you relieve stress?'"

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Step 1: Initial Setup

[0336] The server initializes all modules and loads the necessary information.

[0337] Specific behavior:

[0338] The server reads the initialization file.

[0339] The server initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine.

[0340] The server loads the dialogue scenario and personality trait mapping information.

[0341] Input: Initial setting file, dialogue scenario information, personality trait mapping information

[0342] Output: Initialized modules, loaded information

[0343] Step 2: Enter user registration information

[0344] A user accesses the system using a terminal and enters the required information.

[0345] Specific behavior:

[0346] The user accesses the system's registration page using a terminal.

[0347] The user enters personal information such as name, age, and desired job type.

[0348] The terminal transmits the user's information to the server.

[0349] Input: User registration information (name, age, desired job type, etc.)

[0350] Output: Registration information sent to the server

[0351] Step 3: Generate your first question

[0352] The server generates an initial question based on the user's registration information.

[0353] Specific behavior:

[0354] The server receives the user's registration information.

[0355] The server selects the appropriate dialogue scenario.

[0356] The server calls the API of generative artificial intelligence (ChatGPT) to generate the initial question.

[0357] Input: User registration information

[0358] Output: Initial question (e.g., "What is the most stressful situation for you?")

[0359] Step 4: Question and answer exchange

[0360] The generated question is presented to the user and an answer is received from the user.

[0361] Specific behavior:

[0362] The server generates a question and sends it to the terminal.

[0363] The terminal displays the question to the user.

[0364] The user enters an answer to the question.

[0365] The terminal sends the user's answer to the server.

[0366] Input: Initial question, user answer

[0367] Output: The user's response received

[0368] Step 5: Response analysis and emotion recognition

[0369] The server analyzes the user's responses and recognizes emotions using an emotion engine.

[0370] Specific behavior:

[0371] The server analyzes the received user responses using a natural language processing (NLP) module.

[0372] The analysis includes sentiment analysis, intent estimation, and keyword extraction.

[0373] The server uses an emotion engine to recognize the user's emotion.

[0374] The server estimates the user's personality traits based on the analysis results.

[0375] Input: User's answer

[0376] Output: Analysis results (sentiment analysis, intent estimation, keyword extraction), recognized emotions

[0377] Step 6: Generate the next question

[0378] The server generates the next question based on the analysis results and emotion recognition results.

[0379] Specific behavior:

[0380] The server integrates the analysis results with the output of the emotion engine.

[0381] The server calls the API of generative artificial intelligence (ChatGPT) to generate the next question.

[0382] The server sends the following question to the terminal:

[0383] Input: Analysis results, emotion recognition results

[0384] Output: Next question (e.g., "How do you relieve stress?")

[0385] Step 7: Iterate

[0386] This process is repeated multiple times to gather detailed information about the user's personality traits.

[0387] Specific behavior:

[0388] Repeat process steps 4 to 6.

[0389] Each response and analysis result is stored in a database.

[0390] The server uses an emotion engine to track the user's emotional changes.

[0391] The server adaptively adjusts questions based on emotional changes.

[0392] Input: User answers, analysis results, emotion recognition results

[0393] Output: Updated personality trait database, adaptively adjusted questions

[0394] Step 8: Generate personality results

[0395] Once the server has collected enough information, it will generate a final personality test result.

[0396] Specific behavior:

[0397] The server determines whether it has gathered enough information.

[0398] The server generates the final personality test results.

[0399] The results generated by the server are compiled in a report format.

[0400] The server feeds back the final report to the user.

[0401] Input: Collected personality traits database

[0402] Output: Personality test results, feedback in report format

[0403] (Application example 2)

[0404] 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."

[0405] Conventional personality diagnosis systems only analyze a user's personality traits, but the ways in which the results can be utilized are limited. Furthermore, it is difficult to propose products that meet the individual needs of users in virtual stores. Therefore, there is a need for a method that can more precisely analyze a user's personality traits and link them to product proposals.

[0406] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving an answer from the user via the terminal, means for analyzing the user's answer using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers, and means for making product suggestions based on the user's personality in a virtual store. This makes it possible to grasp the user's personality traits in detail and to suggest individual products based on them.

[0407] "Generative AI" is an AI technology that can automatically generate text based on user input.

[0408] The "initial question" is the question that is presented first when starting a personality test for the user.

[0409] A "terminal" is an electronic device that allows a user to input information, and includes smartphones, personal computers, and the like.

[0410] "User responses" are text data and other input information provided by the user.

[0411] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0412] "Analyzing user responses" is the process of interpreting the information provided by the user and identifying various characteristics based on that information.

[0413] "Personality traits" are information about a user's emotions, behavioral patterns, and individual characteristics.

[0414] "Generating the next question" is the process of generating a new question based on the user's answer and the analysis results.

[0415] "Personality diagnosis result" refers to a comprehensive result regarding the user's personality that is generated based on information collected through dialogue.

[0416] A "virtual store" is a virtual store that does not exist in a physical location and provides products and services over the Internet.

[0417] "Product suggestion" is the process of recommending specific products or services based on a user's needs and characteristics.

[0418] This invention relates to a personalized shopping assistant in a virtual store using generative artificial intelligence and an emotion engine. This system can analyze the user's personality traits in detail and make personalized product recommendations based on the results.

[0419] System configuration

[0420] The system mainly consists of the following elements:

[0421] 1. Generative AI: Responsible for generating initial and follow-up questions.

[0422] 2. Terminal: A device such as a smartphone or smart glasses that allows the user to input information and displays results.

[0423] 3. Natural language processing technology: Used to analyze user responses and infer personality traits.

[0424] 4. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[0425] 5. Server: Generates questions, receives and analyzes answers, and generates and stores personality test results.

[0426] 6. Database: Stores user response data and personality trait information.

[0427] System Operation Overview

[0428] 1. Initial Setup:

[0429] The server loads the initial configuration file and initializes the generative artificial intelligence (e.g. ChatGPT), natural language processing (NLP) module, and emotion engine. Dialogue scenarios and personality trait mapping information are also loaded at this time.

[0430] 2. Start the personality test:

[0431] Users access the system using a device such as a smartphone or smart glasses and enter the necessary registration information (such as name, age, desired product, etc.), which is then sent to the server.

[0432] 3. First question generation:

[0433] The server calls ChatGPT's API based on the user's registration information to generate an initial question, such as "What is the most stressful situation for you?"

[0434] 4. Question and answer exchange:

[0435] The generated question is sent to the terminal and displayed to the user, the user enters an answer, and the terminal sends the answer to the server.

[0436] 5. Answer analysis and emotion recognition:

[0437] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and based on the results, it infers the user's personality traits.

[0438] 6. Generate the following question:

[0439] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, it will generate the next question, asking for specific countermeasures, such as "How do you relieve stress?"

[0440] 7. Generating personality test results:

[0441] Through repeated processing, the system collects detailed information about the user's personality. The final personality assessment results are generated by the server and fed back to the user in the form of a report. This information is also used to make personalized product recommendations in the virtual store.

[0442] Specific examples

[0443] For example, if a user answers, "I feel stressed when a work deadline is approaching," the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits are understood in detail and used to suggest products in the virtual store.

[0444] Example prompt sentence:

[0445] Generate the next question based on the user's answers and sentiment.

[0446] Answer: "I feel stressed when I'm facing deadlines at work."

[0447] Emotion: "High stress"

[0448] Using this prompt, the generative AI model generates questions to drive the next dialogue, a process that enables personalized shopping based on the user's personality traits.

[0449] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0450] Step 1:

[0451] The server reads the initial setting file and initializes the generative artificial intelligence, natural language processing module, and emotion engine. This loads the dialogue scenario and personality trait mapping information. The input used in this step is the initial setting file, and the output is the initialized generative artificial intelligence, natural language processing module, and emotion engine.

[0452] Step 2:

[0453] A user accesses the system using a terminal and enters the necessary registration information, such as name, age, desired product, etc. The terminal then transmits this information to the server. The input here is the registration information entered by the user into the terminal, and the output is the registration information transmitted to the server.

[0454] Step 3:

[0455] The server calls the API of the generative artificial intelligence based on the received user registration information to generate the initial question. For example, the question might be, "What situation causes you the most stress?" The input is the user registration information, and the output is the generated initial question.

[0456] Step 4:

[0457] The generated initial question is sent from the server to the terminal and displayed to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the question displayed on the terminal and the user's answer sent to the server.

[0458] Step 5:

[0459] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the user's personality traits as an analysis result.

[0460] Step 6:

[0461] The server calls the generative artificial intelligence API again based on the analysis results and the output of the emotion engine to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the next question generated is, "How do you relieve stress?" The input is the analysis results and the output of the emotion engine, and the output is the generated next question.

[0462] Step 7:

[0463] The server sends the next question to the device, and the user enters an answer, which is then sent back to the server. This process is repeated to gather more information about the user's personality. The input is the next question and the user's additional answer, and the output is a new question based on the additional answer and the analysis results.

[0464] Step 8:

[0465] Once enough information has been collected, the server generates the final personality assessment results. These results are created in the form of a report and sent to the terminal. This information is also used to make personalized product recommendations within the virtual store. The input is the collected personality trait information, and the output is a report of the personality assessment results and product recommendations.

[0466] 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.

[0467] 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.

[0468] 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.

[0469] [Second embodiment]

[0470] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0471] 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.

[0472] 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).

[0473] 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.

[0474] 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.

[0475] 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).

[0476] 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.

[0477] 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.

[0478] 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.

[0479] 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.

[0480] 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.

[0481] 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."

[0482] This invention relates to a highly accurate personality testing system using generative artificial intelligence. This system analyzes the user's personality traits in detail through natural dialogue with the user, improving the reliability of personality tests in employment examinations.

[0483] System Overview

[0484] The system mainly consists of the following elements:

[0485] 1. Generative AI module: Questions are generated using generative AI such as ChatGPT.

[0486] 2. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[0487] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0488] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0489] 5. Database: Stores user response data and personality trait information.

[0490] System Operation

[0491] The processing of the system will be explained in natural language below.

[0492] 1. Initial Setup

[0493] The server reads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[0494] 2. Start the personality test

[0495] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0496] 3. First question generation

[0497] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[0498] 4. Question and answer exchanges

[0499] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[0500] 5. Analysis of responses

[0501] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This makes it possible to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[0502] 6. Next Question Generation

[0503] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[0504] 7. Iteration

[0505] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[0506] 8. Generating personality test results

[0507] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0508] Specific examples

[0509] For example, if a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[0510] The above is an embodiment of the present invention, which goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring of personnel.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[0514] Step 2:

[0515] A user accesses the system's entry point using a terminal and enters necessary information such as name, age, desired occupation, etc. into the registration form. The terminal then sends this registration information to the server.

[0516] Step 3:

[0517] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[0518] Step 4:

[0519] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[0520] Step 5:

[0521] The server analyzes the user's responses using natural language processing technology, including sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results.

[0522] Step 6:

[0523] The server then calls the ChatGPT API again to generate the next question based on the estimated personality traits, and sends the generated next question to the device, which then displays it to the user.

[0524] Step 7:

[0525] The user answers new questions using the device. The device sends the answers to the server. Steps 5 and 6 are repeated to accumulate the necessary personality trait data.

[0526] Step 8:

[0527] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[0528] Step 9:

[0529] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[0530] This is the specific processing flow of the personality testing system that utilizes generative AI. This process enables detailed and accurate analysis of the user's personality traits and supports appropriate hiring decisions.

[0531] Example 1

[0532] 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."

[0533] Conventional personality tests rely on limited question and answer patterns, making it difficult to grasp the user's personality traits in detail. Furthermore, the exchange of questions and answers is mechanical, lacking naturalness and reliability. As a result, accuracy is insufficient for selecting the right candidates, especially in job interviews.

[0534] 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.

[0535] In this invention, the server includes means for generating initial questions using generative artificial intelligence, means for receiving registration information and answers from the user via the terminal, and means for analyzing the user's answers using natural language processing technology. This allows for detailed analysis of the user's personality traits while maintaining the naturalness of the dialogue, enabling highly accurate personality diagnosis.

[0536] "Generative AI" is an AI system that uses natural language processing technology to generate text and dialogue.

[0537] A "terminal" is a device that allows a user to access a system and input or receive information. Examples of such devices include personal computers, smartphones, and tablets.

[0538] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language. This technology includes sentiment analysis, intent estimation, and keyword extraction.

[0539] "Analysis results" are data and information obtained from users' responses using natural language processing technology.

[0540] "Personality traits" is a concept that refers to specific characteristics and tendencies regarding a user's behavior, reactions, and emotions.

[0541] A "dialogue scenario" is a set of questions and answers designed to achieve a specific purpose.

[0542] A "database" is a system for storing, managing, and retrieving data in an organized and efficient manner.

[0543] The "personality assessment results" are reports that detail the user's personality traits, generated based on the user's answers and the analysis results.

[0544] This invention relates to a highly accurate personality testing system using generative artificial intelligence. The system aims to improve the reliability of personality tests in employment examinations by analyzing the user's personality traits in detail through natural dialogue with the user.

[0545] System Configuration

[0546] The system of the present invention includes the following elements:

[0547] 1. Generative AI module: This is a module that generates questions using a generative AI model, for example, using ChatGPT's API.

[0548] 2. Device: The interface through which the user provides input (e.g., computer, smartphone, tablet).

[0549] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0550] 4. Natural Language Processing (NLP) module: This is a technology for analyzing user responses. Specifically, it performs sentiment analysis, intent estimation, keyword extraction, etc.

