system

Abstract

We provide the system. [Solution] The system includes a generative model that generates a list of questions based on information received from the user, and means for providing the user with interactive questions to delve deeper into their characteristics. A means for analyzing the responses received from the user and generating a profile to identify the user's characteristics and strengths, A means for calculating the optimal roles and placements based on the aforementioned profile and proposing the optimal placement to the user, A means for receiving feedback from the user and for fine-tuning the generative model, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, in many organizations, the strengths of each employee have not been effectively utilized, so the performance of employees has not been maximized, and the high turnover rate of new employees has become a problem. For this reason, there is a need for a method to grasp the characteristics of employees and realize an optimal personnel arrangement based on individual strengths. It is necessary to provide a means to efficiently solve such problems and improve the productivity of the entire organization and the engagement of employees.

Means for Solving the Problems

[0005] This invention provides a system that uses a generative model to provide interactive questions that delve deeper into characteristics based on information received from users, and then analyzes the answers to generate a characteristic profile. Based on this profile, it proposes the optimal role and placement for the user and fine-tunes the generative model by obtaining feedback. Through this process, it is possible to efficiently implement personnel placement that maximizes the use of employees' strengths, thereby reducing the turnover rate of new graduates and improving the overall performance of the organization.

[0006] A "user" refers to an individual who uses the system to receive analysis of their own characteristics and suggestions for optimal placement.

[0007] A "generative model" refers to an artificial intelligence-based model that generates questions to delve deeper into characteristics based on input information and performs analysis.

[0008] "Characteristics" refers to the personal characteristics of a user, such as their personality, abilities, and interests.

[0009] "Interactive questions" refer to questions generated to gather information through interactive communication with users.

[0010] A "profile" refers to a collection of information that systematically organizes a user's characteristics and strengths.

[0011] "Optimal roles and placements" refer to the duties and positions assigned to users based on their characteristics and strengths, enabling them to perform at their best within the organization.

[0012] "Feedback" refers to the opinions and information that users input regarding their satisfaction level and suggestions for improvement in response to suggestions received from the system.

[0013] "Fine-tuning" refers to the process of making adjustments to improve the functionality and accuracy of the generative model by incorporating the feedback that has been collected. [Brief explanation of the drawing]

[0014] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

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

[0017] In the following embodiments, a processor with a reference number (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a RAM (Random Access Memory) with a reference number is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a storage with a reference number is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

[0024] As shown in Figure 1, the 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 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the 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 processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention is a system that identifies the characteristics and strengths of each employee within an organization and proposes optimal job placement based on that. This system is mainly composed of a server, terminals, and users, and performs characteristic analysis through an interactive process.

[0036] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server in real time. Based on the received information, the server activates a generative model and creates a list of questions specific to that user.

[0037] Next, the device presents the user with a generated question. The user answers this question and sends the answer to the server via the device. The server then analyzes the received answer and generates a profile to identify the user's characteristics and strengths. This profile details the user's behavioral patterns, strengths, and other relevant information.

[0038] The server then calculates the optimal roles and placements that best utilize the user's strengths, based on their profile. This result is suggested to the user via the terminal. The user can provide feedback on the suggestion and input it into the terminal. This feedback is then sent back to the server and used to further improve and refine the generative model.

[0039] As a concrete example, when user A, who has strong programming development skills, uses the system, the generative model presents interactive questions related to project management and software development. Based on the analysis of the responses, user A is identified as having a strong programming background, and based on this, the system proposes a role for user A as a software engineer. User A then provides feedback on their satisfaction level and any additional requests, and based on this, the system can make further improvements and suggestions.

[0040] Thus, the system of the present invention provides a novel method for accurately understanding the characteristics of individual employees and guiding them to be placed in positions within the organization where they can perform most effectively.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users log in to the system using their terminal and enter basic information such as their name, department, work experience, and interests. The terminal collects this information and sends it to the server.

[0044] Step 2:

[0045] The server activates a generative model based on the received basic information to create a user-specific list of questions. These questions are designed to delve deeper into the user's personality, abilities, and interests.

[0046] Step 3:

[0047] The terminal presents the user with generated questions. The user enters their answers to these questions, and the terminal sends them to the server.

[0048] Step 4:

[0049] The server analyzes the received response data and extracts user characteristics and strengths based on keywords and content within the responses. This analysis organizes the user's behavioral patterns and characteristics into a profile.

[0050] Step 5:

[0051] The server stores the generated characteristic profiles in a database and calculates the optimal roles and placements for users by comparing them with the organization's current situation. This takes into account the organization's structure and the current availability of roles.

[0052] Step 6:

[0053] The terminal receives optimal placement suggestions from the server and displays them to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests.

[0054] Step 7:

[0055] The server collects user feedback and uses it to improve the accuracy of the generative model. Based on this information, the model is fine-tuned to enable more accurate question generation and analysis.

[0056] (Example 1)

[0057] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0058] Optimizing personnel placement within an organization presents challenges, particularly in accurately understanding individual characteristics and strengths, making it difficult to maximize employee capabilities. Traditional methods, in particular, often fail to analyze individual characteristics in detail, resulting in uniform placements. This can lead to decreased employee motivation and hinder productivity improvements. Furthermore, insufficient mechanisms for effectively utilizing feedback hinder the optimization of personnel placement across the entire organization.

[0059] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0060] In this invention, the server includes means for a user to access the system via a terminal and input basic information, means for transmitting the input information to the server and activating a generative AI model to generate a user-specific list of questions, and means for analyzing the answers received from the user and generating a profile that identifies the user's characteristics and strengths. This makes it possible to accurately grasp the characteristics of each employee and to quickly and effectively propose the optimal role and placement. Furthermore, by including means for fine-tuning the generative AI model used by the server based on user feedback, it is possible to improve the accuracy of placement proposals and promote overall organizational efficiency.

[0061] "User" refers to an individual or member of an organization who uses this system to optimize job assignments.

[0062] A "terminal" refers to an electronic device used by users to access a system and input or output information.

[0063] "System" refers to a set of devices and software configured to understand the characteristics and strengths of users and propose optimal job placements.

[0064] A "server" refers to an information processing device that receives data sent by users and performs analysis and generation.

[0065] A "generative AI model" refers to an artificial intelligence algorithm that generates a list of questions tailored to the user's characteristics based on the input information.

[0066] A "question list" refers to a series of interactive questions created by a generative AI model to delve deeper into the user's characteristics.

[0067] A "profile" refers to a set of information that analyzes and organizes a user's characteristics and strengths.

[0068] "Roles and assignments" refer to the work positions and responsibilities proposed to leverage the characteristics and strengths of the users.

[0069] "Feedback" refers to comments from users regarding evaluations and suggestions for improvement in response to system proposals.

[0070] "Fine-tuning" refers to the process of optimizing the generated AI model and other system elements based on feedback to improve accuracy and effectiveness.

[0071] This invention is a system that understands the characteristics and strengths of users and proposes the optimal job placement. This system mainly consists of a server, terminals, and users, and the hardware and software work together to function.

[0072] First, the user accesses the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal verifies this information and sends it to the server. Based on the received information, the server uses a generative AI model to create a user-specific list of questions. This model generates optimized prompts based on the input data to delve deeper into the user's characteristics. An example of such a prompt might be: "Generate a personalized list of questions based on the user's basic information (name, work experience, areas of interest). Then, analyze the answers to identify the user's characteristics and strengths."

[0073] A list of questions generated by the server is presented to the user via the terminal. The user answers the questions, and the terminal sends the answers back to the server. The server uses natural language processing technology to analyze the answers and generate a profile that shows the user's characteristics and strengths. This profile describes the user's behavioral patterns and skill set in detail.

[0074] Subsequently, the server calculates the job placement that would allow the user to perform most effectively, based on their profile. This calculation result is then fed back to the user in the form of a suggestion via the terminal. The user provides feedback on the suggestion, and the information entered into the terminal is sent to the server to help adjust and improve the generated AI model.

[0075] As a concrete example, if user A, who has strong programming development skills, uses this system, the generating AI model will present him with questions related to project management and software development. Based on the analysis of his answers, the role of a software engineer will be suggested to user A. By providing feedback on this suggestion, the system can be expected to improve further in accuracy.

[0076] This system provides a new method for individually optimized role assignments based on the information provided, helping to improve overall organizational efficiency.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] Users log in to the system via a terminal and enter basic information such as their name, work experience, and interests. This becomes the input data. The terminal verifies this entered information, formats it, and converts it into a digital form for transmission to the server. The formatted data is then sent to the server as output.

[0080] Step 2:

[0081] The server analyzes the basic information received from the terminal and activates the generating AI model. The input data is the user's basic information. The server uses prompt statements to instruct the generating AI model, giving commands such as, "Generate a personalized list of questions based on the user's basic information." As part of the data processing, prompt creation and question list construction are performed within the model. The output is a user-specific list of questions.

[0082] Step 3:

[0083] The terminal receives a list of questions sent from the server and displays them sequentially in the user interface. The input data is the list of questions from the server. The terminal performs actions to present these questions interactively to the user and provides a UI that allows the user to answer. The output is the user's answer to each question.

[0084] Step 4:

[0085] The user enters answers to questions presented via the terminal. These answers become the input data. The terminal formats this data in order to send it to the server. As output, the formatted answer data is sent to the server.

[0086] Step 5:

[0087] The server uses natural language processing techniques to analyze the received responses. The response data is used as input. Based on the analysis results, the server performs data calculations to generate a profile that identifies characteristics and strengths. The output is a profile detailing the user's behavioral patterns and skill set.

[0088] Step 6:

[0089] The server calculates the optimal roles and placements based on the generated profiles. The input data is the profiles. In this process, the AI ​​model compares this data with role data within the organization and performs calculations to identify positions where users can perform at their best. The output is a proposal for optimal job placement.

[0090] Step 7:

[0091] The terminal presents the user with job placement suggestions sent from the server. The input data is the job placement suggestions from the server. The terminal visually displays the suggestions and provides a user interface (UI) that allows selection and requests for further information. User feedback is obtained as output.

[0092] Step 8:

[0093] The user inputs feedback on the proposed layout via the terminal. This feedback becomes the input data. The terminal formats this feedback and sends it to the server. The output is the feedback data used to refine the generated AI model.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] The increasing complexity of work environments and organizational structures presents challenges in appropriately understanding the characteristics of employees and machinery and achieving optimal allocation. Furthermore, while efficient work allocation considering the characteristics of robots is required within factories, there is currently a lack of effective systems to implement this.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes a generative model that generates a list of questions based on information received from a user, means for providing interactive questions to delve deeper into the user's characteristics, means for analyzing the answers received from the user and generating a profile to identify the user's characteristics and strengths, and means for calculating the optimal role and placement based on the profile and proposing the optimal placement to the user. This enables the automatic proposal of optimal job duties and work assignments according to the characteristics of individual employees or robots, thereby improving productivity and efficiency.

[0099] A "generative model" is an algorithm that creates an appropriate list of questions based on information from the user.

