system

Abstract

Provide a system. A personality diagnosis means for conducting a personality diagnosis; data receiving means for receiving the personality data acquired by the personality diagnosis means; a question answering means for answering questions regarding job suitability; data receiving means for receiving the job aptitude data acquired by the question and answer means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination; an output means for outputting the team formation result generated by the analysis means; A system including:

Description

[Technical Field]

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

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

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

[0004] Conventional personality assessment systems (such as the MBTI) can identify personality types to a certain extent, but they are unable to fully take into account detailed job aptitudes or personality traits related to individual jobs. This has led to issues such as difficulty in stimulating communication in the workplace and forming efficient teams. Manual team formation is also problematic as it requires a lot of man-hours and is therefore impractical. The present invention aims to solve these issues and provide a system that realizes efficient team formation that takes into account compatibility in the workplace. [Means for solving the problem]

[0005] The present invention provides a system including a personality assessment means for conducting a personality assessment, a data receiving means for receiving personality data acquired by the personality assessment means, a question and answering means for answering questions regarding job aptitude, a data receiving means for receiving the job aptitude data acquired by the question and answering means, an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate ideal team member combinations, and an output means for outputting the team formation results generated by the analysis means. In particular, the personality assessment means includes a means for conducting an MBTI assessment, and the analysis means analyzes the personality data and the job aptitude data using a chat model. This enables efficient team formation based on users' individual personality traits and job aptitudes.

[0006] The "personality assessment means" is a means for conducting questions and assessments to identify the user's personality type.

[0007] "Personality data" is data relating to the user's personality type and characteristics obtained by the personality assessment means.

[0008] The "data receiving means" is a means for receiving personality data or job aptitude data.

[0009] A "question answering means" is a means for providing an interface and tools for users to answer specific questions.

[0010] "Job aptitude data" is data relating to a user's job aptitude, obtained by the user answering questions about the job.

[0011] The "analysis means" is a means for analyzing personality data and job aptitude data and generating ideal team member combinations.

[0012] The "output means" is a means for notifying or displaying the team formation results generated by the analysis means to the user.

[0013] The "chat model" is an AI model that uses natural language processing technology to analyze data and carry out communication and data analysis. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[0036] Personality test tools

[0037] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[0038] Data Receiving Method

[0039] After the user completes the MBTI test, the answer data is sent from the device to the server, which receives this data as personality data and analyzes the user's MBTI type.

[0040] Question answering means

[0041] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[0042] Analysis means

[0043] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a chat model to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality characteristics and job aptitude.

[0044] Output Method

[0045] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0046] Specific examples

[0047] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[0048] The server receives this data and uses a chat model to assign optimal roles to each user. The resulting team formation is "Team A: User A (project leader), User B (technical leader)," and is notified to the users via their devices. In this way, an efficient team with smooth communication is quickly formed.

[0049] This system is particularly effective when creating small teams or starting new projects, significantly reducing the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[0050] The processing flow will be explained below.

[0051] Step 1:

[0052] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one.

[0053] Step 2:

[0054] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[0055] Step 3:

[0056] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[0057] Step 4:

[0058] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[0059] Step 5:

[0060] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[0061] Step 6:

[0062] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[0063] Step 7:

[0064] The server combines the user's personality data and job aptitude data, and this combined data set represents each user's overall personality traits and job aptitudes.

[0065] Step 8:

[0066] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[0067] Step 9:

[0068] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[0069] Step 10:

[0070] The server transmits the generated team formation results to each user's terminal.

[0071] Step 11:

[0072] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[0073] In this way, effective team formation is achieved through specific actions at each step.

[0074] Example 1

[0075] 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."

[0076] Traditional personality tests and job aptitude assessments are often conducted individually, and there is a lack of systems that automatically create ideal team formation through integrated data analysis. Furthermore, manual data analysis and team formation takes time and effort, hindering efficient team building. Furthermore, specialized knowledge is required to identify appropriate role assignments, which makes it difficult to form appropriate teams for small-scale or short-term projects.

[0077] 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.

[0078] In this invention, the server includes: a personality assessment means for conducting a personality assessment; a data receiving means for receiving the personality data acquired by the personality assessment means; a question and answering means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answering means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination; an output means for outputting the team formation results generated by the analysis means; a means for the user to answer the MBTI assessment and job aptitude questions using a terminal; a means for the terminal to transmit the personality data and job aptitude data to the server; and a means for the analysis means to analyze the dataset using a generative AI model. This makes it possible to comprehensively analyze the user's personality assessment data and job aptitude data and automatically generate an optimal team formation and allocation of roles.

[0079] The "personality diagnosis means" is a means for providing an interface and questions for the user to carry out a personality diagnosis, and for obtaining response data.

[0080] The "data receiving means" is a means for receiving data acquired by the personality diagnosis means and the question and answer means and storing the data in a server.

[0081] The "question answering means" is a means for providing an interface and question contents for the user to answer questions about job aptitude, and for acquiring answer data.

[0082] The "analysis means" is a means for comprehensively analyzing the received personality data and job aptitude data to generate an ideal combination of team members.

[0083] The "output means" is a means for displaying the team formation results generated by the analysis means on the user's terminal.

[0084] "Means for a user to answer MBTI diagnosis and job aptitude questions using a terminal" refers to means for a user to answer questions about MBTI diagnosis and job aptitude via the terminal they use.

[0085] The "means for the terminal to transmit personality data and job aptitude data to the server" refers to the means by which the user's terminal encrypts the personality assessment data and job aptitude data and transmits them to the server.

[0086] "Means for analyzing a dataset using a generative AI model" means means for the analysis means to use a generative AI model (e.g., a large-scale language model) to analyze the received dataset and calculate an ideal team composition.

[0087] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[0088] Personality test tools

[0089] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[0090] Data Receiving Method

[0091] After the user completes the MBTI test, the answer data is sent from the device to the server. The server receives this data as personality data and analyzes the user's MBTI type. For example, the server performs the analysis using an MBTI analysis library implemented in Python.

[0092] Question answering means

[0093] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[0094] Analysis means

[0095] The server integrates the user's personality data and job aptitude data, thereby forming a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a generative AI model (e.g., ChatGPT®) to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality traits and job aptitudes.

[0096] Output Method

[0097] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0098] Specific examples

[0099] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[0100] The server receives this data and uses a generative AI model to assign the optimal role to each user. The generated team formation results in "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. In this way, an efficient team with smooth communication is quickly formed. This system is particularly effective when creating small teams or launching new projects, and can significantly reduce the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[0101] Prompt Sentence Examples

[0102] "Based on the results of the MBTI diagnosis, please generate the optimal team composition for User A, an ENFJ type with strong project management skills, and User B, an ISTP type with extensive technical expertise."

[0103] By inputting this prompt into the generative AI model, the system will begin the process of forming a team and proposing an ideal division of roles.

[0104] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0105] Step 1:

[0106] A user accesses the system using a terminal. Input includes the user accessing the system's URL through a web browser. Output includes the display of an MBTI diagnostic interface. Specifically, the terminal displays the MBTI diagnostic interface received from the server to the user. The user answers a series of questions, and the answer data is temporarily stored in the terminal's local storage.

[0107] Step 2:

[0108] After the device completes the MBTI diagnosis, it sends the user's answer data to the server. The input includes the user's MBTI diagnosis answer data. The output is personality data, which is stored on the server. Specifically, the device encrypts the answer data and sends it to the server, which then receives it and stores it in a database.

[0109] Step 3:

[0110] The server analyzes the received personality data. The input includes the personality data received by the server. The output is the analyzed user's MBTI type. Specifically, the server uses an MBTI analysis library implemented in Python to identify the user's MBTI type.

[0111] Step 4:

[0112] The user proceeds to the detailed job aptitude question section. The input includes the user proceeding to the next section. The output includes the job aptitude question interface being displayed on the terminal. As a specific operation, the terminal displays the job aptitude question interface received from the server, and the user answers it.

[0113] Step 5:

[0114] After the user answers the job aptitude questions, the terminal transmits the answer data to the server. The input includes the user's job aptitude question answer data. The output is the job aptitude data stored on the server. Specifically, the terminal encrypts the answer data and transmits it to the server, which receives it and stores it in a database.

[0115] Step 6:

[0116] The server integrates the personality data and the job aptitude data. The input includes the personality data and the job aptitude data. The output is an integrated data set. Specifically, the server retrieves the personality data and the job aptitude data from the database and integrates them.

[0117] Step 7:

[0118] The server analyzes the integrated dataset using a generative AI model. The input includes the integrated dataset. The output generates the ideal team composition. Specifically, the server inputs a prompt sentence into the generative AI model (e.g., ChatGPT) and obtains the analysis result.

[0119] Step 8:

[0120] The server transmits the generated team formation result to the terminal. The team formation result is included as an input. The team formation result is displayed on the terminal as an output. In specific operations, the server transmits the team formation result to the terminal, and the terminal displays it to the user.

[0121] (Application example 1)

[0122] 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."

[0123] In factories, there is a need to improve work efficiency by optimizing the collaboration between workers and robots. However, currently, teams are not formed based on the personality and job aptitude of workers, which results in inefficient work. Furthermore, even when starting up a new production line, there is a lack of means to quickly form optimal teams. This leads to reduced work efficiency and miscommunication.

[0124] 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.

[0125] In this invention, the server includes: a personality diagnosis means for conducting a personality diagnosis; a data receiving means for receiving personality data acquired by the personality diagnosis means; a question and answer means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answer means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate ideal team member combinations; an output means for outputting the team formation results generated by the analysis means; a means for optimizing team formation of workers and robots based on the team formation results generated by the output means to improve factory work efficiency; and a means for providing work instructions and support to each worker and each robot in real time, thereby making it possible to maximize the capabilities of the workers and robots and improve work efficiency and the quality of communication.

[0126] "Personality assessment means" refers to a method or device used by a user to understand their own personality traits, including MBTI assessments.

[0127] The "data receiving means" refers to a method or device for acquiring and recording data obtained from the personality diagnosis means and question and answer means.

[0128] A "question answering means" is a method or device used by a user to answer questions about job suitability.

[0129] "Job aptitude data" is data indicating how suited a user is to a job, and is acquired through the question and answer means.

[0130] The "analysis means" refers to a method or device for integrating acquired personality data and job aptitude data to generate ideal team member combinations.

[0131] The "output means" refers to a method or device for providing the team formation results generated by the analysis means to the user.

[0132] "Workers" are the human workers who actually perform the work in the factory.

[0133] A "robot" is a mechanical device used to assist or perform factory work.

[0134] An "optimizer" is a method or device used to optimize the teaming of workers and robots.

[0135] "Means for providing in real time" refers to a method or device for providing immediate work instructions and support to workers and robots.

[0136] The following system configuration will be described as an embodiment of the present invention.

[0137] Overall overview

[0138] This is a system that optimizes the team composition of workers and robots to efficiently carry out factory work. This system is composed of a user terminal, a server, and a robot, and includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, an output means, an optimization means, and a real-time instruction means.

[0139] User terminal

[0140] The user terminal is a device such as a tablet or smartphone. First, the user accesses the personality diagnosis interface using the user terminal and performs the MBTI diagnosis. Next, the user answers questions about job suitability. These diagnosis results are temporarily stored on the user terminal and then sent to the server.

[0141] server

[0142] The server receives the personality assessment data and job aptitude data and performs an integrated analysis of them. A generative AI model (e.g., OpenAI (registered trademark) GPT-4 (registered trademark) SDK) is used for the analysis. Based on the analysis results, the server generates an ideal team composition of workers and robots. This generated result is then sent back to the user's device and displayed to the user.

[0143] robot

[0144] Robots installed in factories work with the most suitable workers based on the team formation results sent from the server, and have the ability to provide work instructions and support in real time.

[0145] Specific examples

[0146] When starting up a new production line in a factory, Worker A and Worker B access the system. An MBTI diagnosis reveals that Worker A has the personality traits of "ENTJ" and excels in leadership. Worker B has the personality traits of "ISFP" and excels in attention to detail. The server analyzes this data and determines that it would be best to pair Worker A with Robot R2 (with heavy load transport capabilities) and Worker B with Robot R1 (with precision work support capabilities).

[0147] As a result, worker A works with R2 to manage route planning and efficient transportation, while worker B works with R1 to perform precise assembly work. In this way, optimal teaming of workers and robots is quickly achieved, significantly improving the efficiency and accuracy of the production line.

[0148] Prompt Sentence Examples

[0149] "Form optimal teams of workers and robots in a factory. Generate optimal worker-robot pairs based on the provided MBTI diagnostic data and job aptitude data."

[0150] In this way, by providing appropriate instructions and support to workers and robots in real time based on the analysis results of personality diagnostic data and job aptitude data, it is possible to improve work efficiency and the quality of communication within the factory.

[0151] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0152] Step 1:

[0153] The user accesses the personality diagnosis interface using a device (tablet or smartphone) and conducts the MBTI diagnosis. The user answers a series of questions, and the answer data is temporarily saved on the device. The input is the user's answer data, and the output is the temporarily saved personality data.

[0154] Step 2:

[0155] The device sends the temporarily saved personality data to the server. The server receives this data and analyzes the user's MBTI type. The input is the personality data sent from the device, and the output is the analyzed MBTI type.

[0156] Step 3:

[0157] The user then proceeds to the job aptitude question section. The user answers a series of job-related questions displayed on the terminal, and the answers are temporarily saved on the terminal. The input is the user's job aptitude answer data, and the output is the temporarily saved job aptitude data.

[0158] Step 4:

[0159] The terminal sends the temporarily stored job aptitude data to the server. The server receives this data, integrates it with the personality data, and begins analysis. The input is the job aptitude data and personality data sent from the terminal, and the output is the integrated data set.

[0160] Step 5:

[0161] The server analyzes the integrated dataset using a generative AI model (e.g., OpenAI GPT-4 SDK). Specifically, it generates ideal team member combinations based on personality data and job aptitude data. The input is the integrated dataset, and the output is the team composition results generated by the analysis.

[0162] Step 6:

[0163] The server sends the generated team composition results to the terminal. The terminal displays the received results to the user. The input is the generated team composition results, and the output is the results displayed to the user.

[0164] Step 7:

[0165] Based on the generated team formation results, the server sends instructions to the robots to optimally match workers and robots. The robots follow the received instructions and begin work. The input is the team formation results, and the output is work instructions for the robots.

[0166] Step 8:

[0167] The robot works with the worker, providing real-time instructions and support as needed. The input is instructions from the server and on-site situation data, and the output is support for the worker and the progress of the work.

[0168] This will enable workers and robots to receive appropriate instructions and support in real time based on the analysis results of personality diagnostic data and job aptitude data, improving work efficiency and the quality of communication within the factory.

[0169] 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.

[0170] The present invention is a system that supports more sophisticated and effective team formation by incorporating user emotion data into analysis in addition to personality assessment and job aptitude data. This system includes personality assessment means, data receiving means, question and answer means, an emotion engine, analysis means, and output means.

[0171] Personality test tools

[0172] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one, which identifies the user's personality type.

[0173] Data Receiving Method

[0174] The device temporarily stores the user's MBTI test answer data, and after answering all questions, sends the data to the server, which receives and analyzes the data as personality data.

[0175] Question answering means

[0176] Next, the user is taken to a detailed job fit question section, where the user again answers job-related questions, which gather data about the user's job fit.

[0177] Emotion Engine

[0178] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine uses voice recognition and image analysis technologies to analyze the user's speech and facial expressions to obtain emotional data.

[0179] Voice Recognition Technology

[0180] The device collects user speech data and performs real-time emotion analysis to identify the user's emotional state.

[0181] Image analysis technology

[0182] The device collects the user's facial expression data and uses image analysis technology to identify emotions, thereby supplementing the user's emotional data.