[0551] 5. Database: Stores user response data and personality trait information.

[0552] System Operation

[0553] Initial Setup

[0554] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[0555] Starting the personality test

[0556] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0557] First question generation

[0558] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question, such as, "What is the most stressful situation for you?"

[0559] Question and answer exchange

[0560] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which then receives the user's answer.

[0561] Analysis of responses

[0562] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This allows the server to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[0563] Next question generation

[0564] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[0565] Iteration

[0566] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[0567] Generating personality test results

[0568] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0569] Specific examples

[0570] If a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[0571] Prompt Sentence Examples

[0572] Below are some example prompts to be input to the generative AI model:

[0573] "What situations cause you the most stress?"

[0574] "How do you respond when you have to complete many tasks in a short amount of time?"

[0575] "How do you relieve stress?"

[0576] In this way, by combining question generation using generative AI with answer analysis using natural language processing technology, the system can analyze users' personality traits with high accuracy. This system goes beyond the limits of conventional personality tests, enabling more accurate analysis of personality traits and the recruitment of appropriate personnel.

[0577] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0578] Step 1: Initial Setup

[0579] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules. At this time, it also loads the dialogue scenarios to be used and mapping information for personality traits. The input is the configuration file, and the output is the initialized AI and NLP modules. Specifically, it uses a Python library to load the API key and model.

[0580] Step 2: Enter your registration information

[0581] A user accesses the system using a terminal and enters registration information such as name, age, desired occupation, etc. The terminal sends this information to the server. The input is the registration information entered by the user into the terminal, and the output is the user information sent to the server. Specifically, information is collected using a web form and sent to the server via an HTTP request.

[0582] Step 3: Generate your first question

[0583] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question. The input is the user's registered information, and the output is the generated initial question. Specifically, the server calls the ChatGPT API to generate a question using a specific prompt, such as "What is the most stressful situation for you?"

[0584] Step 4: Display the question and enter the answer

[0585] The terminal displays the initial question received from the server to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the user's answer sent to the server. Specifically, this involves displaying a question on a web page, allowing the user to enter an answer in a text box and press the submit button.

[0586] Step 5: Analyze the answers

[0587] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the analyzed personality traits. Specifically, it uses an NLP library to perform sentiment analysis (e.g., calculating sentiment scores) and keyword extraction (e.g., tokenization and stop word removal).

[0588] Step 6: Generate the next question

[0589] The server generates the next question based on the analysis result. For example, if the user answers "When I have to complete many tasks in a short period of time," the next question generated would be "How do you relieve stress?" The input is the analysis result, and the output is the next question. Specifically, the server calls the ChatGPT API again and generates a new question using the appropriate prompt.

[0590] Step 7: Iterate

[0591] The server repeatedly exchanges questions and answers to collect detailed information about the user's personality. The input is each user's answer and its analysis results, and the output is the accumulated analysis results. Each answer and analysis result is stored in a database. Specifically, the system includes a loop process in which, after each answer is received and analyzed, the next question is generated and sent to the device.

[0592] Step 8: Generate personality results

[0593] Once enough necessary information has been collected, the server generates the final personality assessment results. The results are created in report format and fed back to the user via the terminal. The input is the accumulated analysis results, and the output is a report of the personality assessment results that is fed back to the user. Specifically, the various analysis results are integrated to generate a report of the evaluation of each of the user's personality traits, and the report is provided to the user in a format such as PDF.

[0594] The above is the specific flow of the program processing of this system. Each step processes and calculates data based on the input, and obtains the desired output, thereby achieving an efficient and highly accurate personality test.

[0595] (Application example 1)

[0596] 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."

[0597] Conventional personality testing systems rely on the subjective judgment of human interviewers and often lack reliability and consistency. Furthermore, they lack the means to analyze the personality traits of individual employees in detail to improve stress management and work efficiency. This makes it difficult to assign appropriate work and provide individual support, potentially reducing production efficiency within the factory.

[0598] 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.

[0599] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving a response from a user via a terminal, means for analyzing the user's response using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's responses, means for analyzing the user's personality traits and stress level and suggesting appropriate task assignments, and means for providing actions and support to help workers relax when they are feeling stressed. This makes it possible to analyze the personality traits of employees in detail and achieve accurate task assignments and stress management.

[0600] "Generative AI" is AI that has the ability to generate questions through dialogue with the user.

[0601] A "terminal" is an interface device that allows a user to input information. Examples include personal computers and smartphones.

[0602] "Natural language processing technology" is a technology for handling, analyzing, and processing human language.

[0603] "User's answer" is text data provided by the user in response to a question posed by the generative artificial intelligence.

[0604] "Personality traits" are individual personality characteristics or features that are estimated by analyzing the user's responses.

[0605] The "personality diagnosis result" is a detailed diagnosis report generated based on the user's personality traits.

[0606] The "stress level" is an index that indicates the degree of stress that the user is feeling.

[0607] "Work assignment" is the assignment of a person or machine to perform a specific task.

[0608] "Actions" are specific movements or behaviors taken to manage or support stress.

[0609] "Support" refers to advice and help provided to users when they are stressed.

[0610] This invention relates to a personality trait analysis system aimed at improving work efficiency in factories and managing worker stress. This system analyzes personality traits through dialogue with users using generative artificial intelligence and natural language processing technology.

[0611] The system consists of the following elements:

[0612] 1. Generative AI module: Used to generate questions.

[0613] 2. Terminal: An interface device through which a user makes input (e.g., a computer or smartphone).

[0614] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0615] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0616] 5. Database: Stores user response data and personality trait information.

[0617] System Operation Overview

[0618] The main processing steps of this system are shown below.

[0619] Hardware and Software Use

[0620] Hardware: Robot terminals and servers in factories

[0621] Software: Generative AI (e.g., OpenAI API), natural language processing module, database module

[0622] Data processing and calculation

[0623] 1. Question generation:

[0624] The server invokes a generative artificial intelligence to generate questions based on appropriate prompts, with the initial prompt being "What was the most stressful thing about work this week?"

[0625] 2. Receiving user responses:

[0626] The user answers the generated questions through the terminal. For example, if the user answers "There was a lot of work, and I was worried about whether I would be able to meet the deadline," the answer is sent from the terminal to the server.

[0627] 3. Analysis of answers:

[0628] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction, to estimate the user's personality traits and stress level.

[0629] 4. Stress and Work Management:

[0630] Based on the analysis results, the system identifies the user's personality traits and stress level, suggests appropriate task assignments, and provides support and actions to help them relax if they are under a high level of stress.

[0631] 5. Data storage:

[0632] Each response and analysis result is stored in a database and used for the next diagnosis and to improve work efficiency.

[0633] Specific examples

[0634] For example, if a user answers the question "What was the most stressful thing about work this week?" with "The workload was heavy and I was worried about whether I would be able to meet the deadline," the server will use this answer to analyze the personality trait of "stress response to deadlines." The next question will then be generated: "When you have a lot of work and a deadline looming, how do you deal with it?" Through this series of conversations, the user's personality traits and stress level are understood in detail.

[0635] This allows for work arrangements that are optimized for the user's characteristics and specific support for stress reduction.

[0636] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0637] Specific processing flow of the system program that realizes the application example

[0638] Step 1: Initial Setup

[0639] The server initializes the generative artificial intelligence module and the natural language processing (NLP) module. The server loads the dialogue scenario and personality trait mapping information.

[0640] Input: Initialization file

[0641] Data processing / calculation: Loading dialogue scenarios and personality trait information into system memory

[0642] Output: Initialized generative AI and NLP modules

[0643] Step 2: Start the personality test

[0644] Users access the system using a terminal and enter the necessary registration information (e.g., name, age, desired occupation), which is then sent to the server.

[0645] Input: User registration information

[0646] Data processing / calculation: Collection of user input data and transmission to the server

[0647] Output: User registration information stored on the server

[0648] Step 3: Generate your first question

[0649] The server selects an appropriate dialogue scenario based on the user's registration information and calls the generative artificial intelligence API to generate the initial question.

[0650] Input: User registration information and interaction scenario

[0651] Data processing / calculation: Generative AI API is used to generate appropriate questions

[0652] Output: The initial question generated

[0653] Step 4: Question and answer exchange

[0654] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which the terminal then sends to the server.

[0655] Input: Generated questions and user answers

[0656] Data processing / calculation: Receive user answers in text format and send them to the server

[0657] Output: User answers stored on the server

[0658] Step 5: Analyze the answers

[0659] The server uses NLP technology to analyze the user's responses, including sentiment analysis, intent estimation, and keyword extraction, to assess the user's personality traits and stress level.

[0660] Input: User's answer

[0661] Data processing / calculation: Using NLP technology to analyze emotions and infer intentions, and calculate personality traits

[0662] Output: User's personality traits and stress level as analysis results

[0663] Step 6: Generate the next question

[0664] The server generates the next question based on the analysis results, such as "When you have a lot of work to do and a deadline looming, how do you handle it?"

[0665] Input: Personality traits as analysis results and previous answers

[0666] Data processing / calculation: Calling a generative AI API to generate the next appropriate question

[0667] Output: Next question to display

[0668] Step 7: Iterate

[0669] The process from step 4 to step 6 is repeated to collect detailed information about the user's personality traits. Each response and analysis result is stored in a database.

[0670] Input: Previous conversation history and analysis results

[0671] Data processing / calculation: Repeating the interactive process and storing the data in a database

[0672] Output: Detailed personality trait data

[0673] Step 8: Generate personality results

[0674] Once enough information is collected, the server generates a final personality test result, which is then presented in the form of a report and fed back to the user.

[0675] Input: Accumulated personality trait data

[0676] Data processing / calculation: Generating personality test results and creating reports

[0677] Output: Personality test results in report format and feedback to the user

[0678] 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.

[0679] This invention relates to a personality testing system that uses generative artificial intelligence and an emotion engine. This system analyzes the personality traits of users in detail through dialogue with them and provides highly reliable personality diagnosis.

[0680] System Overview

[0681] The system mainly consists of the following elements:

[0682] 1. Generative artificial intelligence module: Generates questions using generative AI.

[0683] 2. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[0684] 3. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[0685] 4. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0686] 5. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0687] 6. Database: Stores user response data and personality trait information.

[0688] System Operation

[0689] The processing of the system will be explained in natural language below.

[0690] 1. Initial Setup

[0691] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[0692] 2. Start the personality test

[0693] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0694] 3. First question generation

[0695] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[0696] 4. Question and answer exchanges

[0697] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[0698] 5. Answer Analysis and Emotion Recognition

[0699] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[0700] 6. Next Question Generation

[0701] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, the server generates a next question asking for specific countermeasures, such as "How do you relieve stress?"

[0702] 7. Iteration

[0703] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[0704] 8. Generating personality test results

[0705] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0706] Specific examples

[0707] For example, suppose a user answers, "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits can be understood in detail.

[0708] The above is an embodiment of the present invention. This goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring by combining generative artificial intelligence and an emotion engine.

[0709] The processing flow will be explained below.

[0710] Step 1:

[0711] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. At this stage, it also loads the dialogue scenarios and personality trait mapping information to be used.

[0712] Step 2:

[0713] The user accesses the system's entry point using a terminal and enters the necessary registration information (name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0714] Step 3:

[0715] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[0716] Step 4:

[0717] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[0718] Step 5:

[0719] The server analyzes the user's responses received using natural language processing technology and an emotion engine. The emotion engine recognizes the emotions in the responses and evaluates their impact on the estimation of personality traits.

[0720] Step 6:

[0721] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the server generates the next question, "How do you relieve stress?" The device displays the generated question to the user.

[0722] Step 7:

[0723] The user answers a new question using the device. The device sends the answer to the server. The server analyzes it again, and the emotion engine recognizes the emotion. This process is repeated until the necessary personality trait data is accumulated.

[0724] Step 8:

[0725] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[0726] Step 9:

[0727] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[0728] As a concrete example, consider a case where a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress from this answer. Next, the server generates a question, "How do you respond when a deadline is approaching?" and displays it to the user. The user answers, "I exercise to relieve stress," and the emotion engine recognizes a positive emotion from this answer. Based on this information, the server updates the user's personality trait data.

[0729] This series of processes allows for a detailed understanding of the user's personality traits and enables highly accurate personality diagnosis.

[0730] Example 2

[0731] 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."

[0732] Conventional personality testing systems have a problem with low accuracy of personality diagnosis results because the question generation and answer analysis are fixed and do not fully take into account the user's emotional state. Furthermore, they simply ask many questions one-sidedly and are unable to respond flexibly to the user's real-time emotional changes. This tends to create a stressful experience for users and result in low-quality information.

[0733] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0734] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving answers from a user via a terminal, means for analyzing the user's answer using natural language processing technology, means for recognizing the user's emotion using an emotion engine and correcting the analysis result based on the emotion, means for generating a next question based on the estimated personality traits, and means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers. This enables a highly accurate personality diagnosis that takes the user's emotional state into consideration.

[0735] "Generative AI" is an AI technology used to generate questions and dialogue.

[0736] A "terminal" is a device that has an interface for users to input data, and specifically includes personal computers and smartphones.

[0737] A "user" is a person who accesses the system and enters information or answers questions.

[0738] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and infers intent.

[0739] An "emotion engine" is a technology that recognizes emotions from user input and corrects the results of information analysis based on those emotions.

[0740] "Analysis results" are the analysis results of user responses obtained using natural language processing technology and an emotion engine.

[0741] "Personality traits" are characteristics that indicate the user's personality and behavioral patterns, and are estimated based on the analysis results.