[0100] "Interactive questioning" is a question format provided by generative models to delve deeper into the characteristics of users.

[0101] A "profile" is a collection of information that analyzes and records in detail the characteristics and strengths of a user.

[0102] "Feedback" refers to opinions and impressions received from users regarding suggestions.

[0103] "Methods for analyzing robot characteristics" refer to methods for evaluating the skills and capabilities of robots in a factory and determining the optimal work assignments.

[0104] This invention is implemented through a system that analyzes the characteristics of employees and factory robots and proposes the optimal placement. This system works in cooperation with a server, terminals, and users.

[0105] The server uses a generative AI model to generate individual question lists based on user and robot information. Smartphones and tablets are often used as terminals for information gathering. The generated question lists are provided to the user via the terminal. The answers provided by the user are sent from the terminal to the server, where they are analyzed. This analysis identifies characteristics and strengths, and a profile is generated. The profile information is stored in a database and used to inform personnel allocation and robot task assignments across the entire organization.

[0106] As a concrete example, consider a case where the characteristics of a newly introduced robot on a factory production line are analyzed. The server identifies the tasks and operations that the robot excels at and, based on that, proposes the most efficient placement on the production line.

[0107] Examples of prompt statements include the following:

[0108] "User-inputted robot characteristics: [Speed: High, Accuracy: High, Dexterity: Medium] Question: Based on these characteristics, which task is best suited? Answer: Profile generation and optimal placement suggestion based on collected data."

[0109] In this way, the system enables the optimization of the work environment and production efficiency.

[0110] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0111] Step 1:

[0112] The user uses a terminal to input basic information such as personal information, work experience, interests, and robot characteristics. The entered information is sent to the server via the terminal.

[0113] Input: User information (name, work experience, interests), robot characteristics

[0114] Output: Data transferred to the server

[0115] Step 2:

[0116] Based on the received information, the server activates a generative AI model to generate a user-specific list of questions. This generation process uses natural language processing to create prompt sentences based on the input information.

[0117] Input: Basic information, robot characteristics

[0118] Output: Question List

[0119] Step 3:

[0120] The terminal presents the user with a list of questions received from the server. The user answers the presented interactive questions and sends their answers to the server via the terminal.

[0121] Input: Question list

[0122] Output: User's response

[0123] Step 4:

[0124] The server analyzes user responses and generates profiles to identify characteristics and strengths. The generated profiles are obtained by statistically analyzing the response data and extracting behavioral patterns and skill sets.

[0125] Input: User's response

[0126] Output: Profile

[0127] Step 5:

[0128] The server calculates the optimal roles and placements based on the generated profiles and proposes them to the user. This calculation is performed in a way that matches the required skills within the organization or factory.

[0129] Input: Profile

[0130] Output: Suggested optimal placement

[0131] Step 6:

[0132] The terminal receives optimal placement suggestions from the server and presents them to the user. The user can input feedback into the terminal, which is then sent back to the server and used to improve the generative model.

[0133] Input: Optimal placement proposal

[0134] Output: User feedback

[0135] In this way, the entire system efficiently processes information and continuously provides optimal placement.

[0136] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0137] This invention is an advanced system that understands the characteristics and emotions of each employee within an organization and proposes optimal job placement based on that understanding. By incorporating an emotion engine, this system performs characteristic profiling and placement suggestions that consider not only the user's responses but also their emotional state.

[0138] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server. Upon receiving this information, the server activates a generative model and creates a user-specific list of questions. The questions themselves are designed to delve deeper into the user's characteristics.

[0139] When a user answers a question, the emotion engine uses the voice and text data to perform sentiment analysis. The device also sends this sentiment data to the server. The server analyzes the received responses and sentiment data to generate a profile that identifies characteristics and strengths. Because this profile includes sentiment data, it can also incorporate the user's emotional aspects.

[0140] Next, the server calculates the optimal role and placement for the user based on their profile. Emotional state is also taken into account to suggest an environment where the user can perform best. This suggestion is returned to the user via the terminal, and the user provides feedback on the suggestion.

[0141] For example, if User B is asked questions about program management and team leadership, the emotion engine detects User B's confidence and stress level from the tone of voice used when answering the questions. The server considers this data and suggests that User B is suitable for a project manager position, but suggests that support is needed if the stress level is too high. User B can then provide feedback indicating that leadership training is desirable, allowing the final suggestion to be adjusted.

[0142] In this form, the present invention can reflect not only the user's characteristics but also their emotions, making it possible to propose an organizational arrangement that is more optimized for the individual.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user logs into the system using a terminal and enters necessary basic information such as their name, work experience, and interests. The terminal then sends this information to the server.

[0146] Step 2:

[0147] The server activates a generative model based on the received information to create a user-specific list of questions. These questions are designed to delve deeper into the user's characteristics.

[0148] Step 3:

[0149] The device presents the user with a generated question. The user enters their answer to the question via voice or text. The answer data is temporarily stored on the device.

[0150] Step 4:

[0151] The emotion engine analyzes the user's responses to determine their emotional state by analyzing their voice tone and textual expression. This emotional data is sent to the server along with the response data.

[0152] Step 5:

[0153] The server analyzes the received response data and sentiment data to generate a profile that identifies the user's characteristics and strengths. This profile also includes sentiment data.

[0154] Step 6:

[0155] The server calculates the optimal role and placement for the user based on the generated profile. The calculation results reflect the user's emotional state. This result is sent to the terminal.

[0156] Step 7:

[0157] The terminal displays deployment suggestions from the server to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests. The feedback is sent to the server.

[0158] Step 8:

[0159] The server receives user feedback and uses it to fine-tune the emotion engine and generative models. This information helps improve the accuracy of the models.

[0160] (Example 2)

[0161] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0162] Traditional job placement systems primarily based placement suggestions on superficial information such as skill sets, but they failed to consider the user's emotions and psychological state, limiting their ability to maximize performance. This resulted in reduced accuracy of placement suggestions and insufficient user satisfaction.

[0163] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0164] In this invention, the server includes means for activating a generation AI model based on basic information received from the user and generating a list of individual questions to understand the user's characteristics; means for analyzing the response data and emotional data received from the user with an emotion engine and generating a profile that identifies the user's characteristics and emotional state; and means for referring to the profile and using an artificial intelligence algorithm to calculate and propose the most suitable job placement for the user. This makes it possible to propose a more accurate job placement that takes the user's emotional state into account.

[0165] "User" refers to an individual or group that operates the system and inputs information.

[0166] A "generative AI model" refers to a computational model that uses artificial intelligence to generate specific tasks or outputs based on input data.

[0167] A "customized question list" refers to a set of questions customized to delve into the characteristics of a specific user.

[0168] "Response data" refers to text or audio information provided by users in response to questions.

[0169] An "emotion engine" refers to a computing device or software that analyzes a user's emotional state from voice and text data.

[0170] A "profile" refers to a collection of data created to represent a user's characteristics, strengths, and emotional state.

[0171] An "artificial intelligence algorithm" refers to a method that enables advanced inference and calculation through programs that run on computers and perform data analysis and prediction.

[0172] "Job assignment" refers to the process of determining the role and position of users within an organization.

[0173] "Feedback" refers to the opinions and reactions that users provide to the content suggested by the system.

[0174] An "information recording device" refers to equipment or a system for storing and managing digital data.

[0175] "Output device" refers to equipment or systems used to display calculation results or data to users.

[0176] This system implements a program designed to achieve optimal job placement for users through a multi-stage process. The system is operated by a server (an information processing device), a terminal (an interface device), and the user.

[0177] 1. The user logs into the system using a terminal. They then enter basic information such as personal information, work experience, and interests. This information is entered according to the format specified on the terminal and sent from the terminal to the server after completion.

[0178] 2. The server analyzes the received information using a generative AI model and generates a personalized list of questions. This list of questions is customized to gain a more specific understanding of the user's characteristics.

[0179] 3. When the user answers the list of questions displayed on the device, they enter their answers as audio or text data. The device then sends this data to the server.

[0180] 4. The server uses an emotion engine to analyze the received response data and identify the user's emotional state from their voice tone and text expression. This information is used to generate a trait profile, which includes the user's skills, traits, and emotional tendencies.

[0181] 5. Based on the generated profile, the server uses an artificial intelligence algorithm to calculate and propose the optimal job placement for the user.

[0182] As a concrete example, if User B answers "Very confident" to the question, "Can you handle a tense situation calmly?", the emotion engine analyzes the degree of confidence from the audio data of that answer. Based on this, it is determined that User B has high stress tolerance and is suitable for a position that can handle pressure. As a final suggestion, placement based on these characteristics is presented to the user.

[0183] Examples of prompt statements include the following:

[0184] "Based on your work experience, what skills do you feel have been particularly useful?"

[0185] "What do you consider most important when starting a new project?"

[0186] These factors enable the system to consider not only the user's characteristics but also their emotions, thereby achieving more personalized job assignments.

[0187] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0188] Step 1:

[0189] The user operates a terminal and logs into the system. After logging in, they enter basic information such as their name, work experience, and interests. Once this information is entered, the terminal checks the input data for consistency and integrity. If the information is correctly formatted, the terminal sends the data to the server. The input data is subjective information of the user, and the output data is a transmittable packet containing this information.

[0190] Step 2:

[0191] Upon receiving basic information from the terminal, the server activates a generative AI model. Using this model, the server generates a personalized list of questions tailored to each user. This process uses the user's basic information as input and outputs questions designed to delve deeper into their characteristics based on that information. The generated question list is then sent back to the terminal.

[0192] Step 3:

[0193] The user answers a list of individual questions displayed on the device. The device supports text and voice input and records the user's responses as digital data. The text and voice data serve as input data for analysis and as output data when transmitted from the device to the server.

[0194] Step 4:

[0195] The server processes the received response data and performs sentiment analysis using an emotion engine. This engine analyzes subtle nuances in voice tone and text as input and outputs data indicating the user's emotional state. The server then integrates this sentiment data as part of the user profile, generating a profile that includes traits, strengths, and emotional tendencies.

[0196] Step 5:

[0197] The server uses an artificial intelligence algorithm based on the generated profile to calculate the optimal job placement for the user. This calculation transforms the profile information as input and generates output in the form of optimal roles and placement suggestions for the user. The calculation results are sent to the terminal.

[0198] Step 6:

[0199] Users review the job placement suggestions displayed on their terminals and provide feedback on the system's suggestions. This feedback becomes crucial data for system readjustments and is sent to the server. This results in the provision of a final suggestion that reflects the user's opinions.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0202] In security services and various other operations, appropriate personnel allocation is required, but conventional methods have made it difficult to achieve optimal allocation considering the characteristics and emotional states of staff. This has limited the ability to improve operational efficiency and performance. This invention aims to solve this problem by precisely analyzing the characteristics and emotions of individuals and proposing the most suitable roles based on that analysis.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes means for analyzing voice and text data to analyze the user's emotional state and build an emotion engine; means for a generative model that generates a list of individual questions based on information received from the user; and means for generating a profile to identify the user's characteristics and strengths. This makes it possible to propose optimal role assignments that reflect an individual's characteristics and emotions.