[0183] Analysis means

[0184] The server integrates the user's personality data, job aptitude data, and emotional data to form a dataset, and then analyzes the dataset using a chat model to generate an ideal team composition, which takes into account the user's personality traits, job aptitude, and emotional state in a comprehensive manner.

[0185] Output Method

[0186] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0187] Specific examples

[0188] For example, when a new project is launched, this system is used to organize a project team. User A and User B access the system and answer MBTI tests and job aptitude questions, respectively. The emotion engine then analyzes the users' speech and facial expressions to obtain emotional data.

[0189] User A is an "ENFJ" with a strong aptitude for project management, and emotional data indicates high motivation. On the other hand, User B is an "ISTP" with a wealth of technical expertise, and emotional data indicates a calm and analytical attitude.

[0190] Based on this information, the server uses a chat model to generate an appropriate team composition. The resulting team composition is "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. This results in the rapid composition of an efficient team with smooth communication.

[0191] This system enables efficient team formation that takes emotional aspects into consideration, especially when creating small teams or starting new projects, and significantly reduces the amount of work required compared to manual team formation.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions in sequence.

[0195] Step 2:

[0196] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[0197] Step 3:

[0198] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[0199] Step 4:

[0200] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[0201] Step 5:

[0202] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[0203] Step 6:

[0204] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[0205] Step 7:

[0206] While the user is entering or completing the job fit questions, the emotion engine kicks in. The user's speech and facial expressions are collected.

[0207] Step 8:

[0208] The device sends voice data to the emotion engine, which performs real-time emotion analysis to identify the user's emotional state.

[0209] Step 9:

[0210] The device uses a camera to send the user's facial expression data to the emotion engine, which then analyzes the image to obtain the user's emotion data.

[0211] Step 10:

[0212] The device temporarily stores the emotion data and then transmits it to the server.

[0213] Step 11:

[0214] The server integrates the user's personality data, job aptitude data, and emotional data, and the integrated data set comprehensively represents each user's personality traits, job aptitudes, and emotional state.

[0215] Step 12:

[0216] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[0217] Step 13:

[0218] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[0219] Step 14:

[0220] The server transmits the generated team formation results to each user's terminal.

[0221] Step 15:

[0222] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[0223] In this way, effective team formation is achieved through specific actions at each step.

[0224] Example 2

[0225] 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."

[0226] Conventional team formation systems only consider users' personality traits and job aptitudes, ignoring emotional data, and therefore do not adequately consider actual team performance or the quality of communication. This often leads to a lack of cooperation among team members and inappropriate division of roles, making it difficult to efficiently form new projects or small teams.

[0227] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a personality diagnosis means for conducting a personality diagnosis, a question answering means for answering questions about job aptitude, and an emotion engine for acquiring emotion data. This enables ideal team formation that comprehensively considers personality traits, job aptitude, and emotional state.

[0228] A "personality assessment means" is a device or software for assessing a user's personality traits.

[0229] The "data receiving means" is a device or software for receiving personality data, job aptitude data, and emotion data.

[0230] The "question answering means" is a device or software that allows a user to answer questions about job suitability.

[0231] An "emotion engine" is a device or software that analyzes a user's speech and facial expressions to obtain emotional data.

[0232] The "analysis means" is a device or software for integrating and analyzing personality data, job aptitude data, and emotional data to generate an ideal team formation result.

[0233] The "output means" is a device or software for outputting the team formation results generated by the analysis means to the user.

[0234] A "generative AI model" is an artificial intelligence model that analyzes data and generates ideal team composition results.

[0235] A "prompt" is an instruction for inputting data into a generative AI model.

[0236] This invention provides a system for supporting more sophisticated and effective team formation by integrating user personality traits, job aptitudes, and emotional data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotional engine, an analysis unit, and an output unit.

[0237] Personality test tools

[0238] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions in sequence, which identifies their personality type.

[0239] Data Receiving Method

[0240] The device temporarily stores the user's MBTI diagnostic data, and after answering all questions, sends the data to the server, which receives the data as personality data and stores it in a database.

[0241] Question answering means

[0242] The user then goes to the job suitability question section on the terminal and answers the job-related questions. The terminal also temporarily stores these answer data, and after all questions have been answered, sends the data to the server. The server receives this data as job suitability data and stores it in a database.

[0243] Emotion Engine

[0244] The device uses voice recognition and image analysis technologies to collect the user's speech and facial expression data, thereby analyzing the user's emotional state in real time and obtaining emotional data. For example, the device may use the user's microphone to collect speech data and the built-in camera to capture facial expression data.

[0245] Analysis means

[0246] The server combines the received personality data, job aptitude data, and emotion data to form a single dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition results.

[0247] Specifically, the server inputs the following prompt sentence into the generative AI model:

[0248] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[0249] User A:

[0250] Personality data: ENFJ

[0251] Job aptitude data: Strong aptitude for project management

[0252] Emotional data: High motivation

[0253] User B:

[0254] Personality data: ISTP

[0255] Job Qualifications: Strong technical expertise

[0256] Emotional data: Calm and analytical attitude

[0257] Output Method

[0258] The server sends the generated team formation results to each user's device. The device receives the results and displays them to the user. For example, if User A is recommended as the project leader and User B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0259] This system enables efficient team formation that comprehensively considers personality traits, job aptitude, and emotional state, significantly reducing the amount of work required compared to manual team formation, especially when creating small teams or launching new projects.

[0260] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0261] Processing Steps

[0262] Step 1: Start the personality test

[0263] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions one by one.

[0264] Input: The URL the user visits and the question they are asked

[0265] Output: User's answer

[0266] Specific operation: The user launches a browser, accesses the specified URL, and clicks the "Start" button to begin answering the questions.

[0267] Step 2: Submit your personality test data

[0268] The device temporarily stores the user's MBTI diagnostic answers, and after answering all questions, sends the data to the server, which receives this data as personality data and stores it in a database.

[0269] Input: Answers to all user questions

[0270] Output: Personality data stored in a database

[0271] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends this data in JSON format to the server, and the server stores the received data in the database.

[0272] Step 3: Begin the job qualification questions

[0273] The user navigates to the job suitability question section on the terminal and answers the questions displayed to collect job suitability data.

[0274] Input: Job Qualification Questions

[0275] Output: User's answer

[0276] Specific Actions: A user opens the Job Qualification Questions section and clicks the "Start" button to begin answering the questions.

[0277] Step 4: Submit your job suitability data

[0278] The terminal temporarily stores the answer data for the job aptitude questions, and after answering all the questions, transmits it to the server. The server receives this as job aptitude data and stores it in a database.

[0279] Input: Answers to all user questions

[0280] Output: Job suitability data stored in a database

[0281] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends the answer data in JSON format to the server, which then receives the data and stores it in the database.

[0282] Step 5: Collecting emotion data

[0283] The device uses voice recognition and image analysis technologies to collect data on the user's speech and facial expressions, thereby analyzing the user's emotional state in real time and obtaining emotional data.

[0284] Input: User's speech and facial expression data

[0285] Output: Parsed emotion data

[0286] Specific behavior:

[0287] Voice recognition technology: Collects user speech using the device's built-in microphone and analyzes it in real time.

[0288] Image analysis technology: Captures and analyzes the user's facial expressions using the device's built-in camera.

[0289] Step 6: Data synthesis and analysis

[0290] The server creates a dataset that integrates personality data, job aptitude data, and emotional data, and analyzes it using a generative AI model (e.g., GPT-4).

[0291] Input: personality data, job aptitude data, emotional data

[0292] Output: Analysis results (ideal team composition)

[0293] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[0294] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[0295] User A:

[0296] Personality data: ENFJ

[0297] Job aptitude data: Strong aptitude for project management

[0298] Emotional data: High motivation

[0299] User B:

[0300] Personality data: ISTP

[0301] Job Qualifications: Strong technical expertise

[0302] Emotional data: Calm and analytical attitude

[0303] Step 7: Generate team formation results

[0304] Based on the data analyzed by the generative AI model, the server generates the optimal team composition results.

[0305] Input: Analysis results of the generative AI model

[0306] Output: Team composition results

[0307] Specific operation: The generated team composition results are stored in an internal data structure such as "Team A: User A (project leader), User B (technical leader)".

[0308] Step 8: Viewing the results

[0309] The server transmits the generated team formation results to each user's terminal, which receives the results and displays them to the user.

[0310] Input: Generated team composition results

[0311] Output: Team formation results displayed to the user

[0312] Specific operation: The server sends the team formation results in JSON format to the user's device, which then parses the results and displays them in the browser. For example, it displays "Team A: User A (Project Leader), User B (Technical Leader)."

[0313] (Application example 2)

[0314] 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."

[0315] Conventional team formation systems have relied solely on analysis based on personality data and job aptitude data, but because they do not take emotional data into account, the accuracy of the team combinations is insufficient, making it difficult to maximize performance in actual work. Furthermore, there is a lack of tools for quickly and effectively forming teams, a problem that is particularly pronounced in environments where immediate response is required, such as factories. It is necessary to develop a system that can output real-time analysis results using wearable devices such as smart glasses.

[0316] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0317] In this invention, the server includes a personality assessment unit, a question and answer unit regarding job aptitude, an emotion engine that analyzes the user's voice and facial expressions to acquire emotion data, an analysis unit, and an output unit. This allows for an integrated analysis of personality data, job aptitude data, and emotion data, enabling the generation of ideal team member combinations. Furthermore, analysis results can be presented to employees in real time using wearable devices such as smart glasses, enabling immediate response on the factory floor.

[0318] The "personality assessment means" is a function for providing questions and tests to assess the user's personality traits and obtaining the results as data.

[0319] The "data receiving means" is a function for receiving data obtained from the personality diagnosis means and question and answer means and transmitting the data to the server.

[0320] The "question answering means" is a function that provides an interface for the user to answer questions about job aptitude and acquires the answers as data.

[0321] An "emotion engine" is a system that incorporates technology to analyze a user's voice and facial expressions and identify their emotional state.

[0322] The "analysis means" is a function for comprehensively analyzing the received personality data, job aptitude data, and emotional data to generate an ideal team composition.

[0323] The "output means" is a function for presenting the team formation results generated by the analysis means to the user.

[0324] A "generative AI model" is a trained artificial intelligence model used to analyze data and optimize team composition.

[0325] A "prompt sentence" is an input sentence given when using a generative AI model, and is a sentence that instructs specific analysis and generation.

[0326] The present invention provides a system for supporting team formation by analyzing personality assessment, job aptitude data, and emotion data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotion engine, an analysis unit, and an output unit.

[0327] System configuration

[0328] 1. Personality test:

[0329] The user wears the smart glasses and launches the application. The MBTI diagnosis is displayed on the screen, and the user answers the questions by voice. The answer data is temporarily stored in the smart glasses and then sent to the server.

[0330] 2. Question answering method:

[0331] Once the personality test is complete, the user proceeds to the job aptitude section, where questions are similarly displayed and answered verbally. This data is also sent to the server.

[0332] 3. Emotion Engine:

[0333] The camera on the smart glasses analyzes the user's facial expressions in real time and collects emotion data using speech recognition software (e.g., Google® Cloud Speech-to-Text API). The emotion engine analyzes the emotion data using OpenCV and TENSORFLOW®.

[0334] 4. Data Receiving Method:

[0335] The server receives and centrally manages the personality data, job aptitude data, and emotion data, which are then processed in an integrated manner by the analysis means.

[0336] 5. Analysis methods:

[0337] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition.

[0338] 6. Output Method:

[0339] The analysis results are displayed in real time on the smart glasses display, allowing users to quickly check the team composition results.

[0340] Specific examples

[0341] When a new production line is introduced, factory employees use smart glasses to access the system. Employee A is identified as an ESTJ in the MBTI test, and a job aptitude test indicates a high aptitude for line supervisors. Emotional data indicates high levels of concentration and judgment. Employee B is identified as an INFP, with a high aptitude for quality control, and is also relaxed and cooperative.

[0342] Based on this information, the server uses a generative AI model (GPT-4) to optimally organize the team. The smart glasses display "New production line team: Employee A (line supervisor), Employee B (quality control)."

[0343] Prompt Sentence Examples

[0344] Here are some example prompts to input to a generative AI model:

[0345] "Please suggest the optimal team composition based on the following personality test, job aptitude data, and emotional data."

[0346] In this way, the personality data, job aptitude data, and emotional data are analyzed comprehensively to realize an optimal team composition.

[0347] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0348] Step 1:

[0349] The user starts the smart glasses and starts the personality test (MBTI test). The personality test questions are displayed on the smart glasses' display, and the user answers by voice. The device collects and temporarily stores the user's answer data.

[0350] Input: User's voice responses to personality questions

[0351] Data processing: Converts voice data into text data and temporarily saves it as response data

[0352] Output: Temporarily saved personality test answer data

[0353] Step 2:

[0354] When the personality test is completed, the device sends the answer data to the server, which stores the received data as personality data.

[0355] Input: Temporarily saved personality test answer data

[0356] Data processing: Response data is sent to the server and saved as personality data

[0357] Output: Personality data stored on the server

[0358] Step 3:

[0359] After the personality test, the user proceeds to the job aptitude question section. Questions are displayed on the device's display, and the user answers by voice. The device collects and temporarily stores the answer data on job aptitude.

[0360] Input: User's spoken responses to job qualification questions

[0361] Data processing: Converts voice data into text data and temporarily saves it as response data

[0362] Output: Temporarily saved job aptitude answer data

[0363] Step 4:

[0364] When the job aptitude question session is completed, the terminal transmits the job aptitude answer data to the server, which stores the received data as job aptitude data.

[0365] Input: Temporarily saved job aptitude answer data

[0366] Data processing: The response data is sent to the server and saved as job aptitude data.

[0367] Output: Job suitability data stored on the server

[0368] Step 5:

[0369] Once the user completes the personality and job aptitude tests, the emotion engine is activated. The device collects the user's voice and facial expressions in real time. The emotion data is analyzed using speech recognition software (Google Cloud Speech-to-Text API) and image analysis software (OpenCV, TensorFlow) and sent to the server.

[0370] Input: User's voice and facial expression data

[0371] Data processing: Analyze voice data and recognize emotional states (using Google Cloud Speech-to-Text API). Analyze facial expression data and recognize emotional states (using OpenCV and TensorFlow). Integrate as emotional data.

[0372] Output: Parsed emotion data

[0373] Step 6:

[0374] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (GPT-4) to generate ideal team compositions.

[0375] Input: personality data, job aptitude data, emotional data

[0376] Data processing: Integrate data to form a dataset, analyze the dataset using a generative AI model (GPT-4), and generate the ideal team composition.

[0377] Output: Generated team composition results

[0378] Step 7:

[0379] The server transmits the generated team formation results to the terminal, which then displays the results in real time on the display of the smart glasses for the user.

[0380] Input: Generated team composition results

[0381] Data processing: Team formation results are sent to the device and displayed on the screen

[0382] Output: Team formation results displayed on the smart glasses display

[0383] 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.

[0384] 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 (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0385] 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.

[0386] [Second embodiment]

[0387] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0388] 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.

[0389] 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).

[0390] 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.

[0391] 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.

[0392] 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).

[0393] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0394] 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.

[0395] 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.

[0396] 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.

[0397] In the smart glasses 214, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0398] 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."

[0399] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[0400] Personality test tools

[0401] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[0402] Data Receiving Method

[0403] After the user completes the MBTI test, the answer data is sent from the device to the server, which receives this data as personality data and analyzes the user's MBTI type.

[0404] Question answering means

[0405] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[0406] Analysis means

[0407] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a chat model to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality characteristics and job aptitude.