[0742] A "question" is a question created by generative artificial intelligence and presented to the user.

[0743] "Personality test results" are detailed information about the user's personality that is created based on repeated question and answer exchanges.

[0744] This invention relates to a personality testing system using generative artificial intelligence and an emotion engine. To implement the system, the following elements are combined and operated:

[0745] System Components

[0746] The system mainly consists of the following hardware and software elements:

[0747] 1. Generative AI module: Software for generating questions using generative AI. Specifically, ChatGPT is used.

[0748] 2. Emotion engine: Software that recognizes emotions from user responses and corrects the analysis results.

[0749] 3. Terminal: An interface through which users input information, including computers, smartphones, etc.

[0750] 4. Server: This is the hardware that generates questions, receives and analyzes answers, generates the next questions, and creates the final diagnosis results.

[0751] 5. Natural Language Processing (NLP) module: Software with technology for analyzing user responses.

[0752] 6. Database: A storage for saving user response data and personality trait information.

[0753] System Operation

[0754] The overall processing of this system will be explained below.

[0755] Initial Setup

[0756] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. It also loads the dialogue scenarios and personality trait mapping information to be used.

[0757] Starting the personality test

[0758] Users access the system using a terminal and enter the necessary registration information (name, age, desired occupation, etc.), which is then sent from the terminal to the server.

[0759] First question generation

[0760] The server selects an appropriate dialogue scenario based on the user's registered information and calls the ChatGPT API to generate the initial question, for example, "What is the most stressful situation for you?"

[0761] Question and answer exchange

[0762] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which is then sent from the terminal to the server.

[0763] Answer analysis and emotion recognition

[0764] The server uses natural language processing technology and an emotion engine to analyze the user's responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. Based on the results, the user's personality traits are estimated. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[0765] Next question generation

[0766] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes high stress, it generates the question "How do you relieve stress?"

[0767] Iteration

[0768] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[0769] Generating personality test results

[0770] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0771] Specific examples

[0772] For example, if a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress, it will then generate the next question: "How do you respond when a deadline is approaching?" If the user answers "I exercise to relieve stress," the emotion engine will recognize the positive emotion in that answer, and the server will update the personality traits based on that data.

[0773] Prompt Sentence Examples

[0774] 1. First question generation:

[0775] "The user's name is Taro Yamada, he is 30 years old, and he wants to be an engineer. Please generate the following question as the first question for the user: 'What is the most stressful situation for you?'"

[0776] 2. Generate the following question:

[0777] "The user answers, 'When I have to accomplish many tasks in a short amount of time,' and the emotion engine recognizes this as a high level of stress. Next, generate a question that asks specific ways to deal with stress: 'How do you relieve stress?'"

[0778] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0779] Step 1: Initial Setup

[0780] The server initializes all modules and loads the necessary information.

[0781] Specific behavior:

[0782] The server reads the initialization file.

[0783] The server initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine.

[0784] The server loads the dialogue scenario and personality trait mapping information.

[0785] Input: Initial setting file, dialogue scenario information, personality trait mapping information

[0786] Output: Initialized modules, loaded information

[0787] Step 2: Enter user registration information

[0788] A user accesses the system using a terminal and enters the required information.

[0789] Specific behavior:

[0790] The user accesses the system's registration page using a terminal.

[0791] The user enters personal information such as name, age, and desired job type.

[0792] The terminal transmits the user's information to the server.

[0793] Input: User registration information (name, age, desired job type, etc.)

[0794] Output: Registration information sent to the server

[0795] Step 3: Generate your first question

[0796] The server generates an initial question based on the user's registration information.

[0797] Specific behavior:

[0798] The server receives the user's registration information.

[0799] The server selects the appropriate dialogue scenario.

[0800] The server calls the API of generative artificial intelligence (ChatGPT) to generate the initial question.

[0801] Input: User registration information

[0802] Output: Initial question (e.g., "What is the most stressful situation for you?")

[0803] Step 4: Question and answer exchange

[0804] The generated question is presented to the user and an answer is received from the user.

[0805] Specific behavior:

[0806] The server generates a question and sends it to the terminal.

[0807] The terminal displays the question to the user.

[0808] The user enters an answer to the question.

[0809] The terminal sends the user's answer to the server.

[0810] Input: Initial question, user answer

[0811] Output: The user's response received

[0812] Step 5: Response analysis and emotion recognition

[0813] The server analyzes the user's responses and recognizes emotions using an emotion engine.

[0814] Specific behavior:

[0815] The server analyzes the received user responses using a natural language processing (NLP) module.

[0816] The analysis includes sentiment analysis, intent estimation, and keyword extraction.

[0817] The server uses an emotion engine to recognize the user's emotion.

[0818] The server estimates the user's personality traits based on the analysis results.

[0819] Input: User's answer

[0820] Output: Analysis results (sentiment analysis, intent estimation, keyword extraction), recognized emotions

[0821] Step 6: Generate the next question

[0822] The server generates the next question based on the analysis results and emotion recognition results.

[0823] Specific behavior:

[0824] The server integrates the analysis results with the output of the emotion engine.

[0825] The server calls the API of generative artificial intelligence (ChatGPT) to generate the next question.

[0826] The server sends the following question to the terminal:

[0827] Input: Analysis results, emotion recognition results

[0828] Output: Next question (e.g., "How do you relieve stress?")

[0829] Step 7: Iterate

[0830] This process is repeated multiple times to gather detailed information about the user's personality traits.

[0831] Specific behavior:

[0832] Repeat process steps 4 to 6.

[0833] Each response and analysis result is stored in a database.

[0834] The server uses an emotion engine to track the user's emotional changes.

[0835] The server adaptively adjusts questions based on emotional changes.

[0836] Input: User answers, analysis results, emotion recognition results

[0837] Output: Updated personality trait database, adaptively adjusted questions

[0838] Step 8: Generate personality results

[0839] Once the server has collected enough information, it will generate a final personality test result.

[0840] Specific behavior:

[0841] The server determines whether it has gathered enough information.

[0842] The server generates the final personality test results.

[0843] The results generated by the server are compiled in a report format.

[0844] The server feeds back the final report to the user.

[0845] Input: Collected personality traits database

[0846] Output: Personality test results, feedback in report format

[0847] (Application example 2)

[0848] 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."

[0849] Conventional personality diagnosis systems only analyze a user's personality traits, but the ways in which the results can be utilized are limited. Furthermore, it is difficult to propose products that meet the individual needs of users in virtual stores. Therefore, there is a need for a method that can more precisely analyze a user's personality traits and link them to product proposals.

[0850] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving an answer from the user via the terminal, means for analyzing the user's answer using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers, and means for making product suggestions based on the user's personality in a virtual store. This makes it possible to grasp the user's personality traits in detail and to suggest individual products based on them.

[0851] "Generative AI" is an AI technology that can automatically generate text based on user input.

[0852] The "initial question" is the question that is presented first when starting a personality test for the user.

[0853] A "terminal" is an electronic device that allows a user to input information, and includes smartphones, personal computers, and the like.

[0854] "User responses" are text data and other input information provided by the user.

[0855] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[0856] "Analyzing user responses" is the process of interpreting the information provided by the user and identifying various characteristics based on that information.

[0857] "Personality traits" are information about a user's emotions, behavioral patterns, and individual characteristics.

[0858] "Generating the next question" is the process of generating a new question based on the user's answer and the analysis results.

[0859] "Personality diagnosis result" refers to a comprehensive result regarding the user's personality that is generated based on information collected through dialogue.

[0860] A "virtual store" is a virtual store that does not exist in a physical location and provides products and services over the Internet.

[0861] "Product suggestion" is the process of recommending specific products or services based on a user's needs and characteristics.

[0862] This invention relates to a personalized shopping assistant in a virtual store using generative artificial intelligence and an emotion engine. This system can analyze the user's personality traits in detail and make personalized product recommendations based on the results.

[0863] System configuration

[0864] The system mainly consists of the following elements:

[0865] 1. Generative AI: Responsible for generating initial and follow-up questions.

[0866] 2. Terminal: A device such as a smartphone or smart glasses that allows the user to input information and displays results.

[0867] 3. Natural language processing technology: Used to analyze user responses and infer personality traits.

[0868] 4. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[0869] 5. Server: Generates questions, receives and analyzes answers, and generates and stores personality test results.

[0870] 6. Database: Stores user response data and personality trait information.

[0871] System Operation Overview

[0872] 1. Initial Setup:

[0873] The server loads the initial configuration file and initializes the generative artificial intelligence (e.g. ChatGPT), natural language processing (NLP) module, and emotion engine. Dialogue scenarios and personality trait mapping information are also loaded at this time.

[0874] 2. Start the personality test:

[0875] Users access the system using a device such as a smartphone or smart glasses and enter the necessary registration information (such as name, age, desired product, etc.), which is then sent to the server.

[0876] 3. First question generation:

[0877] The server calls ChatGPT's API based on the user's registration information to generate an initial question, such as "What is the most stressful situation for you?"

[0878] 4. Question and answer exchange:

[0879] The generated question is sent to the terminal and displayed to the user, the user enters an answer, and the terminal sends the answer to the server.

[0880] 5. Answer analysis and emotion recognition:

[0881] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and based on the results, it infers the user's personality traits.

[0882] 6. Generate the following question:

[0883] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, it will generate the next question, asking for specific countermeasures, such as "How do you relieve stress?"

[0884] 7. Generating personality test results:

[0885] Through repeated processing, the system collects detailed information about the user's personality. The final personality assessment results are generated by the server and fed back to the user in the form of a report. This information is also used to make personalized product recommendations in the virtual store.

[0886] Specific examples

[0887] For example, if a user answers, "I feel stressed when a work deadline is approaching," the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits are understood in detail and used to suggest products in the virtual store.

[0888] Example prompt sentence:

[0889] Generate the next question based on the user's answers and sentiment.

[0890] Answer: "I feel stressed when I'm facing deadlines at work."

[0891] Emotion: "High stress"

[0892] Using this prompt, the generative AI model generates questions to drive the next dialogue, a process that enables personalized shopping based on the user's personality traits.

[0893] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0894] Step 1:

[0895] The server reads the initial setting file and initializes the generative artificial intelligence, natural language processing module, and emotion engine. This loads the dialogue scenario and personality trait mapping information. The input used in this step is the initial setting file, and the output is the initialized generative artificial intelligence, natural language processing module, and emotion engine.

[0896] Step 2:

[0897] A user accesses the system using a terminal and enters the necessary registration information, such as name, age, desired product, etc. The terminal then transmits this information to the server. The input here is the registration information entered by the user into the terminal, and the output is the registration information transmitted to the server.

[0898] Step 3:

[0899] The server calls the API of the generative artificial intelligence based on the received user registration information to generate the initial question. For example, the question might be, "What situation causes you the most stress?" The input is the user registration information, and the output is the generated initial question.

[0900] Step 4:

[0901] The generated initial question is sent from the server to the terminal and displayed to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the question displayed on the terminal and the user's answer sent to the server.

[0902] Step 5:

[0903] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the user's personality traits as an analysis result.

[0904] Step 6:

[0905] The server calls the generative artificial intelligence API again based on the analysis results and the output of the emotion engine to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the next question generated is, "How do you relieve stress?" The input is the analysis results and the output of the emotion engine, and the output is the generated next question.

[0906] Step 7:

[0907] The server sends the next question to the device, and the user enters an answer, which is then sent back to the server. This process is repeated to gather more information about the user's personality. The input is the next question and the user's additional answer, and the output is a new question based on the additional answer and the analysis results.

[0908] Step 8:

[0909] Once enough information has been collected, the server generates the final personality assessment results. These results are created in the form of a report and sent to the terminal. This information is also used to make personalized product recommendations within the virtual store. The input is the collected personality trait information, and the output is a report of the personality assessment results and product recommendations.

[0910] 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.

[0911] 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.

[0912] 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.

[0913] [Third embodiment]

[0914] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0915] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0916] 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).

[0917] 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.

[0918] 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.

[0919] 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).

[0920] 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.

[0921] 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.

[0922] 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.

[0923] 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.

[0924] 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.

[0925] 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."

[0926] This invention relates to a highly accurate personality testing system using generative artificial intelligence. This system analyzes the user's personality traits in detail through natural dialogue with the user, improving the reliability of personality tests in employment examinations.

[0927] System Overview

[0928] The system mainly consists of the following elements:

[0929] 1. Generative AI module: Questions are generated using generative AI such as ChatGPT.

[0930] 2. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[0931] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0932] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[0933] 5. Database: Stores user response data and personality trait information.

[0934] System Operation

[0935] The processing of the system will be explained in natural language below.

[0936] 1. Initial Setup

[0937] The server reads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[0938] 2. Start the personality test

[0939] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[0940] 3. First question generation

[0941] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[0942] 4. Question and answer exchanges

[0943] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[0944] 5. Analysis of responses

[0945] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This makes it possible to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[0946] 6. Next Question Generation

[0947] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[0948] 7. Iteration

[0949] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[0950] 8. Generating personality test results

[0951] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[0952] Specific examples

[0953] For example, if a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[0954] The above is an embodiment of the present invention, which goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring of personnel.

[0955] The processing flow will be explained below.

[0956] Step 1:

[0957] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[0958] Step 2:

[0959] A user accesses the system's entry point using a terminal and enters necessary information such as name, age, desired occupation, etc. into the registration form. The terminal then sends this registration information to the server.

[0960] Step 3:

[0961] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[0962] Step 4:

[0963] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[0964] Step 5:

[0965] The server analyzes the user's responses using natural language processing technology, including sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results.