[0205] A "generative model" is an algorithm that automatically generates a list of questions tailored to each user based on their input information.

[0206] An "emotion engine" is a technology that analyzes voice and text data to identify the emotional state of a user.

[0207] A "profile" is a dataset used to record a user's characteristics and strengths, and to manage individual attribute information, including their emotional state.

[0208] "Optimal placement" refers to a placement proposal that aims to distribute roles appropriately based on the characteristics and emotions of users.

[0209] "Feedback" refers to information collected from users, such as responses and opinions, which is used to improve generative models and proposed solutions.

[0210] The system for implementing this invention primarily consists of a server, a terminal, and user interaction. Using a terminal such as a smartphone or tablet, the user enters basic information and logs into the system. The server receives this information and uses a generative AI model to generate a list of individual questions to delve deeper into the user's characteristics.

[0211] The emotion engine analyzes speech-to-text data as the user answers presented questions to identify their emotional state. This process utilizes natural language processing technologies such as Google® Cloud Speech-to-Text API and AWS® Comprehend. The user's responses, along with the emotion data, are sent to the server, where a profile is generated that takes into account their characteristics and emotions.

[0212] Based on the user's profile, the server calculates the optimal role and placement for the user and returns suggestions to the user via the terminal. Individual emotional states are also taken into account, resulting in suggestions for placements that allow the user to perform optimally. Furthermore, user feedback can be collected to dynamically fine-tune the generative model.

[0213] For example, if a security staff member at a specific shopping mall uses this system, questions will be generated based on their experience and characteristics. Monitoring tasks requiring high concentration and composure will be recommended, and this role will be confirmed through sentiment analysis of voice data. Feedback on the final placement suggestions contributes to improving the system's accuracy.

[0214] Specifically, an example of a prompt statement for the generative model of this system is as follows:

[0215] "Please propose a user role assignment. Username: Mr. / Ms. A, Characteristics: Calm, Interests: Electronics, Emotional state: High concentration, Low stress level."

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] Users log in to the system using a terminal. After logging in, users enter basic information such as their name, work experience, and interests. This information is sent from the terminal to the server. The input data includes personal information in text format. The server receives this information and uses it as basic data to create an initial profile.

[0219] Step 2:

[0220] The server activates a generative AI model based on the user's basic information received. The list of questions generated here is designed to delve deeper into the user's characteristics. Analyzing the input basic information, the generative AI model creates a prompt statement containing appropriate questions. This prompt statement is output in the form of questions presented to the user.

[0221] Step 3:

[0222] Users answer a generated list of questions. Responses are entered via the device in either voice or text format. For voice data, the device uses the Google Cloud Speech-to-Text API to convert the audio to text, which is then sent to the server. The server analyzes the received responses and uses a sentiment engine to identify the emotional state. This sentiment analysis uses tools such as AWS Comprehend, and outputs metadata indicating the emotional state.

[0223] Step 4:

[0224] The server generates a profile that shows the user's characteristics and strengths based on the analyzed responses and their emotional states. In this profile generation, emotional states are integrated with the user's characteristic data. The profile is stored on the server and output in a format that can be used as foundational data for role suggestions within the organization.

[0225] Step 5:

[0226] The server calculates the optimal role and placement for the user based on the generated profile. This calculation takes into account characteristic data and emotional state. The optimized role suggestions are output to the user in the form of a display via the terminal.

[0227] Step 6:

[0228] The user provides feedback on the presented role suggestions. This feedback is sent to the server via the terminal. The server uses this feedback to fine-tune the generated AI model and improve the accuracy of future suggestions. The output of this feedback process includes improved tuning parameters for the model and data reflecting the user's further interests.

[0229] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0245] This invention is a system that identifies the characteristics and strengths of each employee within an organization and proposes optimal job placement based on that. This system is mainly composed of a server, terminals, and users, and performs characteristic analysis through an interactive process.

[0246] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server in real time. Based on the received information, the server activates a generative model and creates a list of questions specific to that user.

[0247] Next, the device presents the user with a generated question. The user answers this question and sends the answer to the server via the device. The server then analyzes the received answer and generates a profile to identify the user's characteristics and strengths. This profile details the user's behavioral patterns, strengths, and other relevant information.

[0248] The server then calculates the optimal roles and placements that best utilize the user's strengths, based on their profile. This result is suggested to the user via the terminal. The user can provide feedback on the suggestion and input it into the terminal. This feedback is then sent back to the server and used to further improve and refine the generative model.

[0249] As a concrete example, when user A, who has strong programming development skills, uses the system, the generative model presents interactive questions related to project management and software development. Based on the analysis of the responses, user A is identified as having a strong programming background, and based on this, the system proposes a role for user A as a software engineer. User A then provides feedback on their satisfaction level and any additional requests, and based on this, the system can make further improvements and suggestions.

[0250] Thus, the system of the present invention provides a novel method for accurately understanding the characteristics of individual employees and guiding them to be placed in positions within the organization where they can perform most effectively.

[0251] The following describes the processing flow.

[0252] Step 1:

[0253] Users log in to the system using their terminal and enter basic information such as their name, department, work experience, and interests. The terminal collects this information and sends it to the server.

[0254] Step 2:

[0255] The server activates a generative model based on the received basic information to create a user-specific list of questions. These questions are designed to delve deeper into the user's personality, abilities, and interests.

[0256] Step 3:

[0257] The terminal presents the user with generated questions. The user enters their answers to these questions, and the terminal sends them to the server.

[0258] Step 4:

[0259] The server analyzes the received response data and extracts user characteristics and strengths based on keywords and content within the responses. This analysis organizes the user's behavioral patterns and characteristics into a profile.

[0260] Step 5:

[0261] The server stores the generated characteristic profiles in a database and calculates the optimal roles and placements for users by comparing them with the organization's current situation. This takes into account the organization's structure and the current availability of roles.

[0262] Step 6:

[0263] The terminal receives optimal placement suggestions from the server and displays them to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests.

[0264] Step 7:

[0265] The server collects user feedback and uses it to improve the accuracy of the generative model. Based on this information, the model is fine-tuned to enable more accurate question generation and analysis.

[0266] (Example 1)

[0267] Next, we will describe Example 1. 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."

[0268] Optimizing personnel placement within an organization presents challenges, particularly in accurately understanding individual characteristics and strengths, making it difficult to maximize employee capabilities. Traditional methods, in particular, often fail to analyze individual characteristics in detail, resulting in uniform placements. This can lead to decreased employee motivation and hinder productivity improvements. Furthermore, insufficient mechanisms for effectively utilizing feedback hinder the optimization of personnel placement across the entire organization.

[0269] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0270] In this invention, the server includes means for a user to access the system via a terminal and input basic information, means for transmitting the input information to the server and activating a generative AI model to generate a user-specific list of questions, and means for analyzing the answers received from the user and generating a profile that identifies the user's characteristics and strengths. This makes it possible to accurately grasp the characteristics of each employee and to quickly and effectively propose the optimal role and placement. Furthermore, by including means for fine-tuning the generative AI model used by the server based on user feedback, it is possible to improve the accuracy of placement proposals and promote overall organizational efficiency.

[0271] "User" refers to an individual or member of an organization who uses this system to optimize job assignments.

[0272] A "terminal" refers to an electronic device used by users to access a system and input or output information.

[0273] "System" refers to a set of devices and software configured to understand the characteristics and strengths of users and propose optimal job placements.

[0274] A "server" refers to an information processing device that receives data sent by users and performs analysis and generation.

[0275] A "generative AI model" refers to an artificial intelligence algorithm that generates a list of questions tailored to the user's characteristics based on the input information.

[0276] A "question list" refers to a series of interactive questions created by a generative AI model to delve deeper into the user's characteristics.

[0277] A "profile" refers to a set of information that analyzes and organizes a user's characteristics and strengths.

[0278] "Roles and assignments" refer to the work positions and responsibilities proposed to leverage the characteristics and strengths of the users.

[0279] "Feedback" refers to comments from users regarding evaluations and suggestions for improvement in response to system proposals.

[0280] "Fine-tuning" refers to the process of optimizing the generated AI model and other system elements based on feedback to improve accuracy and effectiveness.

[0281] This invention is a system that understands the characteristics and strengths of users and proposes the optimal job placement. This system mainly consists of a server, terminals, and users, and the hardware and software work together to function.

[0282] First, the user accesses the system using a terminal and enters basic information such as name, work experience, and interests. The terminal verifies this information and sends it to the server. The server creates a personalized question list for the user using an AI model generated based on the received information. This model generates prompt sentences optimized for digging deep into the user's characteristics based on the input data. An example of such a prompt sentence is, "Please generate an individual question list based on the user's basic information (name, work experience, field of interest). Next, analyze the answers and identify the user's characteristics and strengths."

[0283] The question list generated by the server is presented to the user through the terminal. The user answers the questions, and the terminal sends the answers to the server again. The server analyzes the answers using natural language processing technology and generates a profile indicating the user's characteristics and strengths. This profile details the user's behavior patterns and skill sets.

[0284] After that, based on the profile, the server calculates the job placement where the user can be most effectively active. The calculation result is fed back to the user in the form of a proposal through the terminal. The user provides feedback on the proposal, and the information entered into the terminal is sent to the server and used to adjust and improve the generative AI model.

[0285] As a specific example, when User A, who has strengths in program development skills, uses this system, the generative AI model presents questions related to project management and software development to him. As a result of analyzing the answers, a role as a software engineer is proposed for User A. By the user providing feedback on this proposal, further improvement in accuracy can be expected for the system.

[0286] This system provides a new method for performing individually optimized job placement based on the information provided, helping to improve the efficiency of the entire organization.

[0287] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0288] Step 1:

[0289] Users log in to the system via a terminal and enter basic information such as their name, work experience, and interests. This becomes the input data. The terminal verifies this entered information, formats it, and converts it into a digital form for transmission to the server. The formatted data is then sent to the server as output.

[0290] Step 2:

[0291] The server analyzes the basic information received from the terminal and activates the generating AI model. The input data is the user's basic information. The server uses prompt statements to instruct the generating AI model, giving commands such as, "Generate a personalized list of questions based on the user's basic information." As part of the data processing, prompt creation and question list construction are performed within the model. The output is a user-specific list of questions.

[0292] Step 3:

[0293] The terminal receives a list of questions sent from the server and displays them sequentially in the user interface. The input data is the list of questions from the server. The terminal performs actions to present these questions interactively to the user and provides a UI that allows the user to answer. The output is the user's answer to each question.

[0294] Step 4:

[0295] The user enters answers to questions presented via the terminal. These answers become the input data. The terminal formats this data in order to send it to the server. As output, the formatted answer data is sent to the server.

[0296] Step 5:

[0297] The server uses natural language processing techniques to analyze the received responses. The response data is used as input. Based on the analysis results, the server performs data calculations to generate a profile that identifies characteristics and strengths. The output is a profile detailing the user's behavioral patterns and skill set.