[0408] Output Method

[0409] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0410] Specific examples

[0411] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[0412] The server receives this data and uses a chat model to assign optimal roles to each user. The resulting team formation is "Team A: User A (project leader), User B (technical leader)," and is notified to the users via their devices. In this way, an efficient team with smooth communication is quickly formed.

[0413] This system is particularly effective when creating small teams or starting new projects, significantly reducing the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[0414] The processing flow will be explained below.

[0415] Step 1:

[0416] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one.

[0417] Step 2:

[0418] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[0419] Step 3:

[0420] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[0421] Step 4:

[0422] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[0423] Step 5:

[0424] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[0425] Step 6:

[0426] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[0427] Step 7:

[0428] The server combines the user's personality data and job aptitude data, and this combined data set represents each user's overall personality traits and job aptitudes.

[0429] Step 8:

[0430] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[0431] Step 9:

[0432] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[0433] Step 10:

[0434] The server transmits the generated team formation results to each user's terminal.

[0435] Step 11:

[0436] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[0437] In this way, effective team formation is achieved through specific actions at each step.

[0438] Example 1

[0439] 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."

[0440] Traditional personality tests and job aptitude assessments are often conducted individually, and there is a lack of systems that automatically create ideal team formation through integrated data analysis. Furthermore, manual data analysis and team formation takes time and effort, hindering efficient team building. Furthermore, specialized knowledge is required to identify appropriate role assignments, which makes it difficult to form appropriate teams for small-scale or short-term projects.

[0441] 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.

[0442] In this invention, the server includes: a personality assessment means for conducting a personality assessment; a data receiving means for receiving the personality data acquired by the personality assessment means; a question and answering means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answering means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination; an output means for outputting the team formation results generated by the analysis means; a means for the user to answer the MBTI assessment and job aptitude questions using a terminal; a means for the terminal to transmit the personality data and job aptitude data to the server; and a means for the analysis means to analyze the dataset using a generative AI model. This makes it possible to comprehensively analyze the user's personality assessment data and job aptitude data and automatically generate an optimal team formation and allocation of roles.

[0443] The "personality diagnosis means" is a means for providing an interface and questions for the user to carry out a personality diagnosis, and for obtaining response data.

[0444] The "data receiving means" is a means for receiving data acquired by the personality diagnosis means and the question and answer means and storing the data in a server.

[0445] The "question answering means" is a means for providing an interface and question contents for the user to answer questions about job aptitude, and for acquiring answer data.

[0446] The "analysis means" is a means for comprehensively analyzing the received personality data and job aptitude data to generate an ideal combination of team members.

[0447] The "output means" is a means for displaying the team formation results generated by the analysis means on the user's terminal.

[0448] "Means for a user to answer MBTI diagnosis and job aptitude questions using a terminal" refers to means for a user to answer questions about MBTI diagnosis and job aptitude via the terminal they use.

[0449] The "means for the terminal to transmit personality data and job aptitude data to the server" refers to the means by which the user's terminal encrypts the personality assessment data and job aptitude data and transmits them to the server.

[0450] "Means for analyzing a dataset using a generative AI model" means means for the analysis means to use a generative AI model (e.g., a large-scale language model) to analyze the received dataset and calculate an ideal team composition.

[0451] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[0452] Personality test tools

[0453] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[0454] Data Receiving Method

[0455] After the user completes the MBTI test, the answer data is sent from the device to the server. The server receives this data as personality data and analyzes the user's MBTI type. For example, the server performs the analysis using an MBTI analysis library implemented in Python.

[0456] Question answering means

[0457] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[0458] Analysis means

[0459] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a generative AI model (e.g., ChatGPT) to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality traits and job aptitude.

[0460] Output Method

[0461] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0462] Specific examples

[0463] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[0464] The server receives this data and uses a generative AI model to assign the optimal role to each user. The generated team formation results in "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. In this way, an efficient team with smooth communication is quickly formed. This system is particularly effective when creating small teams or launching new projects, and can significantly reduce the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[0465] Prompt Sentence Examples

[0466] "Based on the results of the MBTI diagnosis, please generate the optimal team composition for User A, an ENFJ type with strong project management skills, and User B, an ISTP type with extensive technical expertise."

[0467] By inputting this prompt into the generative AI model, the system will begin the process of forming a team and proposing an ideal division of roles.

[0468] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0469] Step 1:

[0470] A user accesses the system using a terminal. Input includes the user accessing the system's URL through a web browser. Output includes the display of an MBTI diagnostic interface. Specifically, the terminal displays the MBTI diagnostic interface received from the server to the user. The user answers a series of questions, and the answer data is temporarily stored in the terminal's local storage.

[0471] Step 2:

[0472] After the device completes the MBTI diagnosis, it sends the user's answer data to the server. The input includes the user's MBTI diagnosis answer data. The output is personality data, which is stored on the server. Specifically, the device encrypts the answer data and sends it to the server, which then receives it and stores it in a database.

[0473] Step 3:

[0474] The server analyzes the received personality data. The input includes the personality data received by the server. The output is the analyzed user's MBTI type. Specifically, the server uses an MBTI analysis library implemented in Python to identify the user's MBTI type.

[0475] Step 4:

[0476] The user proceeds to the detailed job aptitude question section. The input includes the user proceeding to the next section. The output includes the job aptitude question interface being displayed on the terminal. As a specific operation, the terminal displays the job aptitude question interface received from the server, and the user answers it.

[0477] Step 5:

[0478] After the user answers the job aptitude questions, the terminal transmits the answer data to the server. The input includes the user's job aptitude question answer data. The output is the job aptitude data stored on the server. Specifically, the terminal encrypts the answer data and transmits it to the server, which receives it and stores it in a database.

[0479] Step 6:

[0480] The server integrates the personality data and the job aptitude data. The input includes the personality data and the job aptitude data. The output is an integrated data set. Specifically, the server retrieves the personality data and the job aptitude data from the database and integrates them.

[0481] Step 7:

[0482] The server analyzes the integrated dataset using a generative AI model. The input includes the integrated dataset. The output generates the ideal team composition. Specifically, the server inputs a prompt sentence into the generative AI model (e.g., ChatGPT) and obtains the analysis result.

[0483] Step 8:

[0484] The server transmits the generated team formation result to the terminal. The team formation result is included as an input. The team formation result is displayed on the terminal as an output. In specific operations, the server transmits the team formation result to the terminal, and the terminal displays it to the user.

[0485] (Application example 1)

[0486] 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."

[0487] In factories, there is a need to improve work efficiency by optimizing the collaboration between workers and robots. However, currently, teams are not formed based on the personality and job aptitude of workers, which results in inefficient work. Furthermore, even when starting up a new production line, there is a lack of means to quickly form optimal teams. This leads to reduced work efficiency and miscommunication.

[0488] 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.

[0489] In this invention, the server includes: a personality diagnosis means for conducting a personality diagnosis; a data receiving means for receiving personality data acquired by the personality diagnosis means; a question and answer means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answer means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate ideal team member combinations; an output means for outputting the team formation results generated by the analysis means; a means for optimizing team formation of workers and robots based on the team formation results generated by the output means to improve factory work efficiency; and a means for providing work instructions and support to each worker and each robot in real time, thereby making it possible to maximize the capabilities of the workers and robots and improve work efficiency and the quality of communication.

[0490] "Personality assessment means" refers to a method or device used by a user to understand their own personality traits, including MBTI assessments.

[0491] The "data receiving means" refers to a method or device for acquiring and recording data obtained from the personality diagnosis means and question and answer means.

[0492] A "question answering means" is a method or device used by a user to answer questions about job suitability.

[0493] "Job aptitude data" is data indicating how suited a user is to a job, and is acquired through the question and answer means.

[0494] The "analysis means" refers to a method or device for integrating acquired personality data and job aptitude data to generate ideal team member combinations.

[0495] The "output means" refers to a method or device for providing the team formation results generated by the analysis means to the user.

[0496] "Workers" are the human workers who actually perform the work in the factory.

[0497] A "robot" is a mechanical device used to assist or perform factory work.

[0498] An "optimizer" is a method or device used to optimize the teaming of workers and robots.

[0499] "Means for providing in real time" refers to a method or device for providing immediate work instructions and support to workers and robots.

[0500] The following system configuration will be described as an embodiment of the present invention.

[0501] Overall overview

[0502] This is a system that optimizes the team composition of workers and robots to efficiently carry out factory work. This system is composed of a user terminal, a server, and a robot, and includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, an output means, an optimization means, and a real-time instruction means.

[0503] User terminal

[0504] The user terminal is a device such as a tablet or smartphone. First, the user accesses the personality diagnosis interface using the user terminal and performs the MBTI diagnosis. Next, the user answers questions about job suitability. These diagnosis results are temporarily stored on the user terminal and then sent to the server.

[0505] server

[0506] The server receives the personality assessment data and job aptitude data and performs an integrated analysis of them. A generative AI model (e.g., OpenAI GPT-4 SDK) is used for the analysis. Based on the analysis results, the server generates an ideal team composition of workers and robots. This generated result is then sent back to the user's device and displayed to the user.

[0507] robot

[0508] Robots installed in factories work with the most suitable workers based on the team formation results sent from the server, and have the ability to provide work instructions and support in real time.

[0509] Specific examples

[0510] When starting up a new production line in a factory, Worker A and Worker B access the system. An MBTI diagnosis reveals that Worker A has the personality traits of "ENTJ" and excels in leadership. Worker B has the personality traits of "ISFP" and excels in attention to detail. The server analyzes this data and determines that it would be best to pair Worker A with Robot R2 (with heavy load transport capabilities) and Worker B with Robot R1 (with precision work support capabilities).

[0511] As a result, worker A works with R2 to manage route planning and efficient transportation, while worker B works with R1 to perform precise assembly work. In this way, optimal teaming of workers and robots is quickly achieved, significantly improving the efficiency and accuracy of the production line.

[0512] Prompt Sentence Examples

[0513] "Form optimal teaming of factory workers and robots. Generate optimal worker-robot pairs based on the provided MBTI diagnostic data and job aptitude data."

[0514] In this way, by providing appropriate instructions and support to workers and robots in real time based on the analysis results of personality diagnostic data and job aptitude data, it is possible to improve work efficiency and the quality of communication within the factory.

[0515] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0516] Step 1:

[0517] The user accesses the personality diagnosis interface using a device (tablet or smartphone) and conducts the MBTI diagnosis. The user answers a series of questions, and the answer data is temporarily saved on the device. The input is the user's answer data, and the output is the temporarily saved personality data.

[0518] Step 2:

[0519] The device sends the temporarily saved personality data to the server. The server receives this data and analyzes the user's MBTI type. The input is the personality data sent from the device, and the output is the analyzed MBTI type.

[0520] Step 3:

[0521] The user then proceeds to the job aptitude question section. The user answers a series of job-related questions displayed on the terminal, and the answers are temporarily saved on the terminal. The input is the user's job aptitude answer data, and the output is the temporarily saved job aptitude data.

[0522] Step 4:

[0523] The terminal sends the temporarily stored job aptitude data to the server. The server receives this data, integrates it with the personality data, and begins analysis. The input is the job aptitude data and personality data sent from the terminal, and the output is the integrated data set.

[0524] Step 5:

[0525] The server analyzes the integrated dataset using a generative AI model (e.g., OpenAI GPT-4 SDK). Specifically, it generates ideal team member combinations based on personality data and job aptitude data. The input is the integrated dataset, and the output is the team composition results generated by the analysis.

[0526] Step 6:

[0527] The server sends the generated team composition results to the terminal. The terminal displays the received results to the user. The input is the generated team composition results, and the output is the results displayed to the user.

[0528] Step 7:

[0529] Based on the generated team formation results, the server sends instructions to the robots to optimally match workers and robots. The robots follow the received instructions and begin work. The input is the team formation results, and the output is work instructions for the robots.

[0530] Step 8:

[0531] The robot works with the worker, providing real-time instructions and support as needed. The input is instructions from the server and on-site situation data, and the output is support for the worker and the progress of the work.

[0532] This will enable workers and robots to receive appropriate instructions and support in real time based on the analysis results of personality diagnostic data and job aptitude data, improving work efficiency and the quality of communication within the factory.

[0533] 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.

[0534] The present invention is a system that supports more sophisticated and effective team formation by incorporating user emotion data into analysis in addition to personality assessment and job aptitude data. This system includes personality assessment means, data receiving means, question and answer means, an emotion engine, analysis means, and output means.

[0535] Personality test tools

[0536] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one, which identifies the user's personality type.

[0537] Data Receiving Method

[0538] The device temporarily stores the user's MBTI test answer data, and after answering all questions, sends the data to the server, which receives and analyzes the data as personality data.

[0539] Question answering means

[0540] Next, the user is taken to a detailed job fit question section, where the user again answers job-related questions, which gather data about the user's job fit.

[0541] Emotion Engine

[0542] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine uses voice recognition and image analysis technologies to analyze the user's speech and facial expressions to obtain emotional data.

[0543] Voice Recognition Technology

[0544] The device collects user speech data and performs real-time emotion analysis to identify the user's emotional state.

[0545] Image analysis technology

[0546] The device collects the user's facial expression data and uses image analysis technology to identify emotions, thereby supplementing the user's emotional data.

[0547] Analysis means

[0548] The server integrates the user's personality data, job aptitude data, and emotional data to form a dataset, and then analyzes the dataset using a chat model to generate an ideal team composition, which takes into account the user's personality traits, job aptitude, and emotional state in a comprehensive manner.

[0549] Output Method

[0550] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0551] Specific examples

[0552] For example, when a new project is launched, this system is used to organize a project team. User A and User B access the system and answer MBTI tests and job aptitude questions, respectively. The emotion engine then analyzes the users' speech and facial expressions to obtain emotional data.

[0553] User A is an "ENFJ" with a strong aptitude for project management, and emotional data indicates high motivation. On the other hand, User B is an "ISTP" with a wealth of technical expertise, and emotional data indicates a calm and analytical attitude.

[0554] Based on this information, the server uses a chat model to generate an appropriate team composition. The resulting team composition is "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. This results in the rapid composition of an efficient team with smooth communication.

[0555] This system enables efficient team formation that takes emotional aspects into consideration, especially when creating small teams or starting new projects, and significantly reduces the amount of work required compared to manual team formation.

[0556] The processing flow will be explained below.

[0557] Step 1:

[0558] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions in sequence.

[0559] Step 2:

[0560] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[0561] Step 3:

[0562] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[0563] Step 4:

[0564] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[0565] Step 5:

[0566] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[0567] Step 6:

[0568] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[0569] Step 7:

[0570] While the user is entering or completing the job fit questions, the emotion engine kicks in. The user's speech and facial expressions are collected.

[0571] Step 8:

[0572] The device sends voice data to the emotion engine, which performs real-time emotion analysis to identify the user's emotional state.

[0573] Step 9:

[0574] The device uses a camera to send the user's facial expression data to the emotion engine, which then analyzes the image to obtain the user's emotion data.

[0575] Step 10:

[0576] The device temporarily stores the emotion data and then transmits it to the server.

[0577] Step 11:

[0578] The server integrates the user's personality data, job aptitude data, and emotional data, and the integrated data set comprehensively represents each user's personality traits, job aptitudes, and emotional state.

[0579] Step 12:

[0580] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[0581] Step 13:

[0582] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[0583] Step 14:

[0584] The server transmits the generated team formation results to each user's terminal.

[0585] Step 15:

[0586] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[0587] In this way, effective team formation is achieved through specific actions at each step.

[0588] Example 2

[0589] 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."

[0590] Conventional team formation systems only consider users' personality traits and job aptitudes, ignoring emotional data, and therefore do not adequately consider actual team performance or the quality of communication. This often leads to a lack of cooperation among team members and inappropriate division of roles, making it difficult to efficiently form new projects or small teams.