[0966] Step 6:

[0967] The server then calls the ChatGPT API again to generate the next question based on the estimated personality traits, and sends the generated next question to the device, which then displays it to the user.

[0968] Step 7:

[0969] The user answers new questions using the device. The device sends the answers to the server. Steps 5 and 6 are repeated to accumulate the necessary personality trait data.

[0970] Step 8:

[0971] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[0972] Step 9:

[0973] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[0974] This is the specific processing flow of the personality testing system that utilizes generative AI. This process enables detailed and accurate analysis of the user's personality traits and supports appropriate hiring decisions.

[0975] Example 1

[0976] 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."

[0977] Conventional personality tests rely on limited question and answer patterns, making it difficult to grasp the user's personality traits in detail. Furthermore, the exchange of questions and answers is mechanical, lacking naturalness and reliability. As a result, accuracy is insufficient for selecting the right candidates, especially in job interviews.

[0978] 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.

[0979] In this invention, the server includes means for generating initial questions using generative artificial intelligence, means for receiving registration information and answers from the user via the terminal, and means for analyzing the user's answers using natural language processing technology. This allows for detailed analysis of the user's personality traits while maintaining the naturalness of the dialogue, enabling highly accurate personality diagnosis.

[0980] "Generative AI" is an AI system that uses natural language processing technology to generate text and dialogue.

[0981] A "terminal" is a device that allows a user to access a system and input or receive information. Examples of such devices include personal computers, smartphones, and tablets.

[0982] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language. This technology includes sentiment analysis, intent estimation, and keyword extraction.

[0983] "Analysis results" are data and information obtained from users' responses using natural language processing technology.

[0984] "Personality traits" is a concept that refers to specific characteristics and tendencies regarding a user's behavior, reactions, and emotions.

[0985] A "dialogue scenario" is a set of questions and answers designed to achieve a specific purpose.

[0986] A "database" is a system for storing, managing, and retrieving data in an organized and efficient manner.

[0987] The "personality assessment results" are reports that detail the user's personality traits, generated based on the user's answers and the analysis results.

[0988] This invention relates to a highly accurate personality testing system using generative artificial intelligence. The system aims to improve the reliability of personality tests in employment examinations by analyzing the user's personality traits in detail through natural dialogue with the user.

[0989] System Configuration

[0990] The system of the present invention includes the following elements:

[0991] 1. Generative AI module: This is a module that generates questions using a generative AI model, for example, using ChatGPT's API.

[0992] 2. Device: The interface through which the user provides input (e.g., computer, smartphone, tablet).

[0993] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[0994] 4. Natural Language Processing (NLP) module: This is a technology for analyzing user responses. Specifically, it performs sentiment analysis, intent estimation, keyword extraction, etc.

[0995] 5. Database: Stores user response data and personality trait information.

[0996] System Operation

[0997] Initial Setup

[0998] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[0999] Starting the personality test

[1000] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1001] First question generation

[1002] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question, such as, "What is the most stressful situation for you?"

[1003] Question and answer exchange

[1004] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which then receives the user's answer.

[1005] Analysis of responses

[1006] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This allows the server to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[1007] Next question generation

[1008] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[1009] Iteration

[1010] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[1011] Generating personality test results

[1012] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1013] Specific examples

[1014] If a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[1015] Prompt Sentence Examples

[1016] Below are some example prompts to be input to the generative AI model:

[1017] "What situations cause you the most stress?"

[1018] "How do you respond when you have to complete many tasks in a short amount of time?"

[1019] "How do you relieve stress?"

[1020] In this way, by combining question generation using generative AI with answer analysis using natural language processing technology, the system can analyze users' personality traits with high accuracy. This system goes beyond the limits of conventional personality tests, enabling more accurate analysis of personality traits and the recruitment of appropriate personnel.

[1021] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1022] Step 1: Initial Setup

[1023] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules. At this time, it also loads the dialogue scenarios to be used and mapping information for personality traits. The input is the configuration file, and the output is the initialized AI and NLP modules. Specifically, it uses a Python library to load the API key and model.

[1024] Step 2: Enter your registration information

[1025] A user accesses the system using a terminal and enters registration information such as name, age, desired occupation, etc. The terminal sends this information to the server. The input is the registration information entered by the user into the terminal, and the output is the user information sent to the server. Specifically, information is collected using a web form and sent to the server via an HTTP request.

[1026] Step 3: Generate your first question

[1027] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question. The input is the user's registered information, and the output is the generated initial question. Specifically, the server calls the ChatGPT API to generate a question using a specific prompt, such as "What is the most stressful situation for you?"

[1028] Step 4: Display the question and enter the answer

[1029] The terminal displays the initial question received from the server to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the user's answer sent to the server. Specifically, this involves displaying a question on a web page, allowing the user to enter an answer in a text box and press the submit button.

[1030] Step 5: Analyze the answers

[1031] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the analyzed personality traits. Specifically, it uses an NLP library to perform sentiment analysis (e.g., calculating sentiment scores) and keyword extraction (e.g., tokenization and stop word removal).

[1032] Step 6: Generate the next question

[1033] The server generates the next question based on the analysis result. For example, if the user answers "When I have to complete many tasks in a short period of time," the next question generated would be "How do you relieve stress?" The input is the analysis result, and the output is the next question. Specifically, the server calls the ChatGPT API again and generates a new question using the appropriate prompt.

[1034] Step 7: Iterate

[1035] The server repeatedly exchanges questions and answers to collect detailed information about the user's personality. The input is each user's answer and its analysis results, and the output is the accumulated analysis results. Each answer and analysis result is stored in a database. Specifically, the system includes a loop process in which, after each answer is received and analyzed, the next question is generated and sent to the device.

[1036] Step 8: Generate personality results

[1037] Once enough necessary information has been collected, the server generates the final personality assessment results. The results are created in report format and fed back to the user via the terminal. The input is the accumulated analysis results, and the output is a report of the personality assessment results that is fed back to the user. Specifically, the various analysis results are integrated to generate a report of the evaluation of each of the user's personality traits, and the report is provided to the user in a format such as PDF.

[1038] The above is the specific flow of the program processing of this system. Each step processes and calculates data based on the input, and obtains the desired output, thereby achieving an efficient and highly accurate personality test.

[1039] (Application example 1)

[1040] 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."

[1041] Conventional personality testing systems rely on the subjective judgment of human interviewers and often lack reliability and consistency. Furthermore, they lack the means to analyze the personality traits of individual employees in detail to improve stress management and work efficiency. This makes it difficult to assign appropriate work and provide individual support, potentially reducing production efficiency within the factory.

[1042] 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.

[1043] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving a response from a user via a terminal, means for analyzing the user's response using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's responses, means for analyzing the user's personality traits and stress level and suggesting appropriate task assignments, and means for providing actions and support to help workers relax when they are feeling stressed. This makes it possible to analyze the personality traits of employees in detail and achieve accurate task assignments and stress management.

[1044] "Generative AI" is AI that has the ability to generate questions through dialogue with the user.

[1045] A "terminal" is an interface device that allows a user to input information. Examples include personal computers and smartphones.

[1046] "Natural language processing technology" is a technology for handling, analyzing, and processing human language.

[1047] "User's answer" is text data provided by the user in response to a question posed by the generative artificial intelligence.

[1048] "Personality traits" are individual personality characteristics or features that are estimated by analyzing the user's responses.

[1049] The "personality diagnosis result" is a detailed diagnosis report generated based on the user's personality traits.

[1050] The "stress level" is an index that indicates the degree of stress that the user is feeling.

[1051] "Work assignment" is the assignment of a person or machine to perform a specific task.

[1052] "Actions" are specific movements or behaviors taken to manage or support stress.

[1053] "Support" refers to advice and help provided to users when they are stressed.

[1054] This invention relates to a personality trait analysis system aimed at improving work efficiency in factories and managing worker stress. This system analyzes personality traits through dialogue with users using generative artificial intelligence and natural language processing technology.

[1055] The system consists of the following elements:

[1056] 1. Generative AI module: Used to generate questions.

[1057] 2. Terminal: An interface device through which a user makes input (e.g., a computer or smartphone).

[1058] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[1059] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[1060] 5. Database: Stores user response data and personality trait information.

[1061] System Operation Overview

[1062] The main processing steps of this system are shown below.

[1063] Hardware and Software Use

[1064] Hardware: Robot terminals and servers in factories

[1065] Software: Generative AI (e.g., OpenAI API), natural language processing module, database module

[1066] Data processing and calculation

[1067] 1. Question generation:

[1068] The server invokes a generative artificial intelligence to generate questions based on appropriate prompts, with the initial prompt being "What was the most stressful thing about work this week?"

[1069] 2. Receiving user responses:

[1070] The user answers the generated questions through the terminal. For example, if the user answers "There was a lot of work, and I was worried about whether I would be able to meet the deadline," the answer is sent from the terminal to the server.

[1071] 3. Analysis of answers:

[1072] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction, to estimate the user's personality traits and stress level.

[1073] 4. Stress and Work Management:

[1074] Based on the analysis results, the system identifies the user's personality traits and stress level, suggests appropriate task assignments, and provides support and actions to help them relax if they are under a high level of stress.

[1075] 5. Data storage:

[1076] Each response and analysis result is stored in a database and used for the next diagnosis and to improve work efficiency.

[1077] Specific examples

[1078] For example, if a user answers the question "What was the most stressful thing about work this week?" with "The workload was heavy and I was worried about whether I would be able to meet the deadline," the server will use this answer to analyze the personality trait of "stress response to deadlines." The next question will then be generated: "When you have a lot of work and a deadline looming, how do you deal with it?" Through this series of conversations, the user's personality traits and stress level are understood in detail.

[1079] This allows for work arrangements that are optimized for the user's characteristics and specific support for stress reduction.

[1080] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1081] Specific processing flow of the system program that realizes the application example

[1082] Step 1: Initial Setup

[1083] The server initializes the generative artificial intelligence module and the natural language processing (NLP) module. The server loads the dialogue scenario and personality trait mapping information.

[1084] Input: Initialization file

[1085] Data processing / calculation: Loading dialogue scenarios and personality trait information into system memory

[1086] Output: Initialized generative AI and NLP modules

[1087] Step 2: Start the personality test

[1088] Users access the system using a terminal and enter the necessary registration information (e.g., name, age, desired occupation), which is then sent to the server.

[1089] Input: User registration information

[1090] Data processing / calculation: Collection of user input data and transmission to the server

[1091] Output: User registration information stored on the server

[1092] Step 3: Generate your first question

[1093] The server selects an appropriate dialogue scenario based on the user's registration information and calls the generative artificial intelligence API to generate the initial question.

[1094] Input: User registration information and interaction scenario

[1095] Data processing / calculation: Generative AI API is used to generate appropriate questions

[1096] Output: The initial question generated

[1097] Step 4: Question and answer exchange

[1098] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which the terminal then sends to the server.

[1099] Input: Generated questions and user answers

[1100] Data processing / calculation: Receive user answers in text format and send them to the server

[1101] Output: User answers stored on the server

[1102] Step 5: Analyze the answers

[1103] The server uses NLP technology to analyze the user's responses, including sentiment analysis, intent estimation, and keyword extraction, to assess the user's personality traits and stress level.

[1104] Input: User's answer

[1105] Data processing / calculation: Using NLP technology to analyze emotions and infer intentions, and calculate personality traits

[1106] Output: User's personality traits and stress level as analysis results

[1107] Step 6: Generate the next question

[1108] The server generates the next question based on the analysis results, such as "When you have a lot of work to do and a deadline looming, how do you handle it?"

[1109] Input: Personality traits as analysis results and previous answers

[1110] Data processing / calculation: Calling a generative AI API to generate the next appropriate question

[1111] Output: Next question to display

[1112] Step 7: Iterate

[1113] The process from step 4 to step 6 is repeated to collect detailed information about the user's personality traits. Each response and analysis result is stored in a database.

[1114] Input: Previous conversation history and analysis results

[1115] Data processing / calculation: Repeating the interactive process and storing the data in a database

[1116] Output: Detailed personality trait data

[1117] Step 8: Generate personality results

[1118] Once enough information is collected, the server generates a final personality test result, which is then presented in the form of a report and fed back to the user.

[1119] Input: Accumulated personality trait data

[1120] Data processing / calculation: Generating personality test results and creating reports

[1121] Output: Personality test results in report format and feedback to the user

[1122] 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.

[1123] This invention relates to a personality testing system that uses generative artificial intelligence and an emotion engine. This system analyzes the personality traits of users in detail through dialogue with them and provides highly reliable personality diagnosis.

[1124] System Overview

[1125] The system mainly consists of the following elements:

[1126] 1. Generative artificial intelligence module: Generates questions using generative AI.

[1127] 2. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[1128] 3. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[1129] 4. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[1130] 5. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[1131] 6. Database: Stores user response data and personality trait information.

[1132] System Operation

[1133] The processing of the system will be explained in natural language below.

[1134] 1. Initial Setup

[1135] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[1136] 2. Start the personality test

[1137] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1138] 3. First question generation

[1139] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[1140] 4. Question and answer exchanges

[1141] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[1142] 5. Answer Analysis and Emotion Recognition

[1143] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[1144] 6. Next Question Generation

[1145] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, the server generates a next question asking for specific countermeasures, such as "How do you relieve stress?"

[1146] 7. Iteration

[1147] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[1148] 8. Generating personality test results

[1149] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1150] Specific examples

[1151] For example, suppose a user answers, "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits can be understood in detail.