[0298] Step 6:

[0299] The server calculates the optimal roles and placements based on the generated profiles. The input data is the profiles. In this process, the AI ​​model compares this data with role data within the organization and performs calculations to identify positions where users can perform at their best. The output is a proposal for optimal job placement.

[0300] Step 7:

[0301] The terminal presents the user with job placement suggestions sent from the server. The input data is the job placement suggestions from the server. The terminal visually displays the suggestions and provides a user interface (UI) that allows selection and requests for further information. User feedback is obtained as output.

[0302] Step 8:

[0303] The user inputs feedback on the proposed layout via the terminal. This feedback becomes the input data. The terminal formats this feedback and sends it to the server. The output is the feedback data used to refine the generated AI model.

[0304] (Application Example 1)

[0305] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0306] Due to the complexity of the working environment and organizational structure, there is a problem that it is difficult to appropriately understand the characteristics of employees and machines and achieve an optimal arrangement. Also, within the factory, an efficient work arrangement considering the characteristics of robots is required, but there is currently a lack of an effective system to implement this.

[0307] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0308] In this invention, the server includes a generation model that generates a question list based on information received from a user, means for providing an interactive question for in-depth exploration of the characteristics of the user, means for analyzing the answer received from the user and generating a profile for identifying the characteristics and strengths of the user, and means for calculating an optimal role and arrangement based on the profile and proposing an optimal arrangement to the user. Thereby, an optimal job and work arrangement can be automatically proposed according to the characteristics of individual employees and robots, enabling an improvement in productivity and efficiency.

[0309] The "generation model" is an algorithm that creates an appropriate question list based on information from the user.

[0310] The "interactive question" is a question format provided by the generation model for in-depth exploration of the characteristics of the user.

[0311] The "profile" is a collection of information that analyzes the characteristics and strengths of the user and records them in detail.

[0312] "Feedback" refers to opinions and feelings about the proposal received from the user.

[0313] The "means for analyzing the characteristics of robots" is a method for evaluating the skills and capabilities of robots in the factory and determining an optimal work arrangement.

[0314] This invention is implemented through a system that analyzes the characteristics of employees and factory robots and proposes the optimal placement. This system works in cooperation with a server, terminals, and users.

[0315] The server uses a generative AI model to generate individual question lists based on user and robot information. Smartphones and tablets are often used as terminals for information gathering. The generated question lists are provided to the user via the terminal. The answers provided by the user are sent from the terminal to the server, where they are analyzed. This analysis identifies characteristics and strengths, and a profile is generated. The profile information is stored in a database and used to inform personnel allocation and robot task assignments across the entire organization.

[0316] As a concrete example, consider a case where the characteristics of a newly introduced robot on a factory production line are analyzed. The server identifies the tasks and operations that the robot excels at and, based on that, proposes the most efficient placement on the production line.

[0317] Examples of prompt statements include the following:

[0318] "User-inputted robot characteristics: [Speed: High, Accuracy: High, Dexterity: Medium] Question: Based on these characteristics, which task is best suited? Answer: Profile generation and optimal placement suggestion based on collected data."

[0319] In this way, the system enables the optimization of the work environment and production efficiency.

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The user uses a terminal to input basic information such as personal information, work experience, interests, and robot characteristics. The entered information is sent to the server via the terminal.

[0323] Input: User information (name, work experience, interests), robot characteristics

[0324] Output: Data transferred to the server

[0325] Step 2:

[0326] Based on the received information, the server activates a generative AI model to generate a user-specific list of questions. This generation process uses natural language processing to create prompt sentences based on the input information.

[0327] Input: Basic information, robot characteristics

[0328] Output: Question List

[0329] Step 3:

[0330] The terminal presents the user with a list of questions received from the server. The user answers the presented interactive questions and sends their answers to the server via the terminal.

[0331] Input: Question list

[0332] Output: User's response

[0333] Step 4:

[0334] The server analyzes user responses and generates profiles to identify characteristics and strengths. The generated profiles are obtained by statistically analyzing the response data and extracting behavioral patterns and skill sets.

[0335] Input: User's response

[0336] Output: Profile

[0337] Step 5:

[0338] The server calculates the optimal roles and placements based on the generated profiles and proposes them to the user. This calculation is performed in a way that matches the required skills within the organization or factory.

[0339] Input: Profile

[0340] Output: Suggested optimal placement

[0341] Step 6:

[0342] The terminal receives optimal placement suggestions from the server and presents them to the user. The user can input feedback into the terminal, which is then sent back to the server and used to improve the generative model.

[0343] Input: Optimal placement proposal

[0344] Output: User feedback

[0345] In this way, the entire system efficiently processes information and continuously provides optimal placement.

[0346] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0347] This invention is an advanced system that understands the characteristics and emotions of each employee within an organization and proposes optimal job placement based on that understanding. By incorporating an emotion engine, this system performs characteristic profiling and placement suggestions that consider not only the user's responses but also their emotional state.

[0348] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server. Upon receiving this information, the server activates a generative model and creates a user-specific list of questions. The questions themselves are designed to delve deeper into the user's characteristics.

[0349] When a user answers a question, the emotion engine uses the voice and text data to perform sentiment analysis. The device also sends this sentiment data to the server. The server analyzes the received responses and sentiment data to generate a profile that identifies characteristics and strengths. Because this profile includes sentiment data, it can also incorporate the user's emotional aspects.

[0350] Next, the server calculates the optimal role and placement for the user based on their profile. Emotional state is also taken into account to suggest an environment where the user can perform best. This suggestion is returned to the user via the terminal, and the user provides feedback on the suggestion.

[0351] For example, if User B is asked questions about program management and team leadership, the emotion engine detects User B's confidence and stress level from the tone of voice used when answering the questions. The server considers this data and suggests that User B is suitable for a project manager position, but suggests that support is needed if the stress level is too high. User B can then provide feedback indicating that leadership training is desirable, allowing the final suggestion to be adjusted.

[0352] In this form, the present invention can reflect not only the user's characteristics but also their emotions, making it possible to propose an organizational arrangement that is more optimized for the individual.

[0353] The following describes the processing flow.

[0354] Step 1:

[0355] The user logs into the system using a terminal and enters necessary basic information such as their name, work experience, and interests. The terminal then sends this information to the server.

[0356] Step 2:

[0357] The server activates a generative model based on the received information to create a user-specific list of questions. These questions are designed to delve deeper into the user's characteristics.

[0358] Step 3:

[0359] The device presents the user with a generated question. The user enters their answer to the question via voice or text. The answer data is temporarily stored on the device.

[0360] Step 4:

[0361] The emotion engine analyzes the user's responses to determine their emotional state by analyzing their voice tone and textual expression. This emotional data is sent to the server along with the response data.

[0362] Step 5:

[0363] The server analyzes the received response data and sentiment data to generate a profile that identifies the user's characteristics and strengths. This profile also includes sentiment data.

[0364] Step 6:

[0365] The server calculates the optimal role and placement for the user based on the generated profile. The calculation results reflect the user's emotional state. This result is sent to the terminal.

[0366] Step 7:

[0367] The terminal displays deployment suggestions from the server to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests. The feedback is sent to the server.

[0368] Step 8:

[0369] The server receives user feedback and uses it to fine-tune the emotion engine and generative models. This information helps improve the accuracy of the models.

[0370] (Example 2)

[0371] Next, we will describe Example 2. 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".

[0372] Traditional job placement systems primarily based placement suggestions on superficial information such as skill sets, but they failed to consider the user's emotions and psychological state, limiting their ability to maximize performance. This resulted in reduced accuracy of placement suggestions and insufficient user satisfaction.

[0373] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0374] In this invention, the server includes means for activating a generation AI model based on basic information received from the user and generating a list of individual questions to understand the user's characteristics; means for analyzing the response data and emotional data received from the user with an emotion engine and generating a profile that identifies the user's characteristics and emotional state; and means for referring to the profile and using an artificial intelligence algorithm to calculate and propose the most suitable job placement for the user. This makes it possible to propose a more accurate job placement that takes the user's emotional state into account.

[0375] "User" refers to an individual or group that operates the system and inputs information.

[0376] A "generative AI model" refers to a computational model that uses artificial intelligence to generate specific tasks or outputs based on input data.

[0377] A "customized question list" refers to a set of questions customized to delve into the characteristics of a specific user.

[0378] "Response data" refers to text or audio information provided by users in response to questions.

[0379] An "emotion engine" refers to a computing device or software that analyzes a user's emotional state from voice and text data.

[0380] A "profile" refers to a collection of data created to represent a user's characteristics, strengths, and emotional state.

[0381] An "artificial intelligence algorithm" refers to a method that enables advanced inference and calculation through programs that run on computers and perform data analysis and prediction.

[0382] "Job assignment" refers to the process of determining the role and position of users within an organization.

[0383] "Feedback" refers to the opinions and reactions that users provide to the content suggested by the system.

[0384] An "information recording device" refers to equipment or a system for storing and managing digital data.

[0385] "Output device" refers to equipment or systems used to display calculation results or data to users.

[0386] This system implements a program designed to achieve optimal job placement for users through a multi-stage process. The system is operated by a server (an information processing device), a terminal (an interface device), and the user.

[0387] 1. The user logs into the system using a terminal. They then enter basic information such as personal information, work experience, and interests. This information is entered according to the format specified on the terminal and sent from the terminal to the server after completion.

[0388] 2. The server analyzes the received information using a generative AI model and generates a personalized list of questions. This list of questions is customized to gain a more specific understanding of the user's characteristics.

[0389] 3. When the user answers the list of questions displayed on the device, they enter their answers as audio or text data. The device then sends this data to the server.

[0390] 4. The server uses an emotion engine to analyze the received response data and identify the user's emotional state from their voice tone and text expression. This information is used to generate a trait profile, which includes the user's skills, traits, and emotional tendencies.

[0391] 5. Based on the generated profile, the server uses an artificial intelligence algorithm to calculate and propose the optimal job placement for the user.

[0392] As a concrete example, if User B answers "Very confident" to the question, "Can you handle a tense situation calmly?", the emotion engine analyzes the degree of confidence from the audio data of that answer. Based on this, it is determined that User B has high stress tolerance and is suitable for a position that can handle pressure. As a final suggestion, placement based on these characteristics is presented to the user.

[0393] Examples of prompt statements include the following:

[0394] "Based on your work experience, what skills do you feel have been particularly useful?"

[0395] "What do you consider most important when starting a new project?"

[0396] These factors enable the system to consider not only the user's characteristics but also their emotions, thereby achieving more personalized job assignments.

[0397] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0398] Step 1:

[0399] The user operates a terminal and logs into the system. After logging in, they enter basic information such as their name, work experience, and interests. Once this information is entered, the terminal checks the input data for consistency and integrity. If the information is correctly formatted, the terminal sends the data to the server. The input data is subjective information of the user, and the output data is a transmittable packet containing this information.