[0591] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a personality diagnosis means for conducting a personality diagnosis, a question answering means for answering questions about job aptitude, and an emotion engine for acquiring emotion data. This enables ideal team formation that comprehensively considers personality traits, job aptitude, and emotional state.

[0592] A "personality assessment means" is a device or software for assessing a user's personality traits.

[0593] The "data receiving means" is a device or software for receiving personality data, job aptitude data, and emotion data.

[0594] The "question answering means" is a device or software that allows a user to answer questions about job suitability.

[0595] An "emotion engine" is a device or software that analyzes a user's speech and facial expressions to obtain emotional data.

[0596] The "analysis means" is a device or software for integrating and analyzing personality data, job aptitude data, and emotional data to generate an ideal team formation result.

[0597] The "output means" is a device or software for outputting the team formation results generated by the analysis means to the user.

[0598] A "generative AI model" is an artificial intelligence model that analyzes data and generates ideal team composition results.

[0599] A "prompt" is an instruction for inputting data into a generative AI model.

[0600] This invention provides a system for supporting more sophisticated and effective team formation by integrating user personality traits, job aptitudes, and emotional data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotional engine, an analysis unit, and an output unit.

[0601] Personality test tools

[0602] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions in sequence, which identifies their personality type.

[0603] Data Receiving Method

[0604] The device temporarily stores the user's MBTI diagnostic data, and after answering all questions, sends the data to the server, which receives the data as personality data and stores it in a database.

[0605] Question answering means

[0606] The user then goes to the job suitability question section on the terminal and answers the job-related questions. The terminal also temporarily stores these answer data, and after all questions have been answered, sends the data to the server. The server receives this data as job suitability data and stores it in a database.

[0607] Emotion Engine

[0608] The device uses voice recognition and image analysis technologies to collect the user's speech and facial expression data, thereby analyzing the user's emotional state in real time and obtaining emotional data. For example, the device may use the user's microphone to collect speech data and the built-in camera to capture facial expression data.

[0609] Analysis means

[0610] The server combines the received personality data, job aptitude data, and emotion data to form a single dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition results.

[0611] Specifically, the server inputs the following prompt sentence into the generative AI model:

[0612] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[0613] User A:

[0614] Personality data: ENFJ

[0615] Job aptitude data: Strong aptitude for project management

[0616] Emotional data: High motivation

[0617] User B:

[0618] Personality data: ISTP

[0619] Job Qualifications: Strong technical expertise

[0620] Emotional data: Calm and analytical attitude

[0621] Output Method

[0622] The server sends the generated team formation results to each user's device. The device receives the results and displays them to the user. For example, if User A is recommended as the project leader and User B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0623] This system enables efficient team formation that comprehensively considers personality traits, job aptitude, and emotional state, significantly reducing the amount of work required compared to manual team formation, especially when creating small teams or launching new projects.

[0624] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0625] Processing Steps

[0626] Step 1: Start the personality test

[0627] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions one by one.

[0628] Input: The URL the user visits and the question they are asked

[0629] Output: User's answer

[0630] Specific operation: The user launches a browser, accesses the specified URL, and clicks the "Start" button to begin answering the questions.

[0631] Step 2: Submit your personality test data

[0632] The device temporarily stores the user's MBTI diagnostic answers, and after answering all questions, sends the data to the server, which receives this data as personality data and stores it in a database.

[0633] Input: Answers to all user questions

[0634] Output: Personality data stored in a database

[0635] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends this data in JSON format to the server, and the server stores the received data in the database.

[0636] Step 3: Begin the job qualification questions

[0637] The user navigates to the job suitability question section on the terminal and answers the questions displayed to collect job suitability data.

[0638] Input: Job Qualification Questions

[0639] Output: User's answer

[0640] Specific Actions: A user opens the Job Qualification Questions section and clicks the "Start" button to begin answering the questions.

[0641] Step 4: Submit your job suitability data

[0642] The terminal temporarily stores the answer data for the job aptitude questions, and after answering all the questions, transmits it to the server. The server receives this as job aptitude data and stores it in a database.

[0643] Input: Answers to all user questions

[0644] Output: Job suitability data stored in a database

[0645] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends the answer data in JSON format to the server, which then receives the data and stores it in the database.

[0646] Step 5: Collecting emotion data

[0647] The device uses voice recognition and image analysis technologies to collect data on the user's speech and facial expressions, thereby analyzing the user's emotional state in real time and obtaining emotional data.

[0648] Input: User's speech and facial expression data

[0649] Output: Parsed emotion data

[0650] Specific behavior:

[0651] Voice recognition technology: Collects user speech using the device's built-in microphone and analyzes it in real time.

[0652] Image analysis technology: Captures and analyzes the user's facial expressions using the device's built-in camera.

[0653] Step 6: Data synthesis and analysis

[0654] The server creates a dataset that integrates personality data, job aptitude data, and emotional data, and analyzes it using a generative AI model (e.g., GPT-4).

[0655] Input: personality data, job aptitude data, emotional data

[0656] Output: Analysis results (ideal team composition)

[0657] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[0658] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[0659] User A:

[0660] Personality data: ENFJ

[0661] Job aptitude data: Strong aptitude for project management

[0662] Emotional data: High motivation

[0663] User B:

[0664] Personality data: ISTP

[0665] Job Qualifications: Strong technical expertise

[0666] Emotional data: Calm and analytical attitude

[0667] Step 7: Generate team formation results

[0668] Based on the data analyzed by the generative AI model, the server generates the optimal team composition results.

[0669] Input: Analysis results of the generative AI model

[0670] Output: Team composition results

[0671] Specific operation: The generated team composition results are stored in an internal data structure such as "Team A: User A (project leader), User B (technical leader)".

[0672] Step 8: Viewing the results

[0673] The server transmits the generated team formation results to each user's terminal, which receives the results and displays them to the user.

[0674] Input: Generated team composition results

[0675] Output: Team formation results displayed to the user

[0676] Specific operation: The server sends the team formation results in JSON format to the user's device, which then parses the results and displays them in the browser. For example, it displays "Team A: User A (Project Leader), User B (Technical Leader)."

[0677] (Application example 2)

[0678] 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."

[0679] Conventional team formation systems have relied solely on analysis based on personality data and job aptitude data, but because they do not take emotional data into account, the accuracy of the team combinations is insufficient, making it difficult to maximize performance in actual work. Furthermore, there is a lack of tools for quickly and effectively forming teams, a problem that is particularly pronounced in environments where immediate response is required, such as factories. It is necessary to develop a system that can output real-time analysis results using wearable devices such as smart glasses.

[0680] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0681] In this invention, the server includes a personality assessment unit, a question and answer unit regarding job aptitude, an emotion engine that analyzes the user's voice and facial expressions to acquire emotion data, an analysis unit, and an output unit. This allows for an integrated analysis of personality data, job aptitude data, and emotion data, enabling the generation of ideal team member combinations. Furthermore, analysis results can be presented to employees in real time using wearable devices such as smart glasses, enabling immediate response on the factory floor.

[0682] The "personality assessment means" is a function for providing questions and tests to assess the user's personality traits and obtaining the results as data.

[0683] The "data receiving means" is a function for receiving data obtained from the personality diagnosis means and question and answer means and transmitting the data to the server.

[0684] The "question answering means" is a function that provides an interface for the user to answer questions about job aptitude and acquires the answers as data.

[0685] An "emotion engine" is a system that incorporates technology to analyze a user's voice and facial expressions and identify their emotional state.

[0686] The "analysis means" is a function for comprehensively analyzing the received personality data, job aptitude data, and emotional data to generate an ideal team composition.

[0687] The "output means" is a function for presenting the team formation results generated by the analysis means to the user.

[0688] A "generative AI model" is a trained artificial intelligence model used to analyze data and optimize team composition.

[0689] A "prompt sentence" is an input sentence given when using a generative AI model, and is a sentence that instructs specific analysis and generation.

[0690] The present invention provides a system for supporting team formation by analyzing personality assessment, job aptitude data, and emotion data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotion engine, an analysis unit, and an output unit.

[0691] System configuration

[0692] 1. Personality test:

[0693] The user wears the smart glasses and launches the application. The MBTI diagnosis is displayed on the screen, and the user answers the questions by voice. The answer data is temporarily stored in the smart glasses and then sent to the server.

[0694] 2. Question answering method:

[0695] Once the personality test is complete, the user proceeds to the job aptitude section, where questions are similarly displayed and answered verbally. This data is also sent to the server.

[0696] 3. Emotion Engine:

[0697] The camera on the smart glasses analyzes the user's facial expressions in real time and collects emotion data using speech recognition software (e.g., Google Cloud Speech-to-Text API). The emotion engine analyzes the emotion data using OpenCV and TensorFlow.

[0698] 4. Data Receiving Method:

[0699] The server receives and centrally manages the personality data, job aptitude data, and emotion data, which are then processed in an integrated manner by the analysis means.

[0700] 5. Analysis methods:

[0701] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition.

[0702] 6. Output Method:

[0703] The analysis results are displayed in real time on the smart glasses display, allowing users to quickly check the team composition results.

[0704] Specific examples

[0705] When a new production line is introduced, factory employees use smart glasses to access the system. Employee A is identified as an ESTJ in the MBTI test, and a job aptitude test indicates a high aptitude for line supervisors. Emotional data indicates high levels of concentration and judgment. Employee B is identified as an INFP, with a high aptitude for quality control, and is also relaxed and cooperative.

[0706] Based on this information, the server uses a generative AI model (GPT-4) to optimally organize the team. The smart glasses display "New production line team: Employee A (line supervisor), Employee B (quality control)."

[0707] Prompt Sentence Examples

[0708] Here are some example prompts to input to a generative AI model:

[0709] "Please suggest the optimal team composition based on the following personality test, job aptitude data, and emotional data."

[0710] In this way, the personality data, job aptitude data, and emotional data are analyzed comprehensively to realize an optimal team composition.

[0711] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0712] Step 1:

[0713] The user starts the smart glasses and starts the personality test (MBTI test). The personality test questions are displayed on the smart glasses' display, and the user answers by voice. The device collects and temporarily stores the user's answer data.

[0714] Input: User's voice responses to personality questions

[0715] Data processing: Converts voice data into text data and temporarily saves it as response data

[0716] Output: Temporarily saved personality test answer data

[0717] Step 2:

[0718] When the personality test is completed, the device sends the answer data to the server, which stores the received data as personality data.

[0719] Input: Temporarily saved personality test answer data

[0720] Data processing: Response data is sent to the server and saved as personality data

[0721] Output: Personality data stored on the server

[0722] Step 3:

[0723] After the personality test, the user proceeds to the job aptitude question section. Questions are displayed on the device's display, and the user answers by voice. The device collects and temporarily stores the answer data on job aptitude.

[0724] Input: User's spoken responses to job qualification questions

[0725] Data processing: Converts voice data into text data and temporarily saves it as response data

[0726] Output: Temporarily saved job aptitude answer data

[0727] Step 4:

[0728] When the job aptitude question session is completed, the terminal transmits the job aptitude answer data to the server, which stores the received data as job aptitude data.

[0729] Input: Temporarily saved job aptitude answer data

[0730] Data processing: The response data is sent to the server and saved as job aptitude data.

[0731] Output: Job suitability data stored on the server

[0732] Step 5:

[0733] Once the user completes the personality and job aptitude tests, the emotion engine is activated. The device collects the user's voice and facial expressions in real time. The emotion data is analyzed using speech recognition software (Google Cloud Speech-to-Text API) and image analysis software (OpenCV, TensorFlow) and sent to the server.

[0734] Input: User's voice and facial expression data

[0735] Data processing: Analyze voice data and recognize emotional states (using Google Cloud Speech-to-Text API). Analyze facial expression data and recognize emotional states (using OpenCV and TensorFlow). Integrate as emotional data.

[0736] Output: Parsed emotion data

[0737] Step 6:

[0738] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (GPT-4) to generate ideal team compositions.

[0739] Input: personality data, job aptitude data, emotional data

[0740] Data processing: Integrate data to form a dataset, analyze the dataset using a generative AI model (GPT-4), and generate the ideal team composition.

[0741] Output: Generated team composition results

[0742] Step 7:

[0743] The server transmits the generated team formation results to the terminal, which then displays the results in real time on the display of the smart glasses for the user.

[0744] Input: Generated team composition results

[0745] Data processing: Team formation results are sent to the device and displayed on the screen

[0746] Output: Team formation results displayed on the smart glasses display

[0747] 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.

[0748] 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.

[0749] 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.

[0750] [Third embodiment]

[0751] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0752] 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.

[0753] 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).

[0754] 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.

[0755] 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.

[0756] 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).

[0757] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0758] 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.

[0759] 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.

[0760] 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.

[0761] 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.

[0762] 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."

[0763] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[0764] Personality test tools

[0765] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[0766] Data Receiving Method

[0767] After the user completes the MBTI test, the answer data is sent from the device to the server, which receives this data as personality data and analyzes the user's MBTI type.

[0768] Question answering means

[0769] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[0770] Analysis means

[0771] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a chat model to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality characteristics and job aptitude.

[0772] Output Method

[0773] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0774] Specific examples

[0775] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[0776] The server receives this data and uses a chat model to assign optimal roles to each user. The resulting team formation is "Team A: User A (project leader), User B (technical leader)," and is notified to the users via their devices. In this way, an efficient team with smooth communication is quickly formed.

[0777] This system is particularly effective when creating small teams or starting new projects, significantly reducing the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[0778] The processing flow will be explained below.

[0779] Step 1:

[0780] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one.

[0781] Step 2:

[0782] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[0783] Step 3:

[0784] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[0785] Step 4:

[0786] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[0787] Step 5:

[0788] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[0789] Step 6:

[0790] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[0791] Step 7:

[0792] The server combines the user's personality data and job aptitude data, and this combined data set represents each user's overall personality traits and job aptitudes.

[0793] Step 8:

[0794] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[0795] Step 9:

[0796] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[0797] Step 10:

[0798] The server transmits the generated team formation results to each user's terminal.

[0799] Step 11:

[0800] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[0801] In this way, effective team formation is achieved through specific actions at each step.

[0802] Example 1

[0803] 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."

[0804] Traditional personality tests and job aptitude assessments are often conducted individually, and there is a lack of systems that automatically create ideal team formation through integrated data analysis. Furthermore, manual data analysis and team formation takes time and effort, hindering efficient team building. Furthermore, specialized knowledge is required to identify appropriate role assignments, which makes it difficult to form appropriate teams for small-scale or short-term projects.

[0805] 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.

[0806] In this invention, the server includes: a personality assessment means for conducting a personality assessment; a data receiving means for receiving the personality data acquired by the personality assessment means; a question and answering means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answering means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination; an output means for outputting the team formation results generated by the analysis means; a means for the user to answer the MBTI assessment and job aptitude questions using a terminal; a means for the terminal to transmit the personality data and job aptitude data to the server; and a means for the analysis means to analyze the dataset using a generative AI model. This makes it possible to comprehensively analyze the user's personality assessment data and job aptitude data and automatically generate an optimal team formation and allocation of roles.

[0807] The "personality diagnosis means" is a means for providing an interface and questions for the user to carry out a personality diagnosis, and for obtaining response data.

[0808] The "data receiving means" is a means for receiving data acquired by the personality diagnosis means and the question and answer means and storing the data in a server.

[0809] The "question answering means" is a means for providing an interface and question contents for the user to answer questions about job aptitude, and for acquiring answer data.

[0810] The "analysis means" is a means for comprehensively analyzing the received personality data and job aptitude data to generate an ideal combination of team members.

[0811] The "output means" is a means for displaying the team formation results generated by the analysis means on the user's terminal.

[0812] "Means for a user to answer MBTI diagnosis and job aptitude questions using a terminal" refers to means for a user to answer questions about MBTI diagnosis and job aptitude via the terminal they use.