[1152] The above is an embodiment of the present invention. This goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring by combining generative artificial intelligence and an emotion engine.

[1153] The processing flow will be explained below.

[1154] Step 1:

[1155] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. At this stage, it also loads the dialogue scenarios and personality trait mapping information to be used.

[1156] Step 2:

[1157] The user accesses the system's entry point using a terminal and enters the necessary registration information (name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1158] Step 3:

[1159] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[1160] Step 4:

[1161] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[1162] Step 5:

[1163] The server analyzes the user's responses received using natural language processing technology and an emotion engine. The emotion engine recognizes the emotions in the responses and evaluates their impact on the estimation of personality traits.

[1164] Step 6:

[1165] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the server generates the next question, "How do you relieve stress?" The device displays the generated question to the user.

[1166] Step 7:

[1167] The user answers a new question using the device. The device sends the answer to the server. The server analyzes it again, and the emotion engine recognizes the emotion. This process is repeated until the necessary personality trait data is accumulated.

[1168] Step 8:

[1169] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[1170] Step 9:

[1171] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[1172] As a concrete example, consider a case where a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress from this answer. Next, the server generates a question, "How do you respond when a deadline is approaching?" and displays it to the user. The user answers, "I exercise to relieve stress," and the emotion engine recognizes a positive emotion from this answer. Based on this information, the server updates the user's personality trait data.

[1173] This series of processes allows for a detailed understanding of the user's personality traits and enables highly accurate personality diagnosis.

[1174] Example 2

[1175] 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."

[1176] Conventional personality testing systems have a problem with low accuracy of personality diagnosis results because the question generation and answer analysis are fixed and do not fully take into account the user's emotional state. Furthermore, they simply ask many questions one-sidedly and are unable to respond flexibly to the user's real-time emotional changes. This tends to create a stressful experience for users and result in low-quality information.

[1177] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1178] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving answers from a user via a terminal, means for analyzing the user's answer using natural language processing technology, means for recognizing the user's emotion using an emotion engine and correcting the analysis result based on the emotion, means for generating a next question based on the estimated personality traits, and means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers. This enables a highly accurate personality diagnosis that takes the user's emotional state into consideration.

[1179] "Generative AI" is an AI technology used to generate questions and dialogue.

[1180] A "terminal" is a device that has an interface for users to input data, and specifically includes personal computers and smartphones.

[1181] A "user" is a person who accesses the system and enters information or answers questions.

[1182] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and infers intent.

[1183] An "emotion engine" is a technology that recognizes emotions from user input and corrects the results of information analysis based on those emotions.

[1184] "Analysis results" are the analysis results of user responses obtained using natural language processing technology and an emotion engine.

[1185] "Personality traits" are characteristics that indicate the user's personality and behavioral patterns, and are estimated based on the analysis results.

[1186] A "question" is a question created by generative artificial intelligence and presented to the user.

[1187] "Personality test results" are detailed information about the user's personality that is created based on repeated question and answer exchanges.

[1188] This invention relates to a personality testing system using generative artificial intelligence and an emotion engine. To implement the system, the following elements are combined and operated:

[1189] System Components

[1190] The system mainly consists of the following hardware and software elements:

[1191] 1. Generative AI module: Software for generating questions using generative AI. Specifically, ChatGPT is used.

[1192] 2. Emotion engine: Software that recognizes emotions from user responses and corrects the analysis results.

[1193] 3. Terminal: An interface through which users input information, including computers, smartphones, etc.

[1194] 4. Server: This is the hardware that generates questions, receives and analyzes answers, generates the next questions, and creates the final diagnosis results.

[1195] 5. Natural Language Processing (NLP) module: Software with technology for analyzing user responses.

[1196] 6. Database: A storage for saving user response data and personality trait information.

[1197] System Operation

[1198] The overall processing of this system will be explained below.

[1199] Initial Setup

[1200] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. It also loads the dialogue scenarios and personality trait mapping information to be used.

[1201] Starting the personality test

[1202] Users access the system using a terminal and enter the necessary registration information (name, age, desired occupation, etc.), which is then sent from the terminal to the server.

[1203] First question generation

[1204] The server selects an appropriate dialogue scenario based on the user's registered information and calls the ChatGPT API to generate the initial question, for example, "What is the most stressful situation for you?"

[1205] Question and answer exchange

[1206] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which is then sent from the terminal to the server.

[1207] Answer analysis and emotion recognition

[1208] The server uses natural language processing technology and an emotion engine to analyze the user's responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. Based on the results, the user's personality traits are estimated. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[1209] Next question generation

[1210] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes high stress, it generates the question "How do you relieve stress?"

[1211] Iteration

[1212] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[1213] Generating personality test results

[1214] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1215] Specific examples

[1216] For example, if a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress, it will then generate the next question: "How do you respond when a deadline is approaching?" If the user answers "I exercise to relieve stress," the emotion engine will recognize the positive emotion in that answer, and the server will update the personality traits based on that data.

[1217] Prompt Sentence Examples

[1218] 1. First question generation:

[1219] "The user's name is Taro Yamada, he is 30 years old, and he wants to be an engineer. Please generate the following question as the first question for the user: 'What is the most stressful situation for you?'"

[1220] 2. Generate the following question:

[1221] "The user answers, 'When I have to accomplish many tasks in a short amount of time,' and the emotion engine recognizes this as a high level of stress. Next, generate a question that asks specific ways to deal with stress: 'How do you relieve stress?'"

[1222] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1223] Step 1: Initial Setup

[1224] The server initializes all modules and loads the necessary information.

[1225] Specific behavior:

[1226] The server reads the initialization file.

[1227] The server initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine.

[1228] The server loads the dialogue scenario and personality trait mapping information.

[1229] Input: Initial setting file, dialogue scenario information, personality trait mapping information

[1230] Output: Initialized modules, loaded information

[1231] Step 2: Enter user registration information

[1232] A user accesses the system using a terminal and enters the required information.

[1233] Specific behavior:

[1234] The user accesses the system's registration page using a terminal.

[1235] The user enters personal information such as name, age, and desired job type.

[1236] The terminal transmits the user's information to the server.

[1237] Input: User registration information (name, age, desired job type, etc.)

[1238] Output: Registration information sent to the server

[1239] Step 3: Generate your first question

[1240] The server generates an initial question based on the user's registration information.

[1241] Specific behavior:

[1242] The server receives the user's registration information.

[1243] The server selects the appropriate dialogue scenario.

[1244] The server calls the API of generative artificial intelligence (ChatGPT) to generate the initial question.

[1245] Input: User registration information

[1246] Output: Initial question (e.g., "What is the most stressful situation for you?")

[1247] Step 4: Question and answer exchange

[1248] The generated question is presented to the user and an answer is received from the user.

[1249] Specific behavior:

[1250] The server generates a question and sends it to the terminal.

[1251] The terminal displays the question to the user.

[1252] The user enters an answer to the question.

[1253] The terminal sends the user's answer to the server.

[1254] Input: Initial question, user answer

[1255] Output: The user's response received

[1256] Step 5: Response analysis and emotion recognition

[1257] The server analyzes the user's responses and recognizes emotions using an emotion engine.

[1258] Specific behavior:

[1259] The server analyzes the received user responses using a natural language processing (NLP) module.

[1260] The analysis includes sentiment analysis, intent estimation, and keyword extraction.

[1261] The server uses an emotion engine to recognize the user's emotion.

[1262] The server estimates the user's personality traits based on the analysis results.

[1263] Input: User's answer

[1264] Output: Analysis results (sentiment analysis, intent estimation, keyword extraction), recognized emotions

[1265] Step 6: Generate the next question

[1266] The server generates the next question based on the analysis results and emotion recognition results.

[1267] Specific behavior:

[1268] The server integrates the analysis results with the output of the emotion engine.

[1269] The server calls the API of generative artificial intelligence (ChatGPT) to generate the next question.

[1270] The server sends the following question to the terminal:

[1271] Input: Analysis results, emotion recognition results

[1272] Output: Next question (e.g., "How do you relieve stress?")

[1273] Step 7: Iterate

[1274] This process is repeated multiple times to gather detailed information about the user's personality traits.

[1275] Specific behavior:

[1276] Repeat process steps 4 to 6.

[1277] Each response and analysis result is stored in a database.

[1278] The server uses an emotion engine to track the user's emotional changes.

[1279] The server adaptively adjusts questions based on emotional changes.

[1280] Input: User answers, analysis results, emotion recognition results

[1281] Output: Updated personality trait database, adaptively adjusted questions

[1282] Step 8: Generate personality results

[1283] Once the server has collected enough information, it will generate a final personality test result.

[1284] Specific behavior:

[1285] The server determines whether it has gathered enough information.

[1286] The server generates the final personality test results.

[1287] The results generated by the server are compiled in a report format.

[1288] The server feeds back the final report to the user.

[1289] Input: Collected personality traits database

[1290] Output: Personality test results, feedback in report format

[1291] (Application example 2)

[1292] 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."

[1293] Conventional personality diagnosis systems only analyze a user's personality traits, but the ways in which the results can be utilized are limited. Furthermore, it is difficult to propose products that meet the individual needs of users in virtual stores. Therefore, there is a need for a method that can more precisely analyze a user's personality traits and link them to product proposals.

[1294] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving an answer from the user via the terminal, means for analyzing the user's answer using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers, and means for making product suggestions based on the user's personality in a virtual store. This makes it possible to grasp the user's personality traits in detail and to suggest individual products based on them.

[1295] "Generative AI" is an AI technology that can automatically generate text based on user input.

[1296] The "initial question" is the question that is presented first when starting a personality test for the user.

[1297] A "terminal" is an electronic device that allows a user to input information, and includes smartphones, personal computers, and the like.

[1298] "User responses" are text data and other input information provided by the user.

[1299] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1300] "Analyzing user responses" is the process of interpreting the information provided by the user and identifying various characteristics based on that information.

[1301] "Personality traits" are information about a user's emotions, behavioral patterns, and individual characteristics.

[1302] "Generating the next question" is the process of generating a new question based on the user's answer and the analysis results.

[1303] "Personality diagnosis result" refers to a comprehensive result regarding the user's personality that is generated based on information collected through dialogue.

[1304] A "virtual store" is a virtual store that does not exist in a physical location and provides products and services over the Internet.

[1305] "Product suggestion" is the process of recommending specific products or services based on a user's needs and characteristics.

[1306] This invention relates to a personalized shopping assistant in a virtual store using generative artificial intelligence and an emotion engine. This system can analyze the user's personality traits in detail and make personalized product recommendations based on the results.

[1307] System configuration

[1308] The system mainly consists of the following elements:

[1309] 1. Generative AI: Responsible for generating initial and follow-up questions.

[1310] 2. Terminal: A device such as a smartphone or smart glasses that allows the user to input information and displays results.

[1311] 3. Natural language processing technology: Used to analyze user responses and infer personality traits.

[1312] 4. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[1313] 5. Server: Generates questions, receives and analyzes answers, and generates and stores personality test results.

[1314] 6. Database: Stores user response data and personality trait information.

[1315] System Operation Overview

[1316] 1. Initial Setup:

[1317] The server loads the initial configuration file and initializes the generative artificial intelligence (e.g. ChatGPT), natural language processing (NLP) module, and emotion engine. Dialogue scenarios and personality trait mapping information are also loaded at this time.

[1318] 2. Start the personality test:

[1319] Users access the system using a device such as a smartphone or smart glasses and enter the necessary registration information (such as name, age, desired product, etc.), which is then sent to the server.

[1320] 3. First question generation:

[1321] The server calls ChatGPT's API based on the user's registration information to generate an initial question, such as "What is the most stressful situation for you?"

[1322] 4. Question and answer exchange:

[1323] The generated question is sent to the terminal and displayed to the user, the user enters an answer, and the terminal sends the answer to the server.

[1324] 5. Answer analysis and emotion recognition:

[1325] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and based on the results, it infers the user's personality traits.

[1326] 6. Generate the following question:

[1327] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, it will generate the next question, asking for specific countermeasures, such as "How do you relieve stress?"

[1328] 7. Generating personality test results:

[1329] Through repeated processing, the system collects detailed information about the user's personality. The final personality assessment results are generated by the server and fed back to the user in the form of a report. This information is also used to make personalized product recommendations in the virtual store.

[1330] Specific examples

[1331] For example, if a user answers, "I feel stressed when a work deadline is approaching," the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits are understood in detail and used to suggest products in the virtual store.

[1332] Example prompt sentence:

[1333] Generate the next question based on the user's answers and sentiment.

[1334] Answer: "I feel stressed when I'm facing deadlines at work."

[1335] Emotion: "High stress"

[1336] Using this prompt, the generative AI model generates questions to drive the next dialogue, a process that enables personalized shopping based on the user's personality traits.

[1337] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1338] Step 1:

[1339] The server reads the initial setting file and initializes the generative artificial intelligence, natural language processing module, and emotion engine. This loads the dialogue scenario and personality trait mapping information. The input used in this step is the initial setting file, and the output is the initialized generative artificial intelligence, natural language processing module, and emotion engine.

[1340] Step 2:

[1341] A user accesses the system using a terminal and enters the necessary registration information, such as name, age, desired product, etc. The terminal then transmits this information to the server. The input here is the registration information entered by the user into the terminal, and the output is the registration information transmitted to the server.

[1342] Step 3:

[1343] The server calls the API of the generative artificial intelligence based on the received user registration information to generate the initial question. For example, the question might be, "What situation causes you the most stress?" The input is the user registration information, and the output is the generated initial question.