[0400] Step 2:

[0401] Upon receiving basic information from the terminal, the server activates a generative AI model. Using this model, the server generates a personalized list of questions tailored to each user. This process uses the user's basic information as input and outputs questions designed to delve deeper into their characteristics based on that information. The generated question list is then sent back to the terminal.

[0402] Step 3:

[0403] The user answers a list of individual questions displayed on the device. The device supports text and voice input and records the user's responses as digital data. The text and voice data serve as input data for analysis and as output data when transmitted from the device to the server.

[0404] Step 4:

[0405] The server processes the received response data and performs sentiment analysis using an emotion engine. This engine analyzes subtle nuances in voice tone and text as input and outputs data indicating the user's emotional state. The server then integrates this sentiment data as part of the user profile, generating a profile that includes traits, strengths, and emotional tendencies.

[0406] Step 5:

[0407] The server uses an artificial intelligence algorithm based on the generated profile to calculate the optimal job placement for the user. This calculation transforms the profile information as input and generates output in the form of optimal roles and placement suggestions for the user. The calculation results are sent to the terminal.

[0408] Step 6:

[0409] Users review the job placement suggestions displayed on their terminals and provide feedback on the system's suggestions. This feedback becomes crucial data for system readjustments and is sent to the server. This results in the provision of a final suggestion that reflects the user's opinions.

[0410] (Application Example 2)

[0411] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0412] In security services and various other operations, appropriate personnel allocation is required, but conventional methods have made it difficult to achieve optimal allocation considering the characteristics and emotional states of staff. This has limited the ability to improve operational efficiency and performance. This invention aims to solve this problem by precisely analyzing the characteristics and emotions of individuals and proposing the most suitable roles based on that analysis.

[0413] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0414] In this invention, the server includes means for analyzing voice and text data to analyze the user's emotional state and build an emotion engine; means for a generative model that generates a list of individual questions based on information received from the user; and means for generating a profile to identify the user's characteristics and strengths. This makes it possible to propose optimal role assignments that reflect an individual's characteristics and emotions.

[0415] A "generative model" is an algorithm that automatically generates a list of questions tailored to each user based on their input information.

[0416] An "emotion engine" is a technology that analyzes voice and text data to identify the emotional state of a user.

[0417] A "profile" is a dataset used to record a user's characteristics and strengths, and to manage individual attribute information, including their emotional state.

[0418] "Optimal placement" refers to a placement proposal that aims to distribute roles appropriately based on the characteristics and emotions of users.

[0419] "Feedback" refers to information collected from users, such as responses and opinions, which is used to improve generative models and proposed solutions.

[0420] The system for implementing this invention primarily consists of a server, a terminal, and user interaction. Using a terminal such as a smartphone or tablet, the user enters basic information and logs into the system. The server receives this information and uses a generative AI model to generate a list of individual questions to delve deeper into the user's characteristics.

[0421] The emotion engine analyzes speech-to-text data as the user answers presented questions to identify their emotional state. This process utilizes natural language processing technologies such as Google Cloud Speech-to-Text API and AWS Comprehend. The user's responses, along with the emotion data, are sent to the server, where a profile is generated that takes into account their characteristics and emotions.

[0422] Based on the user's profile, the server calculates the optimal role and placement for the user and returns suggestions to the user via the terminal. Individual emotional states are also taken into account, resulting in suggestions for placements that allow the user to perform optimally. Furthermore, user feedback can be collected to dynamically fine-tune the generative model.

[0423] For example, if a security staff member at a specific shopping mall uses this system, questions will be generated based on their experience and characteristics. Monitoring tasks requiring high concentration and composure will be recommended, and this role will be confirmed through sentiment analysis of voice data. Feedback on the final placement suggestions contributes to improving the system's accuracy.

[0424] Specifically, an example of a prompt statement for the generative model of this system is as follows:

[0425] "Please propose a user role assignment. Username: Mr. / Ms. A, Characteristics: Calm, Interests: Electronics, Emotional state: High concentration, Low stress level."

[0426] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0427] Step 1:

[0428] Users log in to the system using a terminal. After logging in, users enter basic information such as their name, work experience, and interests. This information is sent from the terminal to the server. The input data includes personal information in text format. The server receives this information and uses it as basic data to create an initial profile.

[0429] Step 2:

[0430] The server activates a generative AI model based on the user's basic information received. The list of questions generated here is designed to delve deeper into the user's characteristics. Analyzing the input basic information, the generative AI model creates a prompt statement containing appropriate questions. This prompt statement is output in the form of questions presented to the user.

[0431] Step 3:

[0432] Users answer a generated list of questions. Responses are entered via the device in either voice or text format. For voice data, the device uses the Google Cloud Speech-to-Text API to convert the audio to text, which is then sent to the server. The server analyzes the received responses and uses a sentiment engine to identify the emotional state. This sentiment analysis uses tools such as AWS Comprehend, and outputs metadata indicating the emotional state.

[0433] Step 4:

[0434] The server generates a profile that shows the user's characteristics and strengths based on the analyzed responses and their emotional states. In this profile generation, emotional states are integrated with the user's characteristic data. The profile is stored on the server and output in a format that can be used as foundational data for role suggestions within the organization.

[0435] Step 5:

[0436] The server calculates the optimal role and placement for the user based on the generated profile. This calculation takes into account characteristic data and emotional state. The optimized role suggestions are output to the user in the form of a display via the terminal.

[0437] Step 6:

[0438] The user provides feedback on the presented role suggestions. This feedback is sent to the server via the terminal. The server uses this feedback to fine-tune the generated AI model and improve the accuracy of future suggestions. The output of this feedback process includes improved tuning parameters for the model and data reflecting the user's further interests.

[0439] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0440] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0441] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0442] [Third Embodiment]

[0443] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0444] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0445] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0446] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0447] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0448] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0449] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0450] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0451] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0452] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0453] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0454] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0455] This invention is a system that identifies the characteristics and strengths of each employee within an organization and proposes optimal job placement based on that. This system is mainly composed of a server, terminals, and users, and performs characteristic analysis through an interactive process.

[0456] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server in real time. Based on the received information, the server activates a generative model and creates a list of questions specific to that user.

[0457] Next, the device presents the user with a generated question. The user answers this question and sends the answer to the server via the device. The server then analyzes the received answer and generates a profile to identify the user's characteristics and strengths. This profile details the user's behavioral patterns, strengths, and other relevant information.

[0458] The server then calculates the optimal roles and placements that best utilize the user's strengths, based on their profile. This result is suggested to the user via the terminal. The user can provide feedback on the suggestion and input it into the terminal. This feedback is then sent back to the server and used to further improve and refine the generative model.

[0459] As a concrete example, when user A, who has strong programming development skills, uses the system, the generative model presents interactive questions related to project management and software development. Based on the analysis of the responses, user A is identified as having a strong programming background, and based on this, the system proposes a role for user A as a software engineer. User A then provides feedback on their satisfaction level and any additional requests, and based on this, the system can make further improvements and suggestions.

[0460] Thus, the system of the present invention provides a novel method for accurately understanding the characteristics of individual employees and guiding them to be placed in positions within the organization where they can perform most effectively.

[0461] The following describes the processing flow.

[0462] Step 1:

[0463] Users log in to the system using their terminal and enter basic information such as their name, department, work experience, and interests. The terminal collects this information and sends it to the server.

[0464] Step 2:

[0465] The server activates a generative model based on the received basic information to create a user-specific list of questions. These questions are designed to delve deeper into the user's personality, abilities, and interests.

[0466] Step 3:

[0467] The terminal presents the user with generated questions. The user enters their answers to these questions, and the terminal sends them to the server.

[0468] Step 4:

[0469] The server analyzes the received response data and extracts user characteristics and strengths based on keywords and content within the responses. This analysis organizes the user's behavioral patterns and characteristics into a profile.

[0470] Step 5:

[0471] The server stores the generated characteristic profiles in a database and calculates the optimal roles and placements for users by comparing them with the organization's current situation. This takes into account the organization's structure and the current availability of roles.

[0472] Step 6:

[0473] The terminal receives optimal placement suggestions from the server and displays them to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests.

[0474] Step 7:

[0475] The server collects user feedback and uses it to improve the accuracy of the generative model. Based on this information, the model is fine-tuned to enable more accurate question generation and analysis.

[0476] (Example 1)

[0477] Next, we will describe Example 1. 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."

[0478] Optimizing personnel placement within an organization presents challenges, particularly in accurately understanding individual characteristics and strengths, making it difficult to maximize employee capabilities. Traditional methods, in particular, often fail to analyze individual characteristics in detail, resulting in uniform placements. This can lead to decreased employee motivation and hinder productivity improvements. Furthermore, insufficient mechanisms for effectively utilizing feedback hinder the optimization of personnel placement across the entire organization.

[0479] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0480] In this invention, the server includes means for a user to access the system via a terminal and input basic information, means for transmitting the input information to the server and activating a generative AI model to generate a user-specific list of questions, and means for analyzing the answers received from the user and generating a profile that identifies the user's characteristics and strengths. This makes it possible to accurately grasp the characteristics of each employee and to quickly and effectively propose the optimal role and placement. Furthermore, by including means for fine-tuning the generative AI model used by the server based on user feedback, it is possible to improve the accuracy of placement proposals and promote overall organizational efficiency.

[0481] "User" refers to an individual or member of an organization who uses this system to optimize job assignments.

[0482] A "terminal" refers to an electronic device used by users to access a system and input or output information.

[0483] "System" refers to a set of devices and software configured to understand the characteristics and strengths of users and propose optimal job placements.

[0484] A "server" refers to an information processing device that receives data sent by users and performs analysis and generation.

[0485] A "generative AI model" refers to an artificial intelligence algorithm that generates a list of questions tailored to the user's characteristics based on the input information.

[0486] A "question list" refers to a series of interactive questions created by a generative AI model to delve deeper into the user's characteristics.

[0487] A "profile" refers to a set of information that analyzes and organizes a user's characteristics and strengths.

[0488] "Roles and assignments" refer to the work positions and responsibilities proposed to leverage the characteristics and strengths of the users.

[0489] "Feedback" refers to comments from users regarding evaluations and suggestions for improvement in response to system proposals.

[0490] "Fine-tuning" refers to the process of optimizing the generated AI model and other system elements based on feedback to improve accuracy and effectiveness.

[0491] This invention is a system that understands the characteristics and strengths of users and proposes the optimal job placement. This system mainly consists of a server, terminals, and users, and the hardware and software work together to function.

[0492] First, the user accesses the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal verifies this information and sends it to the server. Based on the received information, the server uses a generative AI model to create a user-specific list of questions. This model generates optimized prompts based on the input data to delve deeper into the user's characteristics. An example of such a prompt might be: "Generate a personalized list of questions based on the user's basic information (name, work experience, areas of interest). Then, analyze the answers to identify the user's characteristics and strengths."