[0813] The "means for the terminal to transmit personality data and job aptitude data to the server" refers to the means by which the user's terminal encrypts the personality assessment data and job aptitude data and transmits them to the server.

[0814] "Means for analyzing a dataset using a generative AI model" means means for the analysis means to use a generative AI model (e.g., a large-scale language model) to analyze the received dataset and calculate an ideal team composition.

[0815] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[0816] Personality test tools

[0817] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[0818] Data Receiving Method

[0819] After the user completes the MBTI test, the answer data is sent from the device to the server. The server receives this data as personality data and analyzes the user's MBTI type. For example, the server performs the analysis using an MBTI analysis library implemented in Python.

[0820] Question answering means

[0821] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[0822] Analysis means

[0823] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a generative AI model (e.g., ChatGPT) to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality traits and job aptitude.

[0824] Output Method

[0825] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0826] Specific examples

[0827] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[0828] The server receives this data and uses a generative AI model to assign the optimal role to each user. The generated team formation results in "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. In this way, an efficient team with smooth communication is quickly formed. This system is particularly effective when creating small teams or launching new projects, and can significantly reduce the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[0829] Prompt Sentence Examples

[0830] "Based on the results of the MBTI diagnosis, please generate the optimal team composition for User A, an ENFJ type with strong project management skills, and User B, an ISTP type with extensive technical expertise."

[0831] By inputting this prompt into the generative AI model, the system will begin the process of forming a team and proposing an ideal division of roles.

[0832] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0833] Step 1:

[0834] A user accesses the system using a terminal. Input includes the user accessing the system's URL through a web browser. Output includes the display of an MBTI diagnostic interface. Specifically, the terminal displays the MBTI diagnostic interface received from the server to the user. The user answers a series of questions, and the answer data is temporarily stored in the terminal's local storage.

[0835] Step 2:

[0836] After the device completes the MBTI diagnosis, it sends the user's answer data to the server. The input includes the user's MBTI diagnosis answer data. The output is personality data, which is stored on the server. Specifically, the device encrypts the answer data and sends it to the server, which then receives it and stores it in a database.

[0837] Step 3:

[0838] The server analyzes the received personality data. The input includes the personality data received by the server. The output is the analyzed user's MBTI type. Specifically, the server uses an MBTI analysis library implemented in Python to identify the user's MBTI type.

[0839] Step 4:

[0840] The user proceeds to the detailed job aptitude question section. The input includes the user proceeding to the next section. The output includes the job aptitude question interface being displayed on the terminal. As a specific operation, the terminal displays the job aptitude question interface received from the server, and the user answers it.

[0841] Step 5:

[0842] After the user answers the job aptitude questions, the terminal transmits the answer data to the server. The input includes the user's job aptitude question answer data. The output is the job aptitude data stored on the server. Specifically, the terminal encrypts the answer data and transmits it to the server, which receives it and stores it in a database.

[0843] Step 6:

[0844] The server integrates the personality data and the job aptitude data. The input includes the personality data and the job aptitude data. The output is an integrated data set. Specifically, the server retrieves the personality data and the job aptitude data from the database and integrates them.

[0845] Step 7:

[0846] The server analyzes the integrated dataset using a generative AI model. The input includes the integrated dataset. The output generates the ideal team composition. Specifically, the server inputs a prompt sentence into the generative AI model (e.g., ChatGPT) and obtains the analysis result.

[0847] Step 8:

[0848] The server transmits the generated team formation result to the terminal. The team formation result is included as an input. The team formation result is displayed on the terminal as an output. In specific operations, the server transmits the team formation result to the terminal, and the terminal displays it to the user.

[0849] (Application example 1)

[0850] 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."

[0851] In factories, there is a need to improve work efficiency by optimizing the collaboration between workers and robots. However, currently, teams are not formed based on the personality and job aptitude of workers, which results in inefficient work. Furthermore, even when starting up a new production line, there is a lack of means to quickly form optimal teams. This leads to reduced work efficiency and miscommunication.

[0852] 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.

[0853] In this invention, the server includes: a personality diagnosis means for conducting a personality diagnosis; a data receiving means for receiving personality data acquired by the personality diagnosis means; a question and answer means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answer means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate ideal team member combinations; an output means for outputting the team formation results generated by the analysis means; a means for optimizing team formation of workers and robots based on the team formation results generated by the output means to improve factory work efficiency; and a means for providing work instructions and support to each worker and each robot in real time, thereby making it possible to maximize the capabilities of the workers and robots and improve work efficiency and the quality of communication.

[0854] "Personality assessment means" refers to a method or device used by a user to understand their own personality traits, including MBTI assessments.

[0855] The "data receiving means" refers to a method or device for acquiring and recording data obtained from the personality diagnosis means and question and answer means.

[0856] A "question answering means" is a method or device used by a user to answer questions about job suitability.

[0857] "Job aptitude data" is data indicating how suited a user is to a job, and is acquired through the question and answer means.

[0858] The "analysis means" refers to a method or device for integrating acquired personality data and job aptitude data to generate ideal team member combinations.

[0859] The "output means" refers to a method or device for providing the team formation results generated by the analysis means to the user.

[0860] "Workers" are the human workers who actually perform the work in the factory.

[0861] A "robot" is a mechanical device used to assist or perform factory work.

[0862] An "optimizer" is a method or device used to optimize the teaming of workers and robots.

[0863] "Means for providing in real time" refers to a method or device for providing immediate work instructions and support to workers and robots.

[0864] The following system configuration will be described as an embodiment of the present invention.

[0865] Overall overview

[0866] This is a system that optimizes the team composition of workers and robots to efficiently carry out factory work. This system is composed of a user terminal, a server, and a robot, and includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, an output means, an optimization means, and a real-time instruction means.

[0867] User terminal

[0868] The user terminal is a device such as a tablet or smartphone. First, the user accesses the personality diagnosis interface using the user terminal and performs the MBTI diagnosis. Next, the user answers questions about job suitability. These diagnosis results are temporarily stored on the user terminal and then sent to the server.

[0869] server

[0870] The server receives the personality assessment data and job aptitude data and performs an integrated analysis of them. A generative AI model (e.g., OpenAI GPT-4 SDK) is used for the analysis. Based on the analysis results, the server generates an ideal team composition of workers and robots. This generated result is then sent back to the user's device and displayed to the user.

[0871] robot

[0872] Robots installed in factories work with the most suitable workers based on the team formation results sent from the server, and have the ability to provide work instructions and support in real time.

[0873] Specific examples

[0874] When starting up a new production line in a factory, Worker A and Worker B access the system. An MBTI diagnosis reveals that Worker A has the personality traits of "ENTJ" and excels in leadership. Worker B has the personality traits of "ISFP" and excels in attention to detail. The server analyzes this data and determines that it would be best to pair Worker A with Robot R2 (with heavy load transport capabilities) and Worker B with Robot R1 (with precision work support capabilities).

[0875] As a result, worker A works with R2 to manage route planning and efficient transportation, while worker B works with R1 to perform precise assembly work. In this way, optimal teaming of workers and robots is quickly achieved, significantly improving the efficiency and accuracy of the production line.

[0876] Prompt Sentence Examples

[0877] "Form optimal teaming of factory workers and robots. Generate optimal worker-robot pairs based on the provided MBTI diagnostic data and job aptitude data."

[0878] In this way, by providing appropriate instructions and support to workers and robots in real time based on the analysis results of personality diagnostic data and job aptitude data, it is possible to improve work efficiency and the quality of communication within the factory.

[0879] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0880] Step 1:

[0881] The user accesses the personality diagnosis interface using a device (tablet or smartphone) and conducts the MBTI diagnosis. The user answers a series of questions, and the answer data is temporarily saved on the device. The input is the user's answer data, and the output is the temporarily saved personality data.

[0882] Step 2:

[0883] The device sends the temporarily saved personality data to the server. The server receives this data and analyzes the user's MBTI type. The input is the personality data sent from the device, and the output is the analyzed MBTI type.

[0884] Step 3:

[0885] The user then proceeds to the job aptitude question section. The user answers a series of job-related questions displayed on the terminal, and the answers are temporarily saved on the terminal. The input is the user's job aptitude answer data, and the output is the temporarily saved job aptitude data.

[0886] Step 4:

[0887] The terminal sends the temporarily stored job aptitude data to the server. The server receives this data, integrates it with the personality data, and begins analysis. The input is the job aptitude data and personality data sent from the terminal, and the output is the integrated data set.

[0888] Step 5:

[0889] The server analyzes the integrated dataset using a generative AI model (e.g., OpenAI GPT-4 SDK). Specifically, it generates ideal team member combinations based on personality data and job aptitude data. The input is the integrated dataset, and the output is the team composition results generated by the analysis.

[0890] Step 6:

[0891] The server sends the generated team composition results to the terminal. The terminal displays the received results to the user. The input is the generated team composition results, and the output is the results displayed to the user.

[0892] Step 7:

[0893] Based on the generated team formation results, the server sends instructions to the robots to optimally match workers and robots. The robots follow the received instructions and begin work. The input is the team formation results, and the output is work instructions for the robots.

[0894] Step 8:

[0895] The robot works with the worker, providing real-time instructions and support as needed. The input is instructions from the server and on-site situation data, and the output is support for the worker and the progress of the work.

[0896] This will enable workers and robots to receive appropriate instructions and support in real time based on the analysis results of personality diagnostic data and job aptitude data, improving work efficiency and the quality of communication within the factory.

[0897] 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.

[0898] The present invention is a system that supports more sophisticated and effective team formation by incorporating user emotion data into analysis in addition to personality assessment and job aptitude data. This system includes personality assessment means, data receiving means, question and answer means, an emotion engine, analysis means, and output means.

[0899] Personality test tools

[0900] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one, which identifies the user's personality type.

[0901] Data Receiving Method

[0902] The device temporarily stores the user's MBTI test answer data, and after answering all questions, sends the data to the server, which receives and analyzes the data as personality data.

[0903] Question answering means

[0904] Next, the user is taken to a detailed job fit question section, where the user again answers job-related questions, which gather data about the user's job fit.

[0905] Emotion Engine

[0906] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine uses voice recognition and image analysis technologies to analyze the user's speech and facial expressions to obtain emotional data.

[0907] Voice Recognition Technology

[0908] The device collects user speech data and performs real-time emotion analysis to identify the user's emotional state.

[0909] Image analysis technology

[0910] The device collects the user's facial expression data and uses image analysis technology to identify emotions, thereby supplementing the user's emotional data.

[0911] Analysis means

[0912] The server integrates the user's personality data, job aptitude data, and emotional data to form a dataset, and then analyzes the dataset using a chat model to generate an ideal team composition, which takes into account the user's personality traits, job aptitude, and emotional state in a comprehensive manner.

[0913] Output Method

[0914] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0915] Specific examples

[0916] For example, when a new project is launched, this system is used to organize a project team. User A and User B access the system and answer MBTI tests and job aptitude questions, respectively. The emotion engine then analyzes the users' speech and facial expressions to obtain emotional data.

[0917] User A is an "ENFJ" with a strong aptitude for project management, and emotional data indicates high motivation. On the other hand, User B is an "ISTP" with a wealth of technical expertise, and emotional data indicates a calm and analytical attitude.

[0918] Based on this information, the server uses a chat model to generate an appropriate team composition. The resulting team composition is "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. This results in the rapid composition of an efficient team with smooth communication.

[0919] This system enables efficient team formation that takes emotional aspects into consideration, especially when creating small teams or starting new projects, and significantly reduces the amount of work required compared to manual team formation.

[0920] The processing flow will be explained below.

[0921] Step 1:

[0922] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions in sequence.

[0923] Step 2:

[0924] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[0925] Step 3:

[0926] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[0927] Step 4:

[0928] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[0929] Step 5:

[0930] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[0931] Step 6:

[0932] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[0933] Step 7:

[0934] While the user is entering or completing the job fit questions, the emotion engine kicks in. The user's speech and facial expressions are collected.

[0935] Step 8:

[0936] The device sends voice data to the emotion engine, which performs real-time emotion analysis to identify the user's emotional state.

[0937] Step 9:

[0938] The device uses a camera to send the user's facial expression data to the emotion engine, which then analyzes the image to obtain the user's emotion data.

[0939] Step 10:

[0940] The device temporarily stores the emotion data and then transmits it to the server.

[0941] Step 11:

[0942] The server integrates the user's personality data, job aptitude data, and emotional data, and the integrated data set comprehensively represents each user's personality traits, job aptitudes, and emotional state.

[0943] Step 12:

[0944] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[0945] Step 13:

[0946] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[0947] Step 14:

[0948] The server transmits the generated team formation results to each user's terminal.

[0949] Step 15:

[0950] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[0951] In this way, effective team formation is achieved through specific actions at each step.

[0952] Example 2

[0953] 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."

[0954] Conventional team formation systems only consider users' personality traits and job aptitudes, ignoring emotional data, and therefore do not adequately consider actual team performance or the quality of communication. This often leads to a lack of cooperation among team members and inappropriate division of roles, making it difficult to efficiently form new projects or small teams.

[0955] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a personality diagnosis means for conducting a personality diagnosis, a question answering means for answering questions about job aptitude, and an emotion engine for acquiring emotion data. This enables ideal team formation that comprehensively considers personality traits, job aptitude, and emotional state.

[0956] A "personality assessment means" is a device or software for assessing a user's personality traits.

[0957] The "data receiving means" is a device or software for receiving personality data, job aptitude data, and emotion data.

[0958] The "question answering means" is a device or software that allows a user to answer questions about job suitability.

[0959] An "emotion engine" is a device or software that analyzes a user's speech and facial expressions to obtain emotional data.

[0960] The "analysis means" is a device or software for integrating and analyzing personality data, job aptitude data, and emotional data to generate an ideal team formation result.

[0961] The "output means" is a device or software for outputting the team formation results generated by the analysis means to the user.

[0962] A "generative AI model" is an artificial intelligence model that analyzes data and generates ideal team composition results.

[0963] A "prompt" is an instruction for inputting data into a generative AI model.

[0964] This invention provides a system for supporting more sophisticated and effective team formation by integrating user personality traits, job aptitudes, and emotional data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotional engine, an analysis unit, and an output unit.

[0965] Personality test tools

[0966] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions in sequence, which identifies their personality type.

[0967] Data Receiving Method

[0968] The device temporarily stores the user's MBTI diagnostic data, and after answering all questions, sends the data to the server, which receives the data as personality data and stores it in a database.

[0969] Question answering means

[0970] The user then goes to the job suitability question section on the terminal and answers the job-related questions. The terminal also temporarily stores these answer data, and after all questions have been answered, sends the data to the server. The server receives this data as job suitability data and stores it in a database.

[0971] Emotion Engine

[0972] The device uses voice recognition and image analysis technologies to collect the user's speech and facial expression data, thereby analyzing the user's emotional state in real time and obtaining emotional data. For example, the device may use the user's microphone to collect speech data and the built-in camera to capture facial expression data.

[0973] Analysis means

[0974] The server combines the received personality data, job aptitude data, and emotion data to form a single dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition results.

[0975] Specifically, the server inputs the following prompt sentence into the generative AI model:

[0976] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[0977] User A:

[0978] Personality data: ENFJ

[0979] Job aptitude data: Strong aptitude for project management

[0980] Emotional data: High motivation

[0981] User B:

[0982] Personality data: ISTP

[0983] Job Qualifications: Strong technical expertise

[0984] Emotional data: Calm and analytical attitude

[0985] Output Method

[0986] The server sends the generated team formation results to each user's device. The device receives the results and displays them to the user. For example, if User A is recommended as the project leader and User B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[0987] This system enables efficient team formation that comprehensively considers personality traits, job aptitude, and emotional state, significantly reducing the amount of work required compared to manual team formation, especially when creating small teams or launching new projects.