[1344] Step 4:

[1345] The generated initial question is sent from the server to the terminal and displayed to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the question displayed on the terminal and the user's answer sent to the server.

[1346] Step 5:

[1347] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the user's personality traits as an analysis result.

[1348] Step 6:

[1349] The server calls the generative artificial intelligence API again based on the analysis results and the output of the emotion engine to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the next question generated is, "How do you relieve stress?" The input is the analysis results and the output of the emotion engine, and the output is the generated next question.

[1350] Step 7:

[1351] The server sends the next question to the device, and the user enters an answer, which is then sent back to the server. This process is repeated to gather more information about the user's personality. The input is the next question and the user's additional answer, and the output is a new question based on the additional answer and the analysis results.

[1352] Step 8:

[1353] Once enough information has been collected, the server generates the final personality assessment results. These results are created in the form of a report and sent to the terminal. This information is also used to make personalized product recommendations within the virtual store. The input is the collected personality trait information, and the output is a report of the personality assessment results and product recommendations.

[1354] 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.

[1355] 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.

[1356] 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.

[1357] [Fourth embodiment]

[1358] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1359] 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.

[1360] 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).

[1361] 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.

[1362] 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.

[1363] 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).

[1364] 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.

[1365] 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.

[1366] 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.

[1367] 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.

[1368] 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.

[1369] 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.

[1370] 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."

[1371] This invention relates to a highly accurate personality testing system using generative artificial intelligence. This system analyzes the user's personality traits in detail through natural dialogue with the user, improving the reliability of personality tests in employment examinations.

[1372] System Overview

[1373] The system mainly consists of the following elements:

[1374] 1. Generative AI module: Questions are generated using generative AI such as ChatGPT.

[1375] 2. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[1376] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[1377] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[1378] 5. Database: Stores user response data and personality trait information.

[1379] System Operation

[1380] The processing of the system will be explained in natural language below.

[1381] 1. Initial Setup

[1382] The server reads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[1383] 2. Start the personality test

[1384] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1385] 3. First question generation

[1386] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[1387] 4. Question and answer exchanges

[1388] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[1389] 5. Analysis of responses

[1390] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This makes it possible to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[1391] 6. Next Question Generation

[1392] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[1393] 7. Iteration

[1394] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[1395] 8. Generating personality test results

[1396] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1397] Specific examples

[1398] For example, if a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[1399] The above is an embodiment of the present invention, which goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring of personnel.

[1400] The processing flow will be explained below.

[1401] Step 1:

[1402] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT) and natural language processing (NLP) modules. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[1403] Step 2:

[1404] A user accesses the system's entry point using a terminal and enters necessary information such as name, age, desired occupation, etc. into the registration form. The terminal then sends this registration information to the server.

[1405] Step 3:

[1406] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[1407] Step 4:

[1408] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[1409] Step 5:

[1410] The server analyzes the user's responses using natural language processing technology, including sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results.

[1411] Step 6:

[1412] The server then calls the ChatGPT API again to generate the next question based on the estimated personality traits, and sends the generated next question to the device, which then displays it to the user.

[1413] Step 7:

[1414] The user answers new questions using the device. The device sends the answers to the server. Steps 5 and 6 are repeated to accumulate the necessary personality trait data.

[1415] Step 8:

[1416] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[1417] Step 9:

[1418] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[1419] This is the specific processing flow of the personality testing system that utilizes generative AI. This process enables detailed and accurate analysis of the user's personality traits and supports appropriate hiring decisions.

[1420] Example 1

[1421] 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."

[1422] Conventional personality tests rely on limited question and answer patterns, making it difficult to grasp the user's personality traits in detail. Furthermore, the exchange of questions and answers is mechanical, lacking naturalness and reliability. As a result, accuracy is insufficient for selecting the right candidates, especially in job interviews.

[1423] 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.

[1424] In this invention, the server includes means for generating initial questions using generative artificial intelligence, means for receiving registration information and answers from the user via the terminal, and means for analyzing the user's answers using natural language processing technology. This allows for detailed analysis of the user's personality traits while maintaining the naturalness of the dialogue, enabling highly accurate personality diagnosis.

[1425] "Generative AI" is an AI system that uses natural language processing technology to generate text and dialogue.

[1426] A "terminal" is a device that allows a user to access a system and input or receive information. Examples of such devices include personal computers, smartphones, and tablets.

[1427] "Natural language processing technology" is a technology that enables computers to understand, analyze, and generate human language. This technology includes sentiment analysis, intent estimation, and keyword extraction.

[1428] "Analysis results" are data and information obtained from users' responses using natural language processing technology.

[1429] "Personality traits" is a concept that refers to specific characteristics and tendencies regarding a user's behavior, reactions, and emotions.

[1430] A "dialogue scenario" is a set of questions and answers designed to achieve a specific purpose.

[1431] A "database" is a system for storing, managing, and retrieving data in an organized and efficient manner.

[1432] The "personality assessment results" are reports that detail the user's personality traits, generated based on the user's answers and the analysis results.

[1433] This invention relates to a highly accurate personality testing system using generative artificial intelligence. The system aims to improve the reliability of personality tests in employment examinations by analyzing the user's personality traits in detail through natural dialogue with the user.

[1434] System Configuration

[1435] The system of the present invention includes the following elements:

[1436] 1. Generative AI module: This is a module that generates questions using a generative AI model, for example, using ChatGPT's API.

[1437] 2. Device: The interface through which the user provides input (e.g., computer, smartphone, tablet).

[1438] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[1439] 4. Natural Language Processing (NLP) module: This is a technology for analyzing user responses. Specifically, it performs sentiment analysis, intent estimation, keyword extraction, etc.

[1440] 5. Database: Stores user response data and personality trait information.

[1441] System Operation

[1442] Initial Setup

[1443] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules, including the dialogue scenarios and personality trait mapping information to be used.

[1444] Starting the personality test

[1445] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1446] First question generation

[1447] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question, such as, "What is the most stressful situation for you?"

[1448] Question and answer exchange

[1449] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which then receives the user's answer.

[1450] Analysis of responses

[1451] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. This allows the server to estimate personality traits such as "stress tolerance" and "task management ability" from the content of the user's responses.

[1452] Next question generation

[1453] The server generates the next question based on the analysis results. For example, if the user answers "When I have to complete many tasks in a short period of time," the server generates the next question, such as "How do you relieve stress?"

[1454] Iteration

[1455] This process is repeated to gather detailed information about the user's personality traits, and each response and analysis is stored in a database.

[1456] Generating personality test results

[1457] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1458] Specific examples

[1459] If a user answers, "I feel stressed when a work deadline approaches," the server analyzes this answer and determines the personality trait of "stress response to deadlines." It then generates the next question, "How do you respond when a deadline is approaching?" Through this series of dialogues, the server can gain a detailed understanding of the user's personality traits.

[1460] Prompt Sentence Examples

[1461] Below are some example prompts to be input to the generative AI model:

[1462] "What situations cause you the most stress?"

[1463] "How do you respond when you have to complete many tasks in a short amount of time?"

[1464] "How do you relieve stress?"

[1465] In this way, by combining question generation using generative AI with answer analysis using natural language processing technology, the system can analyze users' personality traits with high accuracy. This system goes beyond the limits of conventional personality tests, enabling more accurate analysis of personality traits and the recruitment of appropriate personnel.

[1466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1467] Step 1: Initial Setup

[1468] The server reads the initial configuration file and initializes the generative artificial intelligence (e.g., ChatGPT) and natural language processing (NLP) modules. At this time, it also loads the dialogue scenarios to be used and mapping information for personality traits. The input is the configuration file, and the output is the initialized AI and NLP modules. Specifically, it uses a Python library to load the API key and model.

[1469] Step 2: Enter your registration information

[1470] A user accesses the system using a terminal and enters registration information such as name, age, desired occupation, etc. The terminal sends this information to the server. The input is the registration information entered by the user into the terminal, and the output is the user information sent to the server. Specifically, information is collected using a web form and sent to the server via an HTTP request.

[1471] Step 3: Generate your first question

[1472] The server selects an appropriate dialogue scenario based on the user's registered information and calls the generative artificial intelligence API to generate the initial question. The input is the user's registered information, and the output is the generated initial question. Specifically, the server calls the ChatGPT API to generate a question using a specific prompt, such as "What is the most stressful situation for you?"

[1473] Step 4: Display the question and enter the answer

[1474] The terminal displays the initial question received from the server to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the user's answer sent to the server. Specifically, this involves displaying a question on a web page, allowing the user to enter an answer in a text box and press the submit button.

[1475] Step 5: Analyze the answers

[1476] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the analyzed personality traits. Specifically, it uses an NLP library to perform sentiment analysis (e.g., calculating sentiment scores) and keyword extraction (e.g., tokenization and stop word removal).

[1477] Step 6: Generate the next question

[1478] The server generates the next question based on the analysis result. For example, if the user answers "When I have to complete many tasks in a short period of time," the next question generated would be "How do you relieve stress?" The input is the analysis result, and the output is the next question. Specifically, the server calls the ChatGPT API again and generates a new question using the appropriate prompt.

[1479] Step 7: Iterate

[1480] The server repeatedly exchanges questions and answers to collect detailed information about the user's personality. The input is each user's answer and its analysis results, and the output is the accumulated analysis results. Each answer and analysis result is stored in a database. Specifically, the system includes a loop process in which, after each answer is received and analyzed, the next question is generated and sent to the device.

[1481] Step 8: Generate personality results

[1482] Once enough necessary information has been collected, the server generates the final personality assessment results. The results are created in report format and fed back to the user via the terminal. The input is the accumulated analysis results, and the output is a report of the personality assessment results that is fed back to the user. Specifically, the various analysis results are integrated to generate a report of the evaluation of each of the user's personality traits, and the report is provided to the user in a format such as PDF.

[1483] The above is the specific flow of the program processing of this system. Each step processes and calculates data based on the input, and obtains the desired output, thereby achieving an efficient and highly accurate personality test.

[1484] (Application example 1)

[1485] 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."

[1486] Conventional personality testing systems rely on the subjective judgment of human interviewers and often lack reliability and consistency. Furthermore, they lack the means to analyze the personality traits of individual employees in detail to improve stress management and work efficiency. This makes it difficult to assign appropriate work and provide individual support, potentially reducing production efficiency within the factory.

[1487] 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.

[1488] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving a response from a user via a terminal, means for analyzing the user's response using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's responses, means for analyzing the user's personality traits and stress level and suggesting appropriate task assignments, and means for providing actions and support to help workers relax when they are feeling stressed. This makes it possible to analyze the personality traits of employees in detail and achieve accurate task assignments and stress management.

[1489] "Generative AI" is AI that has the ability to generate questions through dialogue with the user.

[1490] A "terminal" is an interface device that allows a user to input information. Examples include personal computers and smartphones.

[1491] "Natural language processing technology" is a technology for handling, analyzing, and processing human language.

[1492] "User's answer" is text data provided by the user in response to a question posed by the generative artificial intelligence.

[1493] "Personality traits" are individual personality characteristics or features that are estimated by analyzing the user's responses.

[1494] The "personality diagnosis result" is a detailed diagnosis report generated based on the user's personality traits.

[1495] The "stress level" is an index that indicates the degree of stress that the user is feeling.

[1496] "Work assignment" is the assignment of a person or machine to perform a specific task.

[1497] "Actions" are specific movements or behaviors taken to manage or support stress.

[1498] "Support" refers to advice and help provided to users when they are stressed.

[1499] This invention relates to a personality trait analysis system aimed at improving work efficiency in factories and managing worker stress. This system analyzes personality traits through dialogue with users using generative artificial intelligence and natural language processing technology.

[1500] The system consists of the following elements:

[1501] 1. Generative AI module: Used to generate questions.

[1502] 2. Terminal: An interface device through which a user makes input (e.g., a computer or smartphone).

[1503] 3. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[1504] 4. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[1505] 5. Database: Stores user response data and personality trait information.

[1506] System Operation Overview

[1507] The main processing steps of this system are shown below.

[1508] Hardware and Software Use

[1509] Hardware: Robot terminals and servers in factories

[1510] Software: Generative AI (e.g., OpenAI API), natural language processing module, database module

[1511] Data processing and calculation

[1512] 1. Question generation:

[1513] The server invokes a generative artificial intelligence to generate questions based on appropriate prompts, with the initial prompt being "What was the most stressful thing about work this week?"

[1514] 2. Receiving user responses:

[1515] The user answers the generated questions through the terminal. For example, if the user answers "There was a lot of work, and I was worried about whether I would be able to meet the deadline," the answer is sent from the terminal to the server.

[1516] 3. Analysis of answers:

[1517] The server uses natural language processing technology to analyze the received user responses, including sentiment analysis, intent estimation, and keyword extraction, to estimate the user's personality traits and stress level.

[1518] 4. Stress and Work Management:

[1519] Based on the analysis results, the system identifies the user's personality traits and stress level, suggests appropriate task assignments, and provides support and actions to help them relax if they are under a high level of stress.

[1520] 5. Data storage:

[1521] Each response and analysis result is stored in a database and used for the next diagnosis and to improve work efficiency.

[1522] Specific examples

[1523] For example, if a user answers the question "What was the most stressful thing about work this week?" with "The workload was heavy and I was worried about whether I would be able to meet the deadline," the server will use this answer to analyze the personality trait of "stress response to deadlines." The next question will then be generated: "When you have a lot of work and a deadline looming, how do you deal with it?" Through this series of conversations, the user's personality traits and stress level are understood in detail.