[0493] A list of questions generated by the server is presented to the user via the terminal. The user answers the questions, and the terminal sends the answers back to the server. The server uses natural language processing technology to analyze the answers and generate a profile that shows the user's characteristics and strengths. This profile describes the user's behavioral patterns and skill set in detail.

[0494] Subsequently, the server calculates the job placement that would allow the user to perform most effectively, based on their profile. This calculation result is then fed back to the user in the form of a suggestion via the terminal. The user provides feedback on the suggestion, and the information entered into the terminal is sent to the server to help adjust and improve the generated AI model.

[0495] As a concrete example, if user A, who has strong programming development skills, uses this system, the generating AI model will present him with questions related to project management and software development. Based on the analysis of his answers, the role of a software engineer will be suggested to user A. By providing feedback on this suggestion, the system can be expected to improve further in accuracy.

[0496] This system provides a new method for individually optimized role assignments based on the information provided, helping to improve overall organizational efficiency.

[0497] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0498] Step 1:

[0499] Users log in to the system via a terminal and enter basic information such as their name, work experience, and interests. This becomes the input data. The terminal verifies this entered information, formats it, and converts it into a digital form for transmission to the server. The formatted data is then sent to the server as output.

[0500] Step 2:

[0501] The server analyzes the basic information received from the terminal and activates the generating AI model. The input data is the user's basic information. The server uses prompt statements to instruct the generating AI model, giving commands such as, "Generate a personalized list of questions based on the user's basic information." As part of the data processing, prompt creation and question list construction are performed within the model. The output is a user-specific list of questions.

[0502] Step 3:

[0503] The terminal receives a list of questions sent from the server and displays them sequentially in the user interface. The input data is the list of questions from the server. The terminal performs actions to present these questions interactively to the user and provides a UI that allows the user to answer. The output is the user's answer to each question.

[0504] Step 4:

[0505] The user enters answers to questions presented via the terminal. These answers become the input data. The terminal formats this data in order to send it to the server. As output, the formatted answer data is sent to the server.

[0506] Step 5:

[0507] The server uses natural language processing techniques to analyze the received responses. The response data is used as input. Based on the analysis results, the server performs data calculations to generate a profile that identifies characteristics and strengths. The output is a profile detailing the user's behavioral patterns and skill set.

[0508] Step 6:

[0509] The server calculates the optimal roles and placements based on the generated profiles. The input data is the profiles. In this process, the AI ​​model compares this data with role data within the organization and performs calculations to identify positions where users can perform at their best. The output is a proposal for optimal job placement.

[0510] Step 7:

[0511] The terminal presents the user with job placement suggestions sent from the server. The input data is the job placement suggestions from the server. The terminal visually displays the suggestions and provides a user interface (UI) that allows selection and requests for further information. User feedback is obtained as output.

[0512] Step 8:

[0513] The user inputs feedback on the proposed layout via the terminal. This feedback becomes the input data. The terminal formats this feedback and sends it to the server. The output is the feedback data used to refine the generated AI model.

[0514] (Application Example 1)

[0515] Next, we will explain Application Example 1. In the following explanation, 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."

[0516] The increasing complexity of work environments and organizational structures presents challenges in appropriately understanding the characteristics of employees and machinery and achieving optimal allocation. Furthermore, while efficient work allocation considering the characteristics of robots is required within factories, there is currently a lack of effective systems to implement this.

[0517] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0518] In this invention, the server includes a generative model that generates a list of questions based on information received from a user, means for providing interactive questions to delve deeper into the user's characteristics, means for analyzing the answers received from the user and generating a profile to identify the user's characteristics and strengths, and means for calculating the optimal role and placement based on the profile and proposing the optimal placement to the user. This enables the automatic proposal of optimal job duties and work assignments according to the characteristics of individual employees or robots, thereby improving productivity and efficiency.

[0519] A "generative model" is an algorithm that creates an appropriate list of questions based on information from the user.

[0520] "Interactive questioning" is a question format provided by generative models to delve deeper into the characteristics of users.

[0521] A "profile" is a collection of information that analyzes and records in detail the characteristics and strengths of a user.

[0522] "Feedback" refers to opinions and impressions received from users regarding suggestions.

[0523] "Methods for analyzing robot characteristics" refer to methods for evaluating the skills and capabilities of robots in a factory and determining the optimal work assignments.

[0524] This invention is implemented through a system that analyzes the characteristics of employees and factory robots and proposes the optimal placement. This system works in cooperation with a server, terminals, and users.

[0525] The server uses a generative AI model to generate individual question lists based on user and robot information. Smartphones and tablets are often used as terminals for information gathering. The generated question lists are provided to the user via the terminal. The answers provided by the user are sent from the terminal to the server, where they are analyzed. This analysis identifies characteristics and strengths, and a profile is generated. The profile information is stored in a database and used to inform personnel allocation and robot task assignments across the entire organization.

[0526] As a concrete example, consider a case where the characteristics of a newly introduced robot on a factory production line are analyzed. The server identifies the tasks and operations that the robot excels at and, based on that, proposes the most efficient placement on the production line.

[0527] Examples of prompt statements include the following:

[0528] "User-inputted robot characteristics: [Speed: High, Accuracy: High, Dexterity: Medium] Question: Based on these characteristics, which task is best suited? Answer: Profile generation and optimal placement suggestion based on collected data."

[0529] In this way, the system enables the optimization of the work environment and production efficiency.

[0530] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0531] Step 1:

[0532] The user uses a terminal to input basic information such as personal information, work experience, interests, and robot characteristics. The entered information is sent to the server via the terminal.

[0533] Input: User information (name, work experience, interests), robot characteristics

[0534] Output: Data transferred to the server

[0535] Step 2:

[0536] Based on the received information, the server activates a generative AI model to generate a user-specific list of questions. This generation process uses natural language processing to create prompt sentences based on the input information.

[0537] Input: Basic information, robot characteristics

[0538] Output: Question List

[0539] Step 3:

[0540] The terminal presents the user with a list of questions received from the server. The user answers the presented interactive questions and sends their answers to the server via the terminal.

[0541] Input: Question list

[0542] Output: User's response

[0543] Step 4:

[0544] The server analyzes user responses and generates profiles to identify characteristics and strengths. The generated profiles are obtained by statistically analyzing the response data and extracting behavioral patterns and skill sets.

[0545] Input: User's response

[0546] Output: Profile

[0547] Step 5:

[0548] The server calculates the optimal roles and placements based on the generated profiles and proposes them to the user. This calculation is performed in a way that matches the required skills within the organization or factory.

[0549] Input: Profile

[0550] Output: Suggested optimal placement

[0551] Step 6:

[0552] The terminal receives optimal placement suggestions from the server and presents them to the user. The user can input feedback into the terminal, which is then sent back to the server and used to improve the generative model.

[0553] Input: Optimal placement proposal

[0554] Output: User feedback

[0555] In this way, the entire system efficiently processes information and continuously provides optimal placement.

[0556] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0557] This invention is an advanced system that understands the characteristics and emotions of each employee within an organization and proposes optimal job placement based on that understanding. By incorporating an emotion engine, this system performs characteristic profiling and placement suggestions that consider not only the user's responses but also their emotional state.

[0558] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server. Upon receiving this information, the server activates a generative model and creates a user-specific list of questions. The questions themselves are designed to delve deeper into the user's characteristics.

[0559] When a user answers a question, the emotion engine uses the voice and text data to perform sentiment analysis. The device also sends this sentiment data to the server. The server analyzes the received responses and sentiment data to generate a profile that identifies characteristics and strengths. Because this profile includes sentiment data, it can also incorporate the user's emotional aspects.

[0560] Next, the server calculates the optimal role and placement for the user based on their profile. Emotional state is also taken into account to suggest an environment where the user can perform best. This suggestion is returned to the user via the terminal, and the user provides feedback on the suggestion.

[0561] For example, if User B is asked questions about program management and team leadership, the emotion engine detects User B's confidence and stress level from the tone of voice used when answering the questions. The server considers this data and suggests that User B is suitable for a project manager position, but suggests that support is needed if the stress level is too high. User B can then provide feedback indicating that leadership training is desirable, allowing the final suggestion to be adjusted.

[0562] In this form, the present invention can reflect not only the user's characteristics but also their emotions, making it possible to propose an organizational arrangement that is more optimized for the individual.

[0563] The following describes the processing flow.

[0564] Step 1:

[0565] The user logs into the system using a terminal and enters necessary basic information such as their name, work experience, and interests. The terminal then sends this information to the server.

[0566] Step 2:

[0567] The server activates a generative model based on the received information to create a user-specific list of questions. These questions are designed to delve deeper into the user's characteristics.

[0568] Step 3:

[0569] The device presents the user with a generated question. The user enters their answer to the question via voice or text. The answer data is temporarily stored on the device.

[0570] Step 4:

[0571] The emotion engine analyzes the user's responses to determine their emotional state by analyzing their voice tone and textual expression. This emotional data is sent to the server along with the response data.

[0572] Step 5:

[0573] The server analyzes the received response data and sentiment data to generate a profile that identifies the user's characteristics and strengths. This profile also includes sentiment data.

[0574] Step 6:

[0575] The server calculates the optimal role and placement for the user based on the generated profile. The calculation results reflect the user's emotional state. This result is sent to the terminal.

[0576] Step 7:

[0577] The terminal displays deployment suggestions from the server to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests. The feedback is sent to the server.

[0578] Step 8:

[0579] The server receives user feedback and uses it to fine-tune the emotion engine and generative models. This information helps improve the accuracy of the models.

[0580] (Example 2)

[0581] Next, we will describe Example 2. 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."

[0582] Traditional job placement systems primarily based placement suggestions on superficial information such as skill sets, but they failed to consider the user's emotions and psychological state, limiting their ability to maximize performance. This resulted in reduced accuracy of placement suggestions and insufficient user satisfaction.

[0583] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0584] In this invention, the server includes means for activating a generation AI model based on basic information received from the user and generating a list of individual questions to understand the user's characteristics; means for analyzing the response data and emotional data received from the user with an emotion engine and generating a profile that identifies the user's characteristics and emotional state; and means for referring to the profile and using an artificial intelligence algorithm to calculate and propose the most suitable job placement for the user. This makes it possible to propose a more accurate job placement that takes the user's emotional state into account.

[0585] "User" refers to an individual or group that operates the system and inputs information.

[0586] A "generative AI model" refers to a computational model that uses artificial intelligence to generate specific tasks or outputs based on input data.

[0587] A "customized question list" refers to a set of questions customized to delve into the characteristics of a specific user.

[0588] "Response data" refers to text or audio information provided by users in response to questions.

[0589] An "emotion engine" refers to a computing device or software that analyzes a user's emotional state from voice and text data.

[0590] A "profile" refers to a collection of data created to represent a user's characteristics, strengths, and emotional state.

[0591] An "artificial intelligence algorithm" refers to a method that enables advanced inference and calculation through programs that run on computers and perform data analysis and prediction.

[0592] "Job assignment" refers to the process of determining the role and position of users within an organization.