[0988] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0989] Processing Steps

[0990] Step 1: Start the personality test

[0991] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions one by one.

[0992] Input: The URL the user visits and the question they are asked

[0993] Output: User's answer

[0994] Specific operation: The user launches a browser, accesses the specified URL, and clicks the "Start" button to begin answering the questions.

[0995] Step 2: Submit your personality test data

[0996] The device temporarily stores the user's MBTI diagnostic answers, and after answering all questions, sends the data to the server, which receives this data as personality data and stores it in a database.

[0997] Input: Answers to all user questions

[0998] Output: Personality data stored in a database

[0999] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends this data in JSON format to the server, and the server stores the received data in the database.

[1000] Step 3: Begin the job qualification questions

[1001] The user navigates to the job suitability question section on the terminal and answers the questions displayed to collect job suitability data.

[1002] Input: Job Qualification Questions

[1003] Output: User's answer

[1004] Specific Actions: A user opens the Job Qualification Questions section and clicks the "Start" button to begin answering the questions.

[1005] Step 4: Submit your job suitability data

[1006] The terminal temporarily stores the answer data for the job aptitude questions, and after answering all the questions, transmits it to the server. The server receives this as job aptitude data and stores it in a database.

[1007] Input: Answers to all user questions

[1008] Output: Job suitability data stored in a database

[1009] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends the answer data in JSON format to the server, which then receives the data and stores it in the database.

[1010] Step 5: Collecting emotion data

[1011] The device uses voice recognition and image analysis technologies to collect data on the user's speech and facial expressions, thereby analyzing the user's emotional state in real time and obtaining emotional data.

[1012] Input: User's speech and facial expression data

[1013] Output: Parsed emotion data

[1014] Specific behavior:

[1015] Voice recognition technology: Collects user speech using the device's built-in microphone and analyzes it in real time.

[1016] Image analysis technology: Captures and analyzes the user's facial expressions using the device's built-in camera.

[1017] Step 6: Data synthesis and analysis

[1018] The server creates a dataset that integrates personality data, job aptitude data, and emotional data, and analyzes it using a generative AI model (e.g., GPT-4).

[1019] Input: personality data, job aptitude data, emotional data

[1020] Output: Analysis results (ideal team composition)

[1021] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[1022] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[1023] User A:

[1024] Personality data: ENFJ

[1025] Job aptitude data: Strong aptitude for project management

[1026] Emotional data: High motivation

[1027] User B:

[1028] Personality data: ISTP

[1029] Job Qualifications: Strong technical expertise

[1030] Emotional data: Calm and analytical attitude

[1031] Step 7: Generate team formation results

[1032] Based on the data analyzed by the generative AI model, the server generates the optimal team composition results.

[1033] Input: Analysis results of the generative AI model

[1034] Output: Team composition results

[1035] Specific operation: The generated team composition results are stored in an internal data structure such as "Team A: User A (project leader), User B (technical leader)".

[1036] Step 8: Viewing the results

[1037] The server transmits the generated team formation results to each user's terminal, which receives the results and displays them to the user.

[1038] Input: Generated team composition results

[1039] Output: Team formation results displayed to the user

[1040] Specific operation: The server sends the team formation results in JSON format to the user's device, which then parses the results and displays them in the browser. For example, it displays "Team A: User A (Project Leader), User B (Technical Leader)."

[1041] (Application example 2)

[1042] 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."

[1043] Conventional team formation systems have relied solely on analysis based on personality data and job aptitude data, but because they do not take emotional data into account, the accuracy of the team combinations is insufficient, making it difficult to maximize performance in actual work. Furthermore, there is a lack of tools for quickly and effectively forming teams, a problem that is particularly pronounced in environments where immediate response is required, such as factories. It is necessary to develop a system that can output real-time analysis results using wearable devices such as smart glasses.

[1044] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1045] In this invention, the server includes a personality assessment unit, a question and answer unit regarding job aptitude, an emotion engine that analyzes the user's voice and facial expressions to acquire emotion data, an analysis unit, and an output unit. This allows for an integrated analysis of personality data, job aptitude data, and emotion data, enabling the generation of ideal team member combinations. Furthermore, analysis results can be presented to employees in real time using wearable devices such as smart glasses, enabling immediate response on the factory floor.

[1046] The "personality assessment means" is a function for providing questions and tests to assess the user's personality traits and obtaining the results as data.

[1047] The "data receiving means" is a function for receiving data obtained from the personality diagnosis means and question and answer means and transmitting the data to the server.

[1048] The "question answering means" is a function that provides an interface for the user to answer questions about job aptitude and acquires the answers as data.

[1049] An "emotion engine" is a system that incorporates technology to analyze a user's voice and facial expressions and identify their emotional state.

[1050] The "analysis means" is a function for comprehensively analyzing the received personality data, job aptitude data, and emotional data to generate an ideal team composition.

[1051] The "output means" is a function for presenting the team formation results generated by the analysis means to the user.

[1052] A "generative AI model" is a trained artificial intelligence model used to analyze data and optimize team composition.

[1053] A "prompt sentence" is an input sentence given when using a generative AI model, and is a sentence that instructs specific analysis and generation.

[1054] The present invention provides a system for supporting team formation by analyzing personality assessment, job aptitude data, and emotion data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotion engine, an analysis unit, and an output unit.

[1055] System configuration

[1056] 1. Personality test:

[1057] The user wears the smart glasses and launches the application. The MBTI diagnosis is displayed on the screen, and the user answers the questions by voice. The answer data is temporarily stored in the smart glasses and then sent to the server.

[1058] 2. Question answering method:

[1059] Once the personality test is complete, the user proceeds to the job aptitude section, where questions are similarly displayed and answered verbally. This data is also sent to the server.

[1060] 3. Emotion Engine:

[1061] The camera on the smart glasses analyzes the user's facial expressions in real time and collects emotion data using speech recognition software (e.g., Google Cloud Speech-to-Text API). The emotion engine analyzes the emotion data using OpenCV and TensorFlow.

[1062] 4. Data Receiving Method:

[1063] The server receives and centrally manages the personality data, job aptitude data, and emotion data, which are then processed in an integrated manner by the analysis means.

[1064] 5. Analysis methods:

[1065] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition.

[1066] 6. Output Method:

[1067] The analysis results are displayed in real time on the smart glasses display, allowing users to quickly check the team composition results.

[1068] Specific examples

[1069] When a new production line is introduced, factory employees use smart glasses to access the system. Employee A is identified as an ESTJ in the MBTI test, and a job aptitude test indicates a high aptitude for line supervisors. Emotional data indicates high levels of concentration and judgment. Employee B is identified as an INFP, with a high aptitude for quality control, and is also relaxed and cooperative.

[1070] Based on this information, the server uses a generative AI model (GPT-4) to optimally organize the team. The smart glasses display "New production line team: Employee A (line supervisor), Employee B (quality control)."

[1071] Prompt Sentence Examples

[1072] Here are some example prompts to input to a generative AI model:

[1073] "Please suggest the optimal team composition based on the following personality test, job aptitude data, and emotional data."

[1074] In this way, the personality data, job aptitude data, and emotional data are analyzed comprehensively to realize an optimal team composition.

[1075] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1076] Step 1:

[1077] The user starts the smart glasses and starts the personality test (MBTI test). The personality test questions are displayed on the smart glasses' display, and the user answers by voice. The device collects and temporarily stores the user's answer data.

[1078] Input: User's voice responses to personality questions

[1079] Data processing: Converts voice data into text data and temporarily saves it as response data

[1080] Output: Temporarily saved personality test answer data

[1081] Step 2:

[1082] When the personality test is completed, the device sends the answer data to the server, which stores the received data as personality data.

[1083] Input: Temporarily saved personality test answer data

[1084] Data processing: Response data is sent to the server and saved as personality data

[1085] Output: Personality data stored on the server

[1086] Step 3:

[1087] After the personality test, the user proceeds to the job aptitude question section. Questions are displayed on the device's display, and the user answers by voice. The device collects and temporarily stores the answer data on job aptitude.

[1088] Input: User's spoken responses to job qualification questions

[1089] Data processing: Converts voice data into text data and temporarily saves it as response data

[1090] Output: Temporarily saved job aptitude answer data

[1091] Step 4:

[1092] When the job aptitude question session is completed, the terminal transmits the job aptitude answer data to the server, which stores the received data as job aptitude data.

[1093] Input: Temporarily saved job aptitude answer data

[1094] Data processing: The response data is sent to the server and saved as job aptitude data.

[1095] Output: Job suitability data stored on the server

[1096] Step 5:

[1097] Once the user completes the personality and job aptitude tests, the emotion engine is activated. The device collects the user's voice and facial expressions in real time. The emotion data is analyzed using speech recognition software (Google Cloud Speech-to-Text API) and image analysis software (OpenCV, TensorFlow) and sent to the server.

[1098] Input: User's voice and facial expression data

[1099] Data processing: Analyze voice data and recognize emotional states (using Google Cloud Speech-to-Text API). Analyze facial expression data and recognize emotional states (using OpenCV and TensorFlow). Integrate as emotional data.

[1100] Output: Parsed emotion data

[1101] Step 6:

[1102] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (GPT-4) to generate ideal team compositions.

[1103] Input: personality data, job aptitude data, emotional data

[1104] Data processing: Integrate data to form a dataset, analyze the dataset using a generative AI model (GPT-4), and generate the ideal team composition.

[1105] Output: Generated team composition results

[1106] Step 7:

[1107] The server transmits the generated team formation results to the terminal, which then displays the results in real time on the display of the smart glasses for the user.

[1108] Input: Generated team composition results

[1109] Data processing: Team formation results are sent to the device and displayed on the screen

[1110] Output: Team formation results displayed on the smart glasses display

[1111] 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.

[1112] 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.

[1113] 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.

[1114] [Fourth embodiment]

[1115] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1116] 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.

[1117] 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).

[1118] 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.

[1119] 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.

[1120] 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).

[1121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1122] 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.

[1123] 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.

[1124] 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.

[1125] 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.

[1126] 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.

[1127] 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."

[1128] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[1129] Personality test tools

[1130] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[1131] Data Receiving Method

[1132] After the user completes the MBTI test, the answer data is sent from the device to the server, which receives this data as personality data and analyzes the user's MBTI type.

[1133] Question answering means

[1134] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[1135] Analysis means

[1136] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a chat model to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality characteristics and job aptitude.

[1137] Output Method

[1138] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[1139] Specific examples

[1140] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[1141] The server receives this data and uses a chat model to assign optimal roles to each user. The resulting team formation is "Team A: User A (project leader), User B (technical leader)," and is notified to the users via their devices. In this way, an efficient team with smooth communication is quickly formed.

[1142] This system is particularly effective when creating small teams or starting new projects, significantly reducing the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[1143] The processing flow will be explained below.

[1144] Step 1:

[1145] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one.

[1146] Step 2:

[1147] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[1148] Step 3:

[1149] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[1150] Step 4:

[1151] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[1152] Step 5:

[1153] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[1154] Step 6:

[1155] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[1156] Step 7:

[1157] The server combines the user's personality data and job aptitude data, and this combined data set represents each user's overall personality traits and job aptitudes.

[1158] Step 8:

[1159] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[1160] Step 9:

[1161] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[1162] Step 10:

[1163] The server transmits the generated team formation results to each user's terminal.

[1164] Step 11:

[1165] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[1166] In this way, effective team formation is achieved through specific actions at each step.

[1167] Example 1

[1168] 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."

[1169] Traditional personality tests and job aptitude assessments are often conducted individually, and there is a lack of systems that automatically create ideal team formation through integrated data analysis. Furthermore, manual data analysis and team formation takes time and effort, hindering efficient team building. Furthermore, specialized knowledge is required to identify appropriate role assignments, which makes it difficult to form appropriate teams for small-scale or short-term projects.

[1170] 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.

[1171] In this invention, the server includes: a personality assessment means for conducting a personality assessment; a data receiving means for receiving the personality data acquired by the personality assessment means; a question and answering means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answering means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination; an output means for outputting the team formation results generated by the analysis means; a means for the user to answer the MBTI assessment and job aptitude questions using a terminal; a means for the terminal to transmit the personality data and job aptitude data to the server; and a means for the analysis means to analyze the dataset using a generative AI model. This makes it possible to comprehensively analyze the user's personality assessment data and job aptitude data and automatically generate an optimal team formation and allocation of roles.

[1172] The "personality diagnosis means" is a means for providing an interface and questions for the user to carry out a personality diagnosis, and for obtaining response data.

[1173] The "data receiving means" is a means for receiving data acquired by the personality diagnosis means and the question and answer means and storing the data in a server.

[1174] The "question answering means" is a means for providing an interface and question contents for the user to answer questions about job aptitude, and for acquiring answer data.

[1175] The "analysis means" is a means for comprehensively analyzing the received personality data and job aptitude data to generate an ideal combination of team members.

[1176] The "output means" is a means for displaying the team formation results generated by the analysis means on the user's terminal.

[1177] "Means for a user to answer MBTI diagnosis and job aptitude questions using a terminal" refers to means for a user to answer questions about MBTI diagnosis and job aptitude via the terminal they use.

[1178] The "means for the terminal to transmit personality data and job aptitude data to the server" refers to the means by which the user's terminal encrypts the personality assessment data and job aptitude data and transmits them to the server.

[1179] "Means for analyzing a dataset using a generative AI model" means means for the analysis means to use a generative AI model (e.g., a large-scale language model) to analyze the received dataset and calculate an ideal team composition.

[1180] The present invention is a system for performing a personality diagnosis based on the MBTI diagnosis and supporting ideal team formation based on the results and additional information on job aptitude. This system includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, and an output means.

[1181] Personality test tools

[1182] When a user accesses the system using a terminal, an interface for conducting an MBTI diagnosis is displayed. The user answers a series of questions, and the answers are temporarily saved by the terminal.

[1183] Data Receiving Method

[1184] After the user completes the MBTI test, the answer data is sent from the device to the server. The server receives this data as personality data and analyzes the user's MBTI type. For example, the server performs the analysis using an MBTI analysis library implemented in Python.

[1185] Question answering means

[1186] Next, the user proceeds to a detailed question section regarding job suitability, where the user answers the questions and the answer data is temporarily stored by the terminal and then transmitted to the server.

[1187] Analysis means

[1188] The server integrates the user's personality data and job aptitude data. This creates a dataset that comprehensively considers not only the user's personality type but also job-related characteristics. The server then analyzes this dataset using a generative AI model (e.g., ChatGPT) to generate an ideal team composition. This analysis method aims to stimulate communication and improve team efficiency by optimally combining each user's personality traits and job aptitude.

[1189] Output Method

[1190] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[1191] Specific examples

[1192] For example, imagine a scenario in which this system is used to efficiently organize a project team at the start of a new project. User A and User B access the system and each answer an MBTI diagnosis and job aptitude questions. User A is an "ENFJ" and is found to have a strong aptitude for project management. On the other hand, User B is an "ISTP" and is found to have a wealth of technical expertise.

[1193] The server receives this data and uses a generative AI model to assign the optimal role to each user. The generated team formation results in "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. In this way, an efficient team with smooth communication is quickly formed. This system is particularly effective when creating small teams or launching new projects, and can significantly reduce the amount of work required compared to manual methods. It is also expected to improve team performance and the quality of communication compared to random team formation.

[1194] Prompt Sentence Examples

[1195] "Based on the results of the MBTI diagnosis, please generate the optimal team composition for User A, an ENFJ type with strong project management skills, and User B, an ISTP type with extensive technical expertise."

[1196] By inputting this prompt into the generative AI model, the system will begin the process of forming a team and proposing an ideal division of roles.