[1524] This allows for work arrangements that are optimized for the user's characteristics and specific support for stress reduction.

[1525] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1526] Specific processing flow of the system program that realizes the application example

[1527] Step 1: Initial Setup

[1528] The server initializes the generative artificial intelligence module and the natural language processing (NLP) module. The server loads the dialogue scenario and personality trait mapping information.

[1529] Input: Initialization file

[1530] Data processing / calculation: Loading dialogue scenarios and personality trait information into system memory

[1531] Output: Initialized generative AI and NLP modules

[1532] Step 2: Start the personality test

[1533] Users access the system using a terminal and enter the necessary registration information (e.g., name, age, desired occupation), which is then sent to the server.

[1534] Input: User registration information

[1535] Data processing / calculation: Collection of user input data and transmission to the server

[1536] Output: User registration information stored on the server

[1537] Step 3: Generate your first question

[1538] The server selects an appropriate dialogue scenario based on the user's registration information and calls the generative artificial intelligence API to generate the initial question.

[1539] Input: User registration information and interaction scenario

[1540] Data processing / calculation: Generative AI API is used to generate appropriate questions

[1541] Output: The initial question generated

[1542] Step 4: Question and answer exchange

[1543] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which the terminal then sends to the server.

[1544] Input: Generated questions and user answers

[1545] Data processing / calculation: Receive user answers in text format and send them to the server

[1546] Output: User answers stored on the server

[1547] Step 5: Analyze the answers

[1548] The server uses NLP technology to analyze the user's responses, including sentiment analysis, intent estimation, and keyword extraction, to assess the user's personality traits and stress level.

[1549] Input: User's answer

[1550] Data processing / calculation: Using NLP technology to analyze emotions and infer intentions, and calculate personality traits

[1551] Output: User's personality traits and stress level as analysis results

[1552] Step 6: Generate the next question

[1553] The server generates the next question based on the analysis results, such as "When you have a lot of work to do and a deadline looming, how do you handle it?"

[1554] Input: Personality traits as analysis results and previous answers

[1555] Data processing / calculation: Calling a generative AI API to generate the next appropriate question

[1556] Output: Next question to display

[1557] Step 7: Iterate

[1558] The process from step 4 to step 6 is repeated to collect detailed information about the user's personality traits. Each response and analysis result is stored in a database.

[1559] Input: Previous conversation history and analysis results

[1560] Data processing / calculation: Repeating the interactive process and storing the data in a database

[1561] Output: Detailed personality trait data

[1562] Step 8: Generate personality results

[1563] Once enough information is collected, the server generates a final personality test result, which is then presented in the form of a report and fed back to the user.

[1564] Input: Accumulated personality trait data

[1565] Data processing / calculation: Generating personality test results and creating reports

[1566] Output: Personality test results in report format and feedback to the user

[1567] 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.

[1568] This invention relates to a personality testing system that uses generative artificial intelligence and an emotion engine. This system analyzes the personality traits of users in detail through dialogue with them and provides highly reliable personality diagnosis.

[1569] System Overview

[1570] The system mainly consists of the following elements:

[1571] 1. Generative artificial intelligence module: Generates questions using generative AI.

[1572] 2. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[1573] 3. Terminal: The interface through which the user provides input (e.g., a computer or smartphone).

[1574] 4. Server: Generates questions, receives and analyzes answers, generates next questions, and produces final diagnostic results.

[1575] 5. Natural Language Processing (NLP) module: Technology for analyzing user responses.

[1576] 6. Database: Stores user response data and personality trait information.

[1577] System Operation

[1578] The processing of the system will be explained in natural language below.

[1579] 1. Initial Setup

[1580] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. The dialogue scenarios and personality trait mapping information to be used are also loaded at this stage.

[1581] 2. Start the personality test

[1582] A user accesses the system using a terminal and enters the necessary registration information (such as name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1583] 3. First question generation

[1584] The server selects an appropriate dialogue scenario based on the user's registered information and calls ChatGPT's API to generate the initial question, such as "What is the most stressful situation for you?"

[1585] 4. Question and answer exchanges

[1586] The generated question is sent to the terminal and displayed to the user. The user inputs an answer, and the terminal sends the answer to the server, which can then receive the user's answer.

[1587] 5. Answer Analysis and Emotion Recognition

[1588] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and then estimates the user's personality traits based on the results. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[1589] 6. Next Question Generation

[1590] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, the server generates a next question asking for specific countermeasures, such as "How do you relieve stress?"

[1591] 7. Iteration

[1592] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[1593] 8. Generating personality test results

[1594] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1595] Specific examples

[1596] For example, suppose a user answers, "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits can be understood in detail.

[1597] The above is an embodiment of the present invention. This goes beyond the limitations of conventional personality tests and enables more accurate analysis of personality traits and appropriate hiring by combining generative artificial intelligence and an emotion engine.

[1598] The processing flow will be explained below.

[1599] Step 1:

[1600] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. At this stage, it also loads the dialogue scenarios and personality trait mapping information to be used.

[1601] Step 2:

[1602] The user accesses the system's entry point using a terminal and enters the necessary registration information (name, age, desired occupation, etc.). The terminal then sends this information to the server.

[1603] Step 3:

[1604] The server selects an appropriate conversation scenario based on the user's registration information, calls the ChatGPT API to generate the initial question, and sends the generated question to the device, which then displays it to the user.

[1605] Step 4:

[1606] The user answers questions posed by the server using the terminal, and the terminal sends the user's answers to the server.

[1607] Step 5:

[1608] The server analyzes the user's responses received using natural language processing technology and an emotion engine. The emotion engine recognizes the emotions in the responses and evaluates their impact on the estimation of personality traits.

[1609] Step 6:

[1610] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the server generates the next question, "How do you relieve stress?" The device displays the generated question to the user.

[1611] Step 7:

[1612] The user answers a new question using the device. The device sends the answer to the server. The server analyzes it again, and the emotion engine recognizes the emotion. This process is repeated until the necessary personality trait data is accumulated.

[1613] Step 8:

[1614] The server uses all the collected data to generate a final personality assessment result, which includes a detailed analysis of the user's personality traits, recommended jobs and career paths, etc.

[1615] Step 9:

[1616] The server sends the final personality assessment report to the device, which then displays it to the user, allowing the user to view details of their personality traits and receive appropriate career advice.

[1617] As a concrete example, consider a case where a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress from this answer. Next, the server generates a question, "How do you respond when a deadline is approaching?" and displays it to the user. The user answers, "I exercise to relieve stress," and the emotion engine recognizes a positive emotion from this answer. Based on this information, the server updates the user's personality trait data.

[1618] This series of processes allows for a detailed understanding of the user's personality traits and enables highly accurate personality diagnosis.

[1619] Example 2

[1620] 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."

[1621] Conventional personality testing systems have a problem with low accuracy of personality diagnosis results because the question generation and answer analysis are fixed and do not fully take into account the user's emotional state. Furthermore, they simply ask many questions one-sidedly and are unable to respond flexibly to the user's real-time emotional changes. This tends to create a stressful experience for users and result in low-quality information.

[1622] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1623] In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving answers from a user via a terminal, means for analyzing the user's answer using natural language processing technology, means for recognizing the user's emotion using an emotion engine and correcting the analysis result based on the emotion, means for generating a next question based on the estimated personality traits, and means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers. This enables a highly accurate personality diagnosis that takes the user's emotional state into consideration.

[1624] "Generative AI" is an AI technology used to generate questions and dialogue.

[1625] A "terminal" is a device that has an interface for users to input data, and specifically includes personal computers and smartphones.

[1626] A "user" is a person who accesses the system and enters information or answers questions.

[1627] "Natural language processing technology" is a technology that analyzes human language, understands its meaning, and infers intent.

[1628] An "emotion engine" is a technology that recognizes emotions from user input and corrects the results of information analysis based on those emotions.

[1629] "Analysis results" are the analysis results of user responses obtained using natural language processing technology and an emotion engine.

[1630] "Personality traits" are characteristics that indicate the user's personality and behavioral patterns, and are estimated based on the analysis results.

[1631] A "question" is a question created by generative artificial intelligence and presented to the user.

[1632] "Personality test results" are detailed information about the user's personality that is created based on repeated question and answer exchanges.

[1633] This invention relates to a personality testing system using generative artificial intelligence and an emotion engine. To implement the system, the following elements are combined and operated:

[1634] System Components

[1635] The system mainly consists of the following hardware and software elements:

[1636] 1. Generative AI module: Software for generating questions using generative AI. Specifically, ChatGPT is used.

[1637] 2. Emotion engine: Software that recognizes emotions from user responses and corrects the analysis results.

[1638] 3. Terminal: An interface through which users input information, including computers, smartphones, etc.

[1639] 4. Server: This is the hardware that generates questions, receives and analyzes answers, generates the next questions, and creates the final diagnosis results.

[1640] 5. Natural Language Processing (NLP) module: Software with technology for analyzing user responses.

[1641] 6. Database: A storage for saving user response data and personality trait information.

[1642] System Operation

[1643] The overall processing of this system will be explained below.

[1644] Initial Setup

[1645] The server loads the initial configuration file and initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine. It also loads the dialogue scenarios and personality trait mapping information to be used.

[1646] Starting the personality test

[1647] Users access the system using a terminal and enter the necessary registration information (name, age, desired occupation, etc.), which is then sent from the terminal to the server.

[1648] First question generation

[1649] The server selects an appropriate dialogue scenario based on the user's registered information and calls the ChatGPT API to generate the initial question, for example, "What is the most stressful situation for you?"

[1650] Question and answer exchange

[1651] The generated question is sent to the terminal and displayed to the user, who then enters an answer, which is then sent from the terminal to the server.

[1652] Answer analysis and emotion recognition

[1653] The server uses natural language processing technology and an emotion engine to analyze the user's responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. Based on the results, the user's personality traits are estimated. The emotion engine recognizes the emotions in the responses and determines how those emotions affect the estimation of personality traits.

[1654] Next question generation

[1655] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes high stress, it generates the question "How do you relieve stress?"

[1656] Iteration

[1657] This process is repeated to gather detailed information about the user's personality. Each answer and the analysis results are stored in a database. The emotion engine also tracks changes in the user's emotions and adjusts the questions accordingly.

[1658] Generating personality test results

[1659] Once enough information is collected, the server generates a final personality assessment result, which is then fed back to the user in the form of a report, including a detailed analysis of personality traits and recommended careers.

[1660] Specific examples

[1661] For example, if a user answers "I feel stressed when a work deadline is approaching," and the emotion engine recognizes a high level of stress, it will then generate the next question: "How do you respond when a deadline is approaching?" If the user answers "I exercise to relieve stress," the emotion engine will recognize the positive emotion in that answer, and the server will update the personality traits based on that data.

[1662] Prompt Sentence Examples

[1663] 1. First question generation:

[1664] "The user's name is Taro Yamada, he is 30 years old, and he wants to be an engineer. Please generate the following question as the first question for the user: 'What is the most stressful situation for you?'"

[1665] 2. Generate the following question:

[1666] "The user answers, 'When I have to accomplish many tasks in a short amount of time,' and the emotion engine recognizes this as a high level of stress. Next, generate a question that asks specific ways to deal with stress: 'How do you relieve stress?'"

[1667] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1668] Step 1: Initial Setup

[1669] The server initializes all modules and loads the necessary information.

[1670] Specific behavior:

[1671] The server reads the initialization file.

[1672] The server initializes the generative artificial intelligence (ChatGPT), natural language processing (NLP) module, and emotion engine.

[1673] The server loads the dialogue scenario and personality trait mapping information.

[1674] Input: Initial setting file, dialogue scenario information, personality trait mapping information

[1675] Output: Initialized modules, loaded information

[1676] Step 2: Enter user registration information

[1677] A user accesses the system using a terminal and enters the required information.

[1678] Specific behavior:

[1679] The user accesses the system's registration page using a terminal.

[1680] The user enters personal information such as name, age, and desired job type.

[1681] The terminal transmits the user's information to the server.

[1682] Input: User registration information (name, age, desired job type, etc.)

[1683] Output: Registration information sent to the server

[1684] Step 3: Generate your first question

[1685] The server generates an initial question based on the user's registration information.

[1686] Specific behavior:

[1687] The server receives the user's registration information.

[1688] The server selects the appropriate dialogue scenario.

[1689] The server calls the API of generative artificial intelligence (ChatGPT) to generate the initial question.

[1690] Input: User registration information

[1691] Output: Initial question (e.g., "What is the most stressful situation for you?")

[1692] Step 4: Question and answer exchange

[1693] The generated question is presented to the user and an answer is received from the user.

[1694] Specific behavior:

[1695] The server generates a question and sends it to the terminal.

[1696] The terminal displays the question to the user.

[1697] The user enters an answer to the question.

[1698] The terminal sends the user's answer to the server.

[1699] Input: Initial question, user answer

[1700] Output: The user's response received

[1701] Step 5: Response analysis and emotion recognition

[1702] The server analyzes the user's responses and recognizes emotions using an emotion engine.

[1703] Specific behavior:

[1704] The server analyzes the received user responses using a natural language processing (NLP) module.

[1705] The analysis includes sentiment analysis, intent estimation, and keyword extraction.

[1706] The server uses an emotion engine to recognize the user's emotion.

[1707] The server estimates the user's personality traits based on the analysis results.

[1708] Input: User's answer

[1709] Output: Analysis results (sentiment analysis, intent estimation, keyword extraction), recognized emotions

[1710] Step 6: Generate the next question

[1711] The server generates the next question based on the analysis results and emotion recognition results.