[0593] "Feedback" refers to the opinions and reactions that users provide to the content suggested by the system.

[0594] An "information recording device" refers to equipment or a system for storing and managing digital data.

[0595] "Output device" refers to equipment or systems used to display calculation results or data to users.

[0596] This system implements a program designed to achieve optimal job placement for users through a multi-stage process. The system is operated by a server (an information processing device), a terminal (an interface device), and the user.

[0597] 1. The user logs into the system using a terminal. They then enter basic information such as personal information, work experience, and interests. This information is entered according to the format specified on the terminal and sent from the terminal to the server after completion.

[0598] 2. The server analyzes the received information using a generative AI model and generates a personalized list of questions. This list of questions is customized to gain a more specific understanding of the user's characteristics.

[0599] 3. When the user answers the list of questions displayed on the device, they enter their answers as audio or text data. The device then sends this data to the server.

[0600] 4. The server uses an emotion engine to analyze the received response data and identify the user's emotional state from their voice tone and text expression. This information is used to generate a trait profile, which includes the user's skills, traits, and emotional tendencies.

[0601] 5. Based on the generated profile, the server uses an artificial intelligence algorithm to calculate and propose the optimal job placement for the user.

[0602] As a concrete example, if User B answers "Very confident" to the question, "Can you handle a tense situation calmly?", the emotion engine analyzes the degree of confidence from the audio data of that answer. Based on this, it is determined that User B has high stress tolerance and is suitable for a position that can handle pressure. As a final suggestion, placement based on these characteristics is presented to the user.

[0603] Examples of prompt statements include the following:

[0604] "Based on your work experience, what skills do you feel have been particularly useful?"

[0605] "What do you consider most important when starting a new project?"

[0606] These factors enable the system to consider not only the user's characteristics but also their emotions, thereby achieving more personalized job assignments.

[0607] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0608] Step 1:

[0609] The user operates a terminal and logs into the system. After logging in, they enter basic information such as their name, work experience, and interests. Once this information is entered, the terminal checks the input data for consistency and integrity. If the information is correctly formatted, the terminal sends the data to the server. The input data is subjective information of the user, and the output data is a transmittable packet containing this information.

[0610] Step 2:

[0611] Upon receiving basic information from the terminal, the server activates a generative AI model. Using this model, the server generates a personalized list of questions tailored to each user. This process uses the user's basic information as input and outputs questions designed to delve deeper into their characteristics based on that information. The generated question list is then sent back to the terminal.

[0612] Step 3:

[0613] The user answers a list of individual questions displayed on the device. The device supports text and voice input and records the user's responses as digital data. The text and voice data serve as input data for analysis and as output data when transmitted from the device to the server.

[0614] Step 4:

[0615] The server processes the received response data and performs sentiment analysis using an emotion engine. This engine analyzes subtle nuances in voice tone and text as input and outputs data indicating the user's emotional state. The server then integrates this sentiment data as part of the user profile, generating a profile that includes traits, strengths, and emotional tendencies.

[0616] Step 5:

[0617] The server uses an artificial intelligence algorithm based on the generated profile to calculate the optimal job placement for the user. This calculation transforms the profile information as input and generates output in the form of optimal roles and placement suggestions for the user. The calculation results are sent to the terminal.

[0618] Step 6:

[0619] Users review the job placement suggestions displayed on their terminals and provide feedback on the system's suggestions. This feedback becomes crucial data for system readjustments and is sent to the server. This results in the provision of a final suggestion that reflects the user's opinions.

[0620] (Application Example 2)

[0621] Next, we will explain application example 2. In the following explanation, 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."

[0622] In security services and various other operations, appropriate personnel allocation is required, but conventional methods have made it difficult to achieve optimal allocation considering the characteristics and emotional states of staff. This has limited the ability to improve operational efficiency and performance. This invention aims to solve this problem by precisely analyzing the characteristics and emotions of individuals and proposing the most suitable roles based on that analysis.

[0623] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0624] In this invention, the server includes means for analyzing voice and text data to analyze the user's emotional state and build an emotion engine; means for a generative model that generates a list of individual questions based on information received from the user; and means for generating a profile to identify the user's characteristics and strengths. This makes it possible to propose optimal role assignments that reflect an individual's characteristics and emotions.

[0625] A "generative model" is an algorithm that automatically generates a list of questions tailored to each user based on their input information.

[0626] An "emotion engine" is a technology that analyzes voice and text data to identify the emotional state of a user.

[0627] A "profile" is a dataset used to record a user's characteristics and strengths, and to manage individual attribute information, including their emotional state.

[0628] "Optimal placement" refers to a placement proposal that aims to distribute roles appropriately based on the characteristics and emotions of users.

[0629] "Feedback" refers to information collected from users, such as responses and opinions, which is used to improve generative models and proposed solutions.

[0630] The system for implementing this invention primarily consists of a server, a terminal, and user interaction. Using a terminal such as a smartphone or tablet, the user enters basic information and logs into the system. The server receives this information and uses a generative AI model to generate a list of individual questions to delve deeper into the user's characteristics.

[0631] The emotion engine analyzes speech-to-text data as the user answers presented questions to identify their emotional state. This process utilizes natural language processing technologies such as Google Cloud Speech-to-Text API and AWS Comprehend. The user's responses, along with the emotion data, are sent to the server, where a profile is generated that takes into account their characteristics and emotions.

[0632] Based on the user's profile, the server calculates the optimal role and placement for the user and returns suggestions to the user via the terminal. Individual emotional states are also taken into account, resulting in suggestions for placements that allow the user to perform optimally. Furthermore, user feedback can be collected to dynamically fine-tune the generative model.

[0633] For example, if a security staff member at a specific shopping mall uses this system, questions will be generated based on their experience and characteristics. Monitoring tasks requiring high concentration and composure will be recommended, and this role will be confirmed through sentiment analysis of voice data. Feedback on the final placement suggestions contributes to improving the system's accuracy.

[0634] Specifically, an example of a prompt statement for the generative model of this system is as follows:

[0635] "Please propose a user role assignment. Username: Mr. / Ms. A, Characteristics: Calm, Interests: Electronics, Emotional state: High concentration, Low stress level."

[0636] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0637] Step 1:

[0638] Users log in to the system using a terminal. After logging in, users enter basic information such as their name, work experience, and interests. This information is sent from the terminal to the server. The input data includes personal information in text format. The server receives this information and uses it as basic data to create an initial profile.

[0639] Step 2:

[0640] The server activates a generative AI model based on the user's basic information received. The list of questions generated here is designed to delve deeper into the user's characteristics. Analyzing the input basic information, the generative AI model creates a prompt statement containing appropriate questions. This prompt statement is output in the form of questions presented to the user.

[0641] Step 3:

[0642] Users answer a generated list of questions. Responses are entered via the device in either voice or text format. For voice data, the device uses the Google Cloud Speech-to-Text API to convert the audio to text, which is then sent to the server. The server analyzes the received responses and uses a sentiment engine to identify the emotional state. This sentiment analysis uses tools such as AWS Comprehend, and outputs metadata indicating the emotional state.

[0643] Step 4:

[0644] The server generates a profile that shows the user's characteristics and strengths based on the analyzed responses and their emotional states. In this profile generation, emotional states are integrated with the user's characteristic data. The profile is stored on the server and output in a format that can be used as foundational data for role suggestions within the organization.

[0645] Step 5:

[0646] The server calculates the optimal role and placement for the user based on the generated profile. This calculation takes into account characteristic data and emotional state. The optimized role suggestions are output to the user in the form of a display via the terminal.

[0647] Step 6:

[0648] The user provides feedback on the presented role suggestions. This feedback is sent to the server via the terminal. The server uses this feedback to fine-tune the generated AI model and improve the accuracy of future suggestions. The output of this feedback process includes improved tuning parameters for the model and data reflecting the user's further interests.

[0649] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0650] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0651] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0652] [Fourth Embodiment]

[0653] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0654] As shown in Figure 7, the 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.

[0655] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0656] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0657] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0658] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0659] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0660] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0661] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0662] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0663] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0664] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0665] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0666] This invention is a system that identifies the characteristics and strengths of each employee within an organization and proposes optimal job placement based on that. This system is mainly composed of a server, terminals, and users, and performs characteristic analysis through an interactive process.

[0667] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server in real time. Based on the received information, the server activates a generative model and creates a list of questions specific to that user.

[0668] Next, the device presents the user with a generated question. The user answers this question and sends the answer to the server via the device. The server then analyzes the received answer and generates a profile to identify the user's characteristics and strengths. This profile details the user's behavioral patterns, strengths, and other relevant information.

[0669] The server then calculates the optimal roles and placements that best utilize the user's strengths, based on their profile. This result is suggested to the user via the terminal. The user can provide feedback on the suggestion and input it into the terminal. This feedback is then sent back to the server and used to further improve and refine the generative model.

[0670] As a concrete example, when user A, who has strong programming development skills, uses the system, the generative model presents interactive questions related to project management and software development. Based on the analysis of the responses, user A is identified as having a strong programming background, and based on this, the system proposes a role for user A as a software engineer. User A then provides feedback on their satisfaction level and any additional requests, and based on this, the system can make further improvements and suggestions.

[0671] Thus, the system of the present invention provides a novel method for accurately understanding the characteristics of individual employees and guiding them to be placed in positions within the organization where they can perform most effectively.

[0672] The following describes the processing flow.

[0673] Step 1:

[0674] Users log in to the system using their terminal and enter basic information such as their name, department, work experience, and interests. The terminal collects this information and sends it to the server.

[0675] Step 2:

[0676] The server activates a generative model based on the received basic information to create a user-specific list of questions. These questions are designed to delve deeper into the user's personality, abilities, and interests.

[0677] Step 3:

[0678] The terminal presents the user with generated questions. The user enters their answers to these questions, and the terminal sends them to the server.

[0679] Step 4:

[0680] The server analyzes the received response data and extracts user characteristics and strengths based on keywords and content within the responses. This analysis organizes the user's behavioral patterns and characteristics into a profile.

[0681] Step 5:

[0682] The server stores the generated characteristic profiles in a database and calculates the optimal roles and placements for users by comparing them with the organization's current situation. This takes into account the organization's structure and the current availability of roles.

[0683] Step 6:

[0684] The terminal receives optimal placement suggestions from the server and displays them to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests.

[0685] Step 7:

[0686] The server collects user feedback and uses it to improve the accuracy of the generative model. Based on this information, the model is fine-tuned to enable more accurate question generation and analysis.

[0687] (Example 1)

[0688] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0689] Optimizing personnel placement within an organization presents challenges, particularly in accurately understanding individual characteristics and strengths, making it difficult to maximize employee capabilities. Traditional methods, in particular, often fail to analyze individual characteristics in detail, resulting in uniform placements. This can lead to decreased employee motivation and hinder productivity improvements. Furthermore, insufficient mechanisms for effectively utilizing feedback hinder the optimization of personnel placement across the entire organization.