[1197] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1198] Step 1:

[1199] A user accesses the system using a terminal. Input includes the user accessing the system's URL through a web browser. Output includes the display of an MBTI diagnostic interface. Specifically, the terminal displays the MBTI diagnostic interface received from the server to the user. The user answers a series of questions, and the answer data is temporarily stored in the terminal's local storage.

[1200] Step 2:

[1201] After the device completes the MBTI diagnosis, it sends the user's answer data to the server. The input includes the user's MBTI diagnosis answer data. The output is personality data, which is stored on the server. Specifically, the device encrypts the answer data and sends it to the server, which then receives it and stores it in a database.

[1202] Step 3:

[1203] The server analyzes the received personality data. The input includes the personality data received by the server. The output is the analyzed user's MBTI type. Specifically, the server uses an MBTI analysis library implemented in Python to identify the user's MBTI type.

[1204] Step 4:

[1205] The user proceeds to the detailed job aptitude question section. The input includes the user proceeding to the next section. The output includes the job aptitude question interface being displayed on the terminal. As a specific operation, the terminal displays the job aptitude question interface received from the server, and the user answers it.

[1206] Step 5:

[1207] After the user answers the job aptitude questions, the terminal transmits the answer data to the server. The input includes the user's job aptitude question answer data. The output is the job aptitude data stored on the server. Specifically, the terminal encrypts the answer data and transmits it to the server, which receives it and stores it in a database.

[1208] Step 6:

[1209] The server integrates the personality data and the job aptitude data. The input includes the personality data and the job aptitude data. The output is an integrated data set. Specifically, the server retrieves the personality data and the job aptitude data from the database and integrates them.

[1210] Step 7:

[1211] The server analyzes the integrated dataset using a generative AI model. The input includes the integrated dataset. The output generates the ideal team composition. Specifically, the server inputs a prompt sentence into the generative AI model (e.g., ChatGPT) and obtains the analysis result.

[1212] Step 8:

[1213] The server transmits the generated team formation result to the terminal. The team formation result is included as an input. The team formation result is displayed on the terminal as an output. In specific operations, the server transmits the team formation result to the terminal, and the terminal displays it to the user.

[1214] (Application example 1)

[1215] 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."

[1216] In factories, there is a need to improve work efficiency by optimizing the collaboration between workers and robots. However, currently, teams are not formed based on the personality and job aptitude of workers, which results in inefficient work. Furthermore, even when starting up a new production line, there is a lack of means to quickly form optimal teams. This leads to reduced work efficiency and miscommunication.

[1217] 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.

[1218] In this invention, the server includes: a personality diagnosis means for conducting a personality diagnosis; a data receiving means for receiving personality data acquired by the personality diagnosis means; a question and answer means for answering questions about job aptitude; a data receiving means for receiving the job aptitude data acquired by the question and answer means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate ideal team member combinations; an output means for outputting the team formation results generated by the analysis means; a means for optimizing team formation of workers and robots based on the team formation results generated by the output means to improve factory work efficiency; and a means for providing work instructions and support to each worker and each robot in real time, thereby making it possible to maximize the capabilities of the workers and robots and improve work efficiency and the quality of communication.

[1219] "Personality assessment means" refers to a method or device used by a user to understand their own personality traits, including MBTI assessments.

[1220] The "data receiving means" refers to a method or device for acquiring and recording data obtained from the personality diagnosis means and question and answer means.

[1221] A "question answering means" is a method or device used by a user to answer questions about job suitability.

[1222] "Job aptitude data" is data indicating how suited a user is to a job, and is acquired through the question and answer means.

[1223] The "analysis means" refers to a method or device for integrating acquired personality data and job aptitude data to generate ideal team member combinations.

[1224] The "output means" refers to a method or device for providing the team formation results generated by the analysis means to the user.

[1225] "Workers" are the human workers who actually perform the work in the factory.

[1226] A "robot" is a mechanical device used to assist or perform factory work.

[1227] An "optimizer" is a method or device used to optimize the teaming of workers and robots.

[1228] "Means for providing in real time" refers to a method or device for providing immediate work instructions and support to workers and robots.

[1229] The following system configuration will be described as an embodiment of the present invention.

[1230] Overall overview

[1231] This is a system that optimizes the team composition of workers and robots to efficiently carry out factory work. This system is composed of a user terminal, a server, and a robot, and includes a personality diagnosis means, a data receiving means, a question and answer means, an analysis means, an output means, an optimization means, and a real-time instruction means.

[1232] User terminal

[1233] The user terminal is a device such as a tablet or smartphone. First, the user accesses the personality diagnosis interface using the user terminal and performs the MBTI diagnosis. Next, the user answers questions about job suitability. These diagnosis results are temporarily stored on the user terminal and then sent to the server.

[1234] server

[1235] The server receives the personality assessment data and job aptitude data and performs an integrated analysis of them. A generative AI model (e.g., OpenAI GPT-4 SDK) is used for the analysis. Based on the analysis results, the server generates an ideal team composition of workers and robots. This generated result is then sent back to the user's device and displayed to the user.

[1236] robot

[1237] Robots installed in factories work with the most suitable workers based on the team formation results sent from the server, and have the ability to provide work instructions and support in real time.

[1238] Specific examples

[1239] When starting up a new production line in a factory, Worker A and Worker B access the system. An MBTI diagnosis reveals that Worker A has the personality traits of "ENTJ" and excels in leadership. Worker B has the personality traits of "ISFP" and excels in attention to detail. The server analyzes this data and determines that it would be best to pair Worker A with Robot R2 (with heavy load transport capabilities) and Worker B with Robot R1 (with precision work support capabilities).

[1240] As a result, worker A works with R2 to manage route planning and efficient transportation, while worker B works with R1 to perform precise assembly work. In this way, optimal teaming of workers and robots is quickly achieved, significantly improving the efficiency and accuracy of the production line.

[1241] Prompt Sentence Examples

[1242] "Form optimal teaming of factory workers and robots. Generate optimal worker-robot pairs based on the provided MBTI diagnostic data and job aptitude data."

[1243] In this way, by providing appropriate instructions and support to workers and robots in real time based on the analysis results of personality diagnostic data and job aptitude data, it is possible to improve work efficiency and the quality of communication within the factory.

[1244] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1245] Step 1:

[1246] The user accesses the personality diagnosis interface using a device (tablet or smartphone) and conducts the MBTI diagnosis. The user answers a series of questions, and the answer data is temporarily saved on the device. The input is the user's answer data, and the output is the temporarily saved personality data.

[1247] Step 2:

[1248] The device sends the temporarily saved personality data to the server. The server receives this data and analyzes the user's MBTI type. The input is the personality data sent from the device, and the output is the analyzed MBTI type.

[1249] Step 3:

[1250] The user then proceeds to the job aptitude question section. The user answers a series of job-related questions displayed on the terminal, and the answers are temporarily saved on the terminal. The input is the user's job aptitude answer data, and the output is the temporarily saved job aptitude data.

[1251] Step 4:

[1252] The terminal sends the temporarily stored job aptitude data to the server. The server receives this data, integrates it with the personality data, and begins analysis. The input is the job aptitude data and personality data sent from the terminal, and the output is the integrated data set.

[1253] Step 5:

[1254] The server analyzes the integrated dataset using a generative AI model (e.g., OpenAI GPT-4 SDK). Specifically, it generates ideal team member combinations based on personality data and job aptitude data. The input is the integrated dataset, and the output is the team composition results generated by the analysis.

[1255] Step 6:

[1256] The server sends the generated team composition results to the terminal. The terminal displays the received results to the user. The input is the generated team composition results, and the output is the results displayed to the user.

[1257] Step 7:

[1258] Based on the generated team formation results, the server sends instructions to the robots to optimally match workers and robots. The robots follow the received instructions and begin work. The input is the team formation results, and the output is work instructions for the robots.

[1259] Step 8:

[1260] The robot works with the worker, providing real-time instructions and support as needed. The input is instructions from the server and on-site situation data, and the output is support for the worker and the progress of the work.

[1261] This will enable workers and robots to receive appropriate instructions and support in real time based on the analysis results of personality diagnostic data and job aptitude data, improving work efficiency and the quality of communication within the factory.

[1262] 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.

[1263] The present invention is a system that supports more sophisticated and effective team formation by incorporating user emotion data into analysis in addition to personality assessment and job aptitude data. This system includes personality assessment means, data receiving means, question and answer means, an emotion engine, analysis means, and output means.

[1264] Personality test tools

[1265] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions one by one, which identifies the user's personality type.

[1266] Data Receiving Method

[1267] The device temporarily stores the user's MBTI test answer data, and after answering all questions, sends the data to the server, which receives and analyzes the data as personality data.

[1268] Question answering means

[1269] Next, the user is taken to a detailed job fit question section, where the user again answers job-related questions, which gather data about the user's job fit.

[1270] Emotion Engine

[1271] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine uses voice recognition and image analysis technologies to analyze the user's speech and facial expressions to obtain emotional data.

[1272] Voice Recognition Technology

[1273] The device collects user speech data and performs real-time emotion analysis to identify the user's emotional state.

[1274] Image analysis technology

[1275] The device collects the user's facial expression data and uses image analysis technology to identify emotions, thereby supplementing the user's emotional data.

[1276] Analysis means

[1277] The server integrates the user's personality data, job aptitude data, and emotional data to form a dataset, and then analyzes the dataset using a chat model to generate an ideal team composition, which takes into account the user's personality traits, job aptitude, and emotional state in a comprehensive manner.

[1278] Output Method

[1279] After the analysis results are generated, the server sends them to each user's device. The device then displays the team formation results to the user. For example, if user A is recommended as the project leader and user B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[1280] Specific examples

[1281] For example, when a new project is launched, this system is used to organize a project team. User A and User B access the system and answer MBTI tests and job aptitude questions, respectively. The emotion engine then analyzes the users' speech and facial expressions to obtain emotional data.

[1282] User A is an "ENFJ" with a strong aptitude for project management, and emotional data indicates high motivation. On the other hand, User B is an "ISTP" with a wealth of technical expertise, and emotional data indicates a calm and analytical attitude.

[1283] Based on this information, the server uses a chat model to generate an appropriate team composition. The resulting team composition is "Team A: User A (project leader), User B (technical leader)" and is notified to the user via their device. This results in the rapid composition of an efficient team with smooth communication.

[1284] This system enables efficient team formation that takes emotional aspects into consideration, especially when creating small teams or starting new projects, and significantly reduces the amount of work required compared to manual team formation.

[1285] The processing flow will be explained below.

[1286] Step 1:

[1287] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed. The user answers the presented questions in sequence.

[1288] Step 2:

[1289] The device temporarily stores the user's MBTI diagnosis answer data, and after answering all questions is completed, sends the data to the server.

[1290] Step 3:

[1291] The server receives the MBTI test answer data and analyzes it to determine the user's MBTI type, which is then stored as personality data.

[1292] Step 4:

[1293] After the user completes the MBTI test, they click the Next button to proceed to the Job Compatibility Questions section, where they answer a series of job-related questions.

[1294] Step 5:

[1295] The terminal temporarily stores the answer data for the questions about job aptitude, and after all the questions have been answered, transmits the data to the server.

[1296] Step 6:

[1297] The server receives the job suitability response data and analyzes it to generate job suitability data for the user, which is stored in a format that includes job-related characteristics.

[1298] Step 7:

[1299] While the user is entering or completing the job fit questions, the emotion engine kicks in. The user's speech and facial expressions are collected.

[1300] Step 8:

[1301] The device sends voice data to the emotion engine, which performs real-time emotion analysis to identify the user's emotional state.

[1302] Step 9:

[1303] The device uses a camera to send the user's facial expression data to the emotion engine, which then analyzes the image to obtain the user's emotion data.

[1304] Step 10:

[1305] The device temporarily stores the emotion data and then transmits it to the server.

[1306] Step 11:

[1307] The server integrates the user's personality data, job aptitude data, and emotional data, and the integrated data set comprehensively represents each user's personality traits, job aptitudes, and emotional state.

[1308] Step 12:

[1309] The server starts the chat model and loads the integration data, which the chat model analyzes and generates the ideal team member combinations.

[1310] Step 13:

[1311] The server generates team composition results that assign appropriate roles to each team based on the analysis results. These results include the optimal roles for each user.

[1312] Step 14:

[1313] The server transmits the generated team formation results to each user's terminal.

[1314] Step 15:

[1315] The device receives the team formation results and displays them to the user. For example, it displays "Team A: User A (project leader), User B (technical leader)."

[1316] In this way, effective team formation is achieved through specific actions at each step.

[1317] Example 2

[1318] 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."

[1319] Conventional team formation systems only consider users' personality traits and job aptitudes, ignoring emotional data, and therefore do not adequately consider actual team performance or the quality of communication. This often leads to a lack of cooperation among team members and inappropriate division of roles, making it difficult to efficiently form new projects or small teams.

[1320] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a personality diagnosis means for conducting a personality diagnosis, a question answering means for answering questions about job aptitude, and an emotion engine for acquiring emotion data. This enables ideal team formation that comprehensively considers personality traits, job aptitude, and emotional state.

[1321] A "personality assessment means" is a device or software for assessing a user's personality traits.

[1322] The "data receiving means" is a device or software for receiving personality data, job aptitude data, and emotion data.

[1323] The "question answering means" is a device or software that allows a user to answer questions about job suitability.

[1324] An "emotion engine" is a device or software that analyzes a user's speech and facial expressions to obtain emotional data.

[1325] The "analysis means" is a device or software for integrating and analyzing personality data, job aptitude data, and emotional data to generate an ideal team formation result.

[1326] The "output means" is a device or software for outputting the team formation results generated by the analysis means to the user.

[1327] A "generative AI model" is an artificial intelligence model that analyzes data and generates ideal team composition results.

[1328] A "prompt" is an instruction for inputting data into a generative AI model.

[1329] This invention provides a system for supporting more sophisticated and effective team formation by integrating user personality traits, job aptitudes, and emotional data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotional engine, an analysis unit, and an output unit.

[1330] Personality test tools

[1331] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions in sequence, which identifies their personality type.

[1332] Data Receiving Method

[1333] The device temporarily stores the user's MBTI diagnostic data, and after answering all questions, sends the data to the server, which receives the data as personality data and stores it in a database.

[1334] Question answering means

[1335] The user then goes to the job suitability question section on the terminal and answers the job-related questions. The terminal also temporarily stores these answer data, and after all questions have been answered, sends the data to the server. The server receives this data as job suitability data and stores it in a database.

[1336] Emotion Engine

[1337] The device uses voice recognition and image analysis technologies to collect the user's speech and facial expression data, thereby analyzing the user's emotional state in real time and obtaining emotional data. For example, the device may use the user's microphone to collect speech data and the built-in camera to capture facial expression data.

[1338] Analysis means

[1339] The server combines the received personality data, job aptitude data, and emotion data to form a single dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition results.

[1340] Specifically, the server inputs the following prompt sentence into the generative AI model:

[1341] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[1342] User A:

[1343] Personality data: ENFJ

[1344] Job aptitude data: Strong aptitude for project management

[1345] Emotional data: High motivation

[1346] User B:

[1347] Personality data: ISTP

[1348] Job Qualifications: Strong technical expertise

[1349] Emotional data: Calm and analytical attitude

[1350] Output Method

[1351] The server sends the generated team formation results to each user's device. The device receives the results and displays them to the user. For example, if User A is recommended as the project leader and User B as the technical leader, the device will display "Team A: User A (project leader), User B (technical leader)."

[1352] This system enables efficient team formation that comprehensively considers personality traits, job aptitude, and emotional state, significantly reducing the amount of work required compared to manual team formation, especially when creating small teams or launching new projects.