[1712] Specific behavior:

[1713] The server integrates the analysis results with the output of the emotion engine.

[1714] The server calls the API of generative artificial intelligence (ChatGPT) to generate the next question.

[1715] The server sends the following question to the terminal:

[1716] Input: Analysis results, emotion recognition results

[1717] Output: Next question (e.g., "How do you relieve stress?")

[1718] Step 7: Iterate

[1719] This process is repeated multiple times to gather detailed information about the user's personality traits.

[1720] Specific behavior:

[1721] Repeat process steps 4 to 6.

[1722] Each response and analysis result is stored in a database.

[1723] The server uses an emotion engine to track the user's emotional changes.

[1724] The server adaptively adjusts questions based on emotional changes.

[1725] Input: User answers, analysis results, emotion recognition results

[1726] Output: Updated personality trait database, adaptively adjusted questions

[1727] Step 8: Generate personality results

[1728] Once the server has collected enough information, it will generate a final personality test result.

[1729] Specific behavior:

[1730] The server determines whether it has gathered enough information.

[1731] The server generates the final personality test results.

[1732] The results generated by the server are compiled in a report format.

[1733] The server feeds back the final report to the user.

[1734] Input: Collected personality traits database

[1735] Output: Personality test results, feedback in report format

[1736] (Application example 2)

[1737] 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."

[1738] Conventional personality diagnosis systems only analyze a user's personality traits, but the ways in which the results can be utilized are limited. Furthermore, it is difficult to propose products that meet the individual needs of users in virtual stores. Therefore, there is a need for a method that can more precisely analyze a user's personality traits and link them to product proposals.

[1739] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating an initial question using generative artificial intelligence, means for receiving an answer from the user via the terminal, means for analyzing the user's answer using natural language processing technology, means for estimating the user's personality traits based on the analysis results, means for generating a next question based on the estimated personality traits, means for generating a personality diagnosis result for the user based on the repeatedly generated questions and the user's answers, and means for making product suggestions based on the user's personality in a virtual store. This makes it possible to grasp the user's personality traits in detail and to suggest individual products based on them.

[1740] "Generative AI" is an AI technology that can automatically generate text based on user input.

[1741] The "initial question" is the question that is presented first when starting a personality test for the user.

[1742] A "terminal" is an electronic device that allows a user to input information, and includes smartphones, personal computers, and the like.

[1743] "User responses" are text data and other input information provided by the user.

[1744] "Natural language processing technology" is a technology that allows computers to understand and analyze human language.

[1745] "Analyzing user responses" is the process of interpreting the information provided by the user and identifying various characteristics based on that information.

[1746] "Personality traits" are information about a user's emotions, behavioral patterns, and individual characteristics.

[1747] "Generating the next question" is the process of generating a new question based on the user's answer and the analysis results.

[1748] "Personality diagnosis result" refers to a comprehensive result regarding the user's personality that is generated based on information collected through dialogue.

[1749] A "virtual store" is a virtual store that does not exist in a physical location and provides products and services over the Internet.

[1750] "Product suggestion" is the process of recommending specific products or services based on a user's needs and characteristics.

[1751] This invention relates to a personalized shopping assistant in a virtual store using generative artificial intelligence and an emotion engine. This system can analyze the user's personality traits in detail and make personalized product recommendations based on the results.

[1752] System configuration

[1753] The system mainly consists of the following elements:

[1754] 1. Generative AI: Responsible for generating initial and follow-up questions.

[1755] 2. Terminal: A device such as a smartphone or smart glasses that allows the user to input information and displays results.

[1756] 3. Natural language processing technology: Used to analyze user responses and infer personality traits.

[1757] 4. Emotion engine: Recognizes emotions from user responses and corrects the analysis results.

[1758] 5. Server: Generates questions, receives and analyzes answers, and generates and stores personality test results.

[1759] 6. Database: Stores user response data and personality trait information.

[1760] System Operation Overview

[1761] 1. Initial Setup:

[1762] The server loads the initial configuration file and initializes the generative artificial intelligence (e.g. ChatGPT), natural language processing (NLP) module, and emotion engine. Dialogue scenarios and personality trait mapping information are also loaded at this time.

[1763] 2. Start the personality test:

[1764] Users access the system using a device such as a smartphone or smart glasses and enter the necessary registration information (such as name, age, desired product, etc.), which is then sent to the server.

[1765] 3. First question generation:

[1766] The server calls ChatGPT's API based on the user's registration information to generate an initial question, such as "What is the most stressful situation for you?"

[1767] 4. Question and answer exchange:

[1768] The generated question is sent to the terminal and displayed to the user, the user enters an answer, and the terminal sends the answer to the server.

[1769] 5. Answer analysis and emotion recognition:

[1770] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction, and based on the results, it infers the user's personality traits.

[1771] 6. Generate the following question:

[1772] Based on the analysis results and the output of the emotion engine, the server calls the ChatGPT API again to generate the next question. For example, if the user answers "When I have to complete many tasks in a short period of time," and the emotion engine recognizes this as high stress, it will generate the next question, asking for specific countermeasures, such as "How do you relieve stress?"

[1773] 7. Generating personality test results:

[1774] Through repeated processing, the system collects detailed information about the user's personality. The final personality assessment results are generated by the server and fed back to the user in the form of a report. This information is also used to make personalized product recommendations in the virtual store.

[1775] Specific examples

[1776] For example, if a user answers, "I feel stressed when a work deadline is approaching," the emotion engine recognizes a high level of stress. The server then generates the next question, "How do you respond when a deadline is approaching?" If the user answers, "I exercise to relieve stress," the emotion engine recognizes the positive emotion in that answer, and the server updates the personality traits based on that data. Through this series of interactions, the user's personality traits are understood in detail and used to suggest products in the virtual store.

[1777] Example prompt sentence:

[1778] Generate the next question based on the user's answers and sentiment.

[1779] Answer: "I feel stressed when I'm facing deadlines at work."

[1780] Emotion: "High stress"

[1781] Using this prompt, the generative AI model generates questions to drive the next dialogue, a process that enables personalized shopping based on the user's personality traits.

[1782] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1783] Step 1:

[1784] The server reads the initial setting file and initializes the generative artificial intelligence, natural language processing module, and emotion engine. This loads the dialogue scenario and personality trait mapping information. The input used in this step is the initial setting file, and the output is the initialized generative artificial intelligence, natural language processing module, and emotion engine.

[1785] Step 2:

[1786] A user accesses the system using a terminal and enters the necessary registration information, such as name, age, desired product, etc. The terminal then transmits this information to the server. The input here is the registration information entered by the user into the terminal, and the output is the registration information transmitted to the server.

[1787] Step 3:

[1788] The server calls the API of the generative artificial intelligence based on the received user registration information to generate the initial question. For example, the question might be, "What situation causes you the most stress?" The input is the user registration information, and the output is the generated initial question.

[1789] Step 4:

[1790] The generated initial question is sent from the server to the terminal and displayed to the user. The user enters an answer to the question, and the terminal sends the answer to the server. The input is the question from the server and the user's answer, and the output is the question displayed on the terminal and the user's answer sent to the server.

[1791] Step 5:

[1792] The server uses natural language processing technology and an emotion engine to analyze the received user responses. The analysis includes sentiment analysis, intent estimation, and keyword extraction. The input is the user's response, and the output is the user's personality traits as an analysis result.

[1793] Step 6:

[1794] The server calls the generative artificial intelligence API again based on the analysis results and the output of the emotion engine to generate the next question. For example, if the user answers, "When I have to complete many tasks in a short period of time," the next question generated is, "How do you relieve stress?" The input is the analysis results and the output of the emotion engine, and the output is the generated next question.

[1795] Step 7:

[1796] The server sends the next question to the device, and the user enters an answer, which is then sent back to the server. This process is repeated to gather more information about the user's personality. The input is the next question and the user's additional answer, and the output is a new question based on the additional answer and the analysis results.

[1797] Step 8:

[1798] Once enough information has been collected, the server generates the final personality assessment results. These results are created in the form of a report and sent to the terminal. This information is also used to make personalized product recommendations within the virtual store. The input is the collected personality trait information, and the output is a report of the personality assessment results and product recommendations.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] 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.

[1804] 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.

[1805] 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).

[1806] 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, motorcycles, and other devices, 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.

[1807] 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."

[1808] 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.

[1809] 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).

[1810] 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.

[1811] 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.

[1812] 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.

[1813] 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.

[1814] 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.

[1815] 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.

[1816] 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.

[1817] 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.

[1818] 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.

[1819] 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.

[1820] The following is further disclosed regarding the above embodiment.

[1821] (Claim 1)

[1822] a means for generating an initial question using generative artificial intelligence;

[1823] means for receiving a response from a user via a terminal;

[1824] A means for analyzing user responses using natural language processing technology;

[1825] means for estimating a personality trait of the user based on the analysis result;

[1826] means for generating a next question based on the estimated personality trait;

[1827] A means for generating a personality diagnosis result for a user based on repeatedly generated questions and the user's answers;

[1828] A system including:

[1829] (Claim 2)

[1830] 2. The system according to claim 1, further comprising means for generating a report of the results of the personality assessment of the user and transmitting the report to the terminal.

[1831] (Claim 3)

[1832] 10. The system of claim 1, further comprising means for using a generative artificial intelligence API in generating the initial question and the subsequent question.

[1833] "Example 1"

[1834] (Claim 1)

[1835] a means for generating an initial question using generative artificial intelligence;

[1836] means for receiving registration information and responses from users via a terminal;

[1837] A means for analyzing user responses using natural language processing technology;

[1838] means for estimating a personality trait of the user based on the analysis result;

[1839] A means for selecting an appropriate dialogue scenario based on the registered information;

[1840] means for generating a next question based on the estimated personality trait;

[1841] A means for generating a personality diagnosis result for a user based on repeatedly generated questions and the user's answers;

[1842] A means for storing the generated personality test results in a database;

[1843] A system including:

[1844] (Claim 2)

[1845] 2. The system according to claim 1, further comprising means for generating a report of the results of the personality assessment of the user and transmitting the report to the terminal.

[1846] (Claim 3)

[1847] 10. The system of claim 1, further comprising means for using a generative artificial intelligence API in generating the initial question and the subsequent question.

[1848] "Application Example 1"

[1849] (Claim 1)

[1850] a means for generating an initial question using generative artificial intelligence;

[1851] means for receiving a response from a user via a terminal;

[1852] A means for analyzing user responses using natural language processing technology;

[1853] means for estimating a personality trait of the user based on the analysis result;

[1854] means for generating a next question based on the estimated personality trait;

[1855] A means for generating a personality diagnosis result for a user based on repeatedly generated questions and the user's answers;

[1856] A means for analyzing the user's personality traits and stress level and suggesting appropriate task responsibilities;

[1857] measures to provide actions and support to help workers relax if they are feeling stressed;

[1858] A system including:

[1859] (Claim 2)

[1860] 2. The system according to claim 1, further comprising means for generating a report of the results of the personality assessment of the user and transmitting the report to the terminal.

[1861] (Claim 3)

[1862] 10. The system of claim 1, further comprising means for using a generative artificial intelligence API in generating the initial question and the subsequent question.

[1863] "Example 2: Combining Emotion Engines"

[1864] (Claim 1)

[1865] a means for generating an initial question using generative artificial intelligence;

[1866] means for receiving a response from a user via a terminal;

[1867] A means for analyzing user responses using natural language processing technology;

[1868] A means for estimating a personality trait of a user based on the analysis result;

[1869] A means for recognizing the user's emotions using an emotion engine and correcting the analysis results based on the emotions;

[1870] means for generating a next question based on the estimated personality trait;

[1871] A means for generating a personality diagnosis result for a user based on repeatedly generated questions and the user's answers;

[1872] A system including:

[1873] (Claim 2)

[1874] 2. The system according to claim 1, further comprising means for generating a report of the results of the personality diagnosis of the user and transmitting the report to the terminal.

[1875] (Claim 3)

[1876] 10. The system of claim 1, further comprising means for using a generative artificial intelligence API in generating the initial question and the subsequent question.

[1877] "Application example 2 when combining emotion engines"

[1878] (Claim 1)

[1879] a means for generating an initial question using generative artificial intelligence;

[1880] means for receiving a response from a user via a terminal;

[1881] A means for analyzing user responses using natural language processing technology;

[1882] means for estimating a personality trait of the user based on the analysis result;

[1883] means for generating a next question based on the estimated personality trait;

[1884] A means for generating a personality diagnosis result for a user based on repeatedly generated questions and the user's answers;

[1885] A means for suggesting products based on the user's personality in a virtual store;

[1886] A system including:

[1887] (Claim 2)

[1888] 2. The system according to claim 1, further comprising means for generating a report of the results of the personality assessment of the user and transmitting the report to the terminal.

[1889] (Claim 3)

[1890] 10. The system of claim 1, further comprising means for using a generative artificial intelligence API in generating the initial question and the subsequent question. [Explanation of symbols]

[1891] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for generating an initial question using generative artificial intelligence; means for receiving a response from a user via a terminal; A means for analyzing user responses using natural language processing technology; means for estimating a personality trait of the user based on the analysis result; means for generating a next question based on the estimated personality trait; A means for generating a personality diagnosis result for a user based on repeatedly generated questions and the user's answers; A system including:

2. 2. The system according to claim 1, further comprising means for generating a report of the results of the personality diagnosis of the user and transmitting the report to the terminal.

3. The system of claim 1, further comprising means for using a generative artificial intelligence API in generating the initial question and the subsequent question.

Images