[1353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1354] Processing Steps

[1355] Step 1: Start the personality test

[1356] The user accesses the system using a terminal, and the MBTI diagnostic interface is displayed on a web browser. The user answers the displayed questions one by one.

[1357] Input: The URL the user visits and the question they are asked

[1358] Output: User's answer

[1359] Specific operation: The user launches a browser, accesses the specified URL, and clicks the "Start" button to begin answering the questions.

[1360] Step 2: Submit your personality test data

[1361] The device temporarily stores the user's MBTI diagnostic answers, and after answering all questions, sends the data to the server, which receives this data as personality data and stores it in a database.

[1362] Input: Answers to all user questions

[1363] Output: Personality data stored in a database

[1364] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends this data in JSON format to the server, and the server stores the received data in the database.

[1365] Step 3: Begin the job qualification questions

[1366] The user navigates to the job suitability question section on the terminal and answers the questions displayed to collect job suitability data.

[1367] Input: Job Qualification Questions

[1368] Output: User's answer

[1369] Specific Actions: A user opens the Job Qualification Questions section and clicks the "Start" button to begin answering the questions.

[1370] Step 4: Submit your job suitability data

[1371] The terminal temporarily stores the answer data for the job aptitude questions, and after answering all the questions, transmits it to the server. The server receives this as job aptitude data and stores it in a database.

[1372] Input: Answers to all user questions

[1373] Output: Job suitability data stored in a database

[1374] Specific operation: After the user answers the final question, they click the "Submit" button. The device sends the answer data in JSON format to the server, which then receives the data and stores it in the database.

[1375] Step 5: Collecting emotion data

[1376] The device uses voice recognition and image analysis technologies to collect data on the user's speech and facial expressions, thereby analyzing the user's emotional state in real time and obtaining emotional data.

[1377] Input: User's speech and facial expression data

[1378] Output: Parsed emotion data

[1379] Specific behavior:

[1380] Voice recognition technology: Collects user speech using the device's built-in microphone and analyzes it in real time.

[1381] Image analysis technology: Captures and analyzes the user's facial expressions using the device's built-in camera.

[1382] Step 6: Data synthesis and analysis

[1383] The server creates a dataset that integrates personality data, job aptitude data, and emotional data, and analyzes it using a generative AI model (e.g., GPT-4).

[1384] Input: personality data, job aptitude data, emotional data

[1385] Output: Analysis results (ideal team composition)

[1386] Specific operation: The server inputs the following prompt sentence into the generative AI model:

[1387] Please suggest an ideal team composition based on the following personality, job fit, and emotional data:

[1388] User A:

[1389] Personality data: ENFJ

[1390] Job aptitude data: Strong aptitude for project management

[1391] Emotional data: High motivation

[1392] User B:

[1393] Personality data: ISTP

[1394] Job Qualifications: Strong technical expertise

[1395] Emotional data: Calm and analytical attitude

[1396] Step 7: Generate team formation results

[1397] Based on the data analyzed by the generative AI model, the server generates the optimal team composition results.

[1398] Input: Analysis results of the generative AI model

[1399] Output: Team composition results

[1400] Specific operation: The generated team composition results are stored in an internal data structure such as "Team A: User A (project leader), User B (technical leader)".

[1401] Step 8: Viewing the results

[1402] The server transmits the generated team formation results to each user's terminal, which receives the results and displays them to the user.

[1403] Input: Generated team composition results

[1404] Output: Team formation results displayed to the user

[1405] Specific operation: The server sends the team formation results in JSON format to the user's device, which then parses the results and displays them in the browser. For example, it displays "Team A: User A (Project Leader), User B (Technical Leader)."

[1406] (Application example 2)

[1407] 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."

[1408] Conventional team formation systems have relied solely on analysis based on personality data and job aptitude data, but because they do not take emotional data into account, the accuracy of the team combinations is insufficient, making it difficult to maximize performance in actual work. Furthermore, there is a lack of tools for quickly and effectively forming teams, a problem that is particularly pronounced in environments where immediate response is required, such as factories. It is necessary to develop a system that can output real-time analysis results using wearable devices such as smart glasses.

[1409] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1410] In this invention, the server includes a personality assessment unit, a question and answer unit regarding job aptitude, an emotion engine that analyzes the user's voice and facial expressions to acquire emotion data, an analysis unit, and an output unit. This allows for an integrated analysis of personality data, job aptitude data, and emotion data, enabling the generation of ideal team member combinations. Furthermore, analysis results can be presented to employees in real time using wearable devices such as smart glasses, enabling immediate response on the factory floor.

[1411] The "personality assessment means" is a function for providing questions and tests to assess the user's personality traits and obtaining the results as data.

[1412] The "data receiving means" is a function for receiving data obtained from the personality diagnosis means and question and answer means and transmitting the data to the server.

[1413] The "question answering means" is a function that provides an interface for the user to answer questions about job aptitude and acquires the answers as data.

[1414] An "emotion engine" is a system that incorporates technology to analyze a user's voice and facial expressions and identify their emotional state.

[1415] The "analysis means" is a function for comprehensively analyzing the received personality data, job aptitude data, and emotional data to generate an ideal team composition.

[1416] The "output means" is a function for presenting the team formation results generated by the analysis means to the user.

[1417] A "generative AI model" is a trained artificial intelligence model used to analyze data and optimize team composition.

[1418] A "prompt sentence" is an input sentence given when using a generative AI model, and is a sentence that instructs specific analysis and generation.

[1419] The present invention provides a system for supporting team formation by analyzing personality assessment, job aptitude data, and emotion data. The system includes a personality assessment unit, a data receiving unit, a question and answer unit, an emotion engine, an analysis unit, and an output unit.

[1420] System configuration

[1421] 1. Personality test:

[1422] The user wears the smart glasses and launches the application. The MBTI diagnosis is displayed on the screen, and the user answers the questions by voice. The answer data is temporarily stored in the smart glasses and then sent to the server.

[1423] 2. Question answering method:

[1424] Once the personality test is complete, the user proceeds to the job aptitude section, where questions are similarly displayed and answered verbally. This data is also sent to the server.

[1425] 3. Emotion Engine:

[1426] The camera on the smart glasses analyzes the user's facial expressions in real time and collects emotion data using speech recognition software (e.g., Google Cloud Speech-to-Text API). The emotion engine analyzes the emotion data using OpenCV and TensorFlow.

[1427] 4. Data Receiving Method:

[1428] The server receives and centrally manages the personality data, job aptitude data, and emotion data, which are then processed in an integrated manner by the analysis means.

[1429] 5. Analysis methods:

[1430] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (e.g., GPT-4) to generate the ideal team composition.

[1431] 6. Output Method:

[1432] The analysis results are displayed in real time on the smart glasses display, allowing users to quickly check the team composition results.

[1433] Specific examples

[1434] When a new production line is introduced, factory employees use smart glasses to access the system. Employee A is identified as an ESTJ in the MBTI test, and a job aptitude test indicates a high aptitude for line supervisors. Emotional data indicates high levels of concentration and judgment. Employee B is identified as an INFP, with a high aptitude for quality control, and is also relaxed and cooperative.

[1435] Based on this information, the server uses a generative AI model (GPT-4) to optimally organize the team. The smart glasses display "New production line team: Employee A (line supervisor), Employee B (quality control)."

[1436] Prompt Sentence Examples

[1437] Here are some example prompts to input to a generative AI model:

[1438] "Please suggest the optimal team composition based on the following personality test, job aptitude data, and emotional data."

[1439] In this way, the personality data, job aptitude data, and emotional data are analyzed comprehensively to realize an optimal team composition.

[1440] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1441] Step 1:

[1442] The user starts the smart glasses and starts the personality test (MBTI test). The personality test questions are displayed on the smart glasses' display, and the user answers by voice. The device collects and temporarily stores the user's answer data.

[1443] Input: User's voice responses to personality questions

[1444] Data processing: Converts voice data into text data and temporarily saves it as response data

[1445] Output: Temporarily saved personality test answer data

[1446] Step 2:

[1447] When the personality test is completed, the device sends the answer data to the server, which stores the received data as personality data.

[1448] Input: Temporarily saved personality test answer data

[1449] Data processing: Response data is sent to the server and saved as personality data

[1450] Output: Personality data stored on the server

[1451] Step 3:

[1452] After the personality test, the user proceeds to the job aptitude question section. Questions are displayed on the device's display, and the user answers by voice. The device collects and temporarily stores the answer data on job aptitude.

[1453] Input: User's spoken responses to job qualification questions

[1454] Data processing: Converts voice data into text data and temporarily saves it as response data

[1455] Output: Temporarily saved job aptitude answer data

[1456] Step 4:

[1457] When the job aptitude question session is completed, the terminal transmits the job aptitude answer data to the server, which stores the received data as job aptitude data.

[1458] Input: Temporarily saved job aptitude answer data

[1459] Data processing: The response data is sent to the server and saved as job aptitude data.

[1460] Output: Job suitability data stored on the server

[1461] Step 5:

[1462] Once the user completes the personality and job aptitude tests, the emotion engine is activated. The device collects the user's voice and facial expressions in real time. The emotion data is analyzed using speech recognition software (Google Cloud Speech-to-Text API) and image analysis software (OpenCV, TensorFlow) and sent to the server.

[1463] Input: User's voice and facial expression data

[1464] Data processing: Analyze voice data and recognize emotional states (using Google Cloud Speech-to-Text API). Analyze facial expression data and recognize emotional states (using OpenCV and TensorFlow). Integrate as emotional data.

[1465] Output: Parsed emotion data

[1466] Step 6:

[1467] The server combines personality data, job aptitude data, and emotion data to form a dataset, which is then analyzed using a generative AI model (GPT-4) to generate ideal team compositions.

[1468] Input: personality data, job aptitude data, emotional data

[1469] Data processing: Integrate data to form a dataset, analyze the dataset using a generative AI model (GPT-4), and generate the ideal team composition.

[1470] Output: Generated team composition results

[1471] Step 7:

[1472] The server transmits the generated team formation results to the terminal, which then displays the results in real time on the display of the smart glasses for the user.

[1473] Input: Generated team composition results

[1474] Data processing: Team formation results are sent to the device and displayed on the screen

[1475] Output: Team formation results displayed on the smart glasses display

[1476] 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.

[1477] 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.

[1478] 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.

[1479] 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.

[1480] 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.

[1481] 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.

[1482] 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).

[1483] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1484] 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."

[1485] 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.

[1486] 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).

[1487] 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.

[1488] 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.

[1489] 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.

[1490] 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.

[1491] 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.

[1492] 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.

[1493] 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.

[1494] 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.

[1495] 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.

[1496] 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.

[1497] The following is further disclosed regarding the above embodiment.

[1498] (Claim 1)

[1499] A personality diagnosis means for conducting a personality diagnosis;

[1500] data receiving means for receiving the personality data acquired by the personality diagnosis means;

[1501] a question answering means for answering questions regarding job suitability;

[1502] data receiving means for receiving the job aptitude data acquired by the question and answer means;

[1503] an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination;

[1504] an output means for outputting the team formation result generated by the analysis means;

[1505] A system including:

[1506] (Claim 2)

[1507] 2. The system according to claim 1, wherein the personality assessment means includes means for conducting an MBTI assessment.

[1508] (Claim 3)

[1509] 2. The system according to claim 1, wherein the analysis means analyzes the personality data and the job aptitude data using a chat model.

[1510] "Example 1"

[1511] (Claim 1)

[1512] A personality diagnosis means for conducting a personality diagnosis;

[1513] data receiving means for receiving the personality data acquired by the personality diagnosis means;

[1514] a question answering means for answering questions regarding job suitability;

[1515] data receiving means for receiving the job aptitude data acquired by the question and answer means;

[1516] an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination;

[1517] an output means for outputting the team formation result generated by the analysis means;

[1518] A means for the user to answer MBTI diagnosis and job aptitude questions using a terminal;

[1519] means for the terminal to transmit personality data and job aptitude data to a server;

[1520] means for analyzing a dataset using the generative AI model;

[1521] A system including:

[1522] (Claim 2)

[1523] 2. The system according to claim 1, wherein the personality assessment means includes means for conducting an MBTI assessment.

[1524] (Claim 3)

[1525] 2. The system according to claim 1, wherein the analysis means analyzes the personality data and the job aptitude data using a generative AI model.

[1526] "Application Example 1"

[1527] (Claim 1)

[1528] A personality diagnosis means for conducting a personality diagnosis;

[1529] data receiving means for receiving the personality data acquired by the personality diagnosis means;

[1530] a question answering means for answering questions regarding job suitability;

[1531] data receiving means for receiving the job aptitude data acquired by the question and answer means;

[1532] an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination;

[1533] an output means for outputting the team formation result generated by the analysis means;

[1534] a means for optimizing the team formation of workers and robots based on the team formation results generated by the output means, thereby improving the efficiency of factory work;

[1535] a means for providing real-time work instructions and support to each worker and each robot;

[1536] A system including:

[1537] (Claim 2)

[1538] 2. The system according to claim 1, wherein the personality assessment means includes means for conducting an MBTI assessment.

[1539] (Claim 3)

[1540] 2. The system according to claim 1, wherein the analysis means analyzes the personality data and the job aptitude data using a generative AI model.

[1541] "Example 2: Combining Emotion Engines"

[1542] (Claim 1)

[1543] A personality diagnosis means for conducting a personality diagnosis;

[1544] data receiving means for receiving the personality data acquired by the personality diagnosis means;

[1545] a question answering means for answering questions regarding job suitability;

[1546] data receiving means for receiving the job aptitude data acquired by the question and answer means;

[1547] an emotion engine for acquiring emotion data;

[1548] data receiving means for receiving emotion data collected by the emotion engine;

[1549] an analysis means for comprehensively analyzing the personality data, the job aptitude data, and the emotion data to generate an ideal combination of team members;

[1550] an output means for outputting the team formation result generated by the analysis means;

[1551] A system including:

[1552] (Claim 2)

[1553] 2. The system according to claim 1, wherein the personality assessment means includes means for conducting an MBTI assessment.

[1554] (Claim 3)

[1555] 2. The system according to claim 1, wherein the analysis means analyzes the personality data, the job aptitude data, and the emotion data using a generative AI model.

[1556] "Application example 2 when combining emotion engines"

[1557] (Claim 1)

[1558] A personality diagnosis means for conducting a personality diagnosis;

[1559] data receiving means for receiving the personality data acquired by the personality diagnosis means;

[1560] a question answering means for answering questions regarding job suitability;

[1561] data receiving means for receiving the job aptitude data acquired by the question and answer means;

[1562] an emotion engine that analyzes the user's voice and facial expressions to acquire emotion data;

[1563] an analysis means for comprehensively analyzing the personality data, the job aptitude data, and the emotion data to generate an ideal combination of team members;

[1564] an output means for outputting the team formation result generated by the analysis means;

[1565] A system including:

[1566] (Claim 2)

[1567] 2. The system according to claim 1, wherein the personality assessment means includes means for conducting an MBTI assessment.

[1568] (Claim 3)

[1569] 2. The system according to claim 1, wherein the analysis means analyzes the personality data, the job aptitude data, and the emotion data using a generative AI model. [Explanation of symbols]

[1570] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A personality diagnosis means for conducting a personality diagnosis; data receiving means for receiving the personality data acquired by the personality diagnosis means; a question answering means for answering questions regarding job suitability; data receiving means for receiving the job aptitude data acquired by the question and answer means; an analysis means for comprehensively analyzing the personality data and the job aptitude data to generate an ideal team member combination; an output means for outputting the team formation result generated by the analysis means; A system including:

2. 2. The system according to claim 1, wherein the personality assessment means includes means for conducting an MBTI assessment.

3. 2. The system according to claim 1, wherein said analysis means analyzes said personality data and said job aptitude data using a chat model.

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