[0690] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0691] In this invention, the server includes means for a user to access the system via a terminal and input basic information, means for transmitting the input information to the server and activating a generative AI model to generate a user-specific list of questions, and means for analyzing the answers received from the user and generating a profile that identifies the user's characteristics and strengths. This makes it possible to accurately grasp the characteristics of each employee and to quickly and effectively propose the optimal role and placement. Furthermore, by including means for fine-tuning the generative AI model used by the server based on user feedback, it is possible to improve the accuracy of placement proposals and promote overall organizational efficiency.

[0692] "User" refers to an individual or member of an organization who uses this system to optimize job assignments.

[0693] A "terminal" refers to an electronic device used by users to access a system and input or output information.

[0694] "System" refers to a set of devices and software configured to understand the characteristics and strengths of users and propose optimal job placements.

[0695] A "server" refers to an information processing device that receives data sent by users and performs analysis and generation.

[0696] A "generative AI model" refers to an artificial intelligence algorithm that generates a list of questions tailored to the user's characteristics based on the input information.

[0697] A "question list" refers to a series of interactive questions created by a generative AI model to delve deeper into the user's characteristics.

[0698] A "profile" refers to a set of information that analyzes and organizes a user's characteristics and strengths.

[0699] "Roles and assignments" refer to the work positions and responsibilities proposed to leverage the characteristics and strengths of the users.

[0700] "Feedback" refers to comments from users regarding evaluations and suggestions for improvement in response to system proposals.

[0701] "Fine-tuning" refers to the process of optimizing the generated AI model and other system elements based on feedback to improve accuracy and effectiveness.

[0702] This invention is a system that understands the characteristics and strengths of users and proposes the optimal job placement. This system mainly consists of a server, terminals, and users, and the hardware and software work together to function.

[0703] First, the user accesses the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal verifies this information and sends it to the server. Based on the received information, the server uses a generative AI model to create a user-specific list of questions. This model generates optimized prompts based on the input data to delve deeper into the user's characteristics. An example of such a prompt might be: "Generate a personalized list of questions based on the user's basic information (name, work experience, areas of interest). Then, analyze the answers to identify the user's characteristics and strengths."

[0704] A list of questions generated by the server is presented to the user via the terminal. The user answers the questions, and the terminal sends the answers back to the server. The server uses natural language processing technology to analyze the answers and generate a profile that shows the user's characteristics and strengths. This profile describes the user's behavioral patterns and skill set in detail.

[0705] Subsequently, the server calculates the job placement that would allow the user to perform most effectively, based on their profile. This calculation result is then fed back to the user in the form of a suggestion via the terminal. The user provides feedback on the suggestion, and the information entered into the terminal is sent to the server to help adjust and improve the generated AI model.

[0706] As a concrete example, if user A, who has strong programming development skills, uses this system, the generating AI model will present him with questions related to project management and software development. Based on the analysis of his answers, the role of a software engineer will be suggested to user A. By providing feedback on this suggestion, the system can be expected to improve further in accuracy.

[0707] This system provides a new method for individually optimized role assignments based on the information provided, helping to improve overall organizational efficiency.

[0708] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0709] Step 1:

[0710] Users log in to the system via a terminal and enter basic information such as their name, work experience, and interests. This becomes the input data. The terminal verifies this entered information, formats it, and converts it into a digital form for transmission to the server. The formatted data is then sent to the server as output.

[0711] Step 2:

[0712] The server analyzes the basic information received from the terminal and activates the generating AI model. The input data is the user's basic information. The server uses prompt statements to instruct the generating AI model, giving commands such as, "Generate a personalized list of questions based on the user's basic information." As part of the data processing, prompt creation and question list construction are performed within the model. The output is a user-specific list of questions.

[0713] Step 3:

[0714] The terminal receives a list of questions sent from the server and displays them sequentially in the user interface. The input data is the list of questions from the server. The terminal performs actions to present these questions interactively to the user and provides a UI that allows the user to answer. The output is the user's answer to each question.

[0715] Step 4:

[0716] The user enters answers to questions presented via the terminal. These answers become the input data. The terminal formats this data in order to send it to the server. As output, the formatted answer data is sent to the server.

[0717] Step 5:

[0718] The server uses natural language processing techniques to analyze the received responses. The response data is used as input. Based on the analysis results, the server performs data calculations to generate a profile that identifies characteristics and strengths. The output is a profile detailing the user's behavioral patterns and skill set.

[0719] Step 6:

[0720] The server calculates the optimal roles and placements based on the generated profiles. The input data is the profiles. In this process, the AI ​​model compares this data with role data within the organization and performs calculations to identify positions where users can perform at their best. The output is a proposal for optimal job placement.

[0721] Step 7:

[0722] The terminal presents the user with job placement suggestions sent from the server. The input data is the job placement suggestions from the server. The terminal visually displays the suggestions and provides a user interface (UI) that allows selection and requests for further information. User feedback is obtained as output.

[0723] Step 8:

[0724] The user inputs feedback on the proposed layout via the terminal. This feedback becomes the input data. The terminal formats this feedback and sends it to the server. The output is the feedback data used to refine the generated AI model.

[0725] (Application Example 1)

[0726] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0727] The increasing complexity of work environments and organizational structures presents challenges in appropriately understanding the characteristics of employees and machinery and achieving optimal allocation. Furthermore, while efficient work allocation considering the characteristics of robots is required within factories, there is currently a lack of effective systems to implement this.

[0728] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0729] In this invention, the server includes a generative model that generates a list of questions based on information received from a user, means for providing interactive questions to delve deeper into the user's characteristics, means for analyzing the answers received from the user and generating a profile to identify the user's characteristics and strengths, and means for calculating the optimal role and placement based on the profile and proposing the optimal placement to the user. This enables the automatic proposal of optimal job duties and work assignments according to the characteristics of individual employees or robots, thereby improving productivity and efficiency.

[0730] A "generative model" is an algorithm that creates an appropriate list of questions based on information from the user.

[0731] "Interactive questioning" is a question format provided by generative models to delve deeper into the characteristics of users.

[0732] A "profile" is a collection of information that analyzes and records in detail the characteristics and strengths of a user.

[0733] "Feedback" refers to opinions and impressions received from users regarding suggestions.

[0734] "Methods for analyzing robot characteristics" refer to methods for evaluating the skills and capabilities of robots in a factory and determining the optimal work assignments.

[0735] This invention is implemented through a system that analyzes the characteristics of employees and factory robots and proposes the optimal placement. This system works in cooperation with a server, terminals, and users.

[0736] The server uses a generative AI model to generate individual question lists based on user and robot information. Smartphones and tablets are often used as terminals for information gathering. The generated question lists are provided to the user via the terminal. The answers provided by the user are sent from the terminal to the server, where they are analyzed. This analysis identifies characteristics and strengths, and a profile is generated. The profile information is stored in a database and used to inform personnel allocation and robot task assignments across the entire organization.

[0737] As a concrete example, consider a case where the characteristics of a newly introduced robot on a factory production line are analyzed. The server identifies the tasks and operations that the robot excels at and, based on that, proposes the most efficient placement on the production line.

[0738] Examples of prompt statements include the following:

[0739] "User-inputted robot characteristics: [Speed: High, Accuracy: High, Dexterity: Medium] Question: Based on these characteristics, which task is best suited? Answer: Profile generation and optimal placement suggestion based on collected data."

[0740] In this way, the system enables the optimization of the work environment and production efficiency.

[0741] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0742] Step 1:

[0743] The user uses a terminal to input basic information such as personal information, work experience, interests, and robot characteristics. The entered information is sent to the server via the terminal.

[0744] Input: User information (name, work experience, interests), robot characteristics

[0745] Output: Data transferred to the server

[0746] Step 2:

[0747] Based on the received information, the server activates a generative AI model to generate a user-specific list of questions. This generation process uses natural language processing to create prompt sentences based on the input information.

[0748] Input: Basic information, robot characteristics

[0749] Output: Question List

[0750] Step 3:

[0751] The terminal presents the user with a list of questions received from the server. The user answers the presented interactive questions and sends their answers to the server via the terminal.

[0752] Input: Question list

[0753] Output: User's response

[0754] Step 4:

[0755] The server analyzes user responses and generates profiles to identify characteristics and strengths. The generated profiles are obtained by statistically analyzing the response data and extracting behavioral patterns and skill sets.

[0756] Input: User's response

[0757] Output: Profile

[0758] Step 5:

[0759] The server calculates the optimal roles and placements based on the generated profiles and proposes them to the user. This calculation is performed in a way that matches the required skills within the organization or factory.

[0760] Input: Profile

[0761] Output: Suggested optimal placement

[0762] Step 6:

[0763] The terminal receives optimal placement suggestions from the server and presents them to the user. The user can input feedback into the terminal, which is then sent back to the server and used to improve the generative model.

[0764] Input: Optimal placement proposal

[0765] Output: User feedback

[0766] In this way, the entire system efficiently processes information and continuously provides optimal placement.

[0767] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0768] This invention is an advanced system that understands the characteristics and emotions of each employee within an organization and proposes optimal job placement based on that understanding. By incorporating an emotion engine, this system performs characteristic profiling and placement suggestions that consider not only the user's responses but also their emotional state.

[0769] First, the user logs into the system using a terminal and enters basic information such as their name, work experience, and interests. The terminal sends this information to the server. Upon receiving this information, the server activates a generative model and creates a user-specific list of questions. The questions themselves are designed to delve deeper into the user's characteristics.

[0770] When a user answers a question, the emotion engine uses the voice and text data to perform sentiment analysis. The device also sends this sentiment data to the server. The server analyzes the received responses and sentiment data to generate a profile that identifies characteristics and strengths. Because this profile includes sentiment data, it can also incorporate the user's emotional aspects.

[0771] Next, the server calculates the optimal role and placement for the user based on their profile. Emotional state is also taken into account to suggest an environment where the user can perform best. This suggestion is returned to the user via the terminal, and the user provides feedback on the suggestion.

[0772] For example, if User B is asked questions about program management and team leadership, the emotion engine detects User B's confidence and stress level from the tone of voice used when answering the questions. The server considers this data and suggests that User B is suitable for a project manager position, but suggests that support is needed if the stress level is too high. User B can then provide feedback indicating that leadership training is desirable, allowing the final suggestion to be adjusted.

[0773] In this form, the present invention can reflect not only the user's characteristics but also their emotions, making it possible to propose an organizational arrangement that is more optimized for the individual.

[0774] The following describes the processing flow.

[0775] Step 1:

[0776] The user logs into the system using a terminal and enters necessary basic information such as their name, work experience, and interests. The terminal then sends this information to the server.

[0777] Step 2:

[0778] The server activates a generative model based on the received information to create a user-specific list of questions. These questions are designed to delve deeper into the user's characteristics.

[0779] Step 3:

[0780] The device presents the user with a generated question. The user enters their answer to the question via voice or text. The answer data is temporarily stored on the device.

[0781] Step 4:

[0782] The emotion engine analyzes the user's responses to determine their emotional state by analyzing their voice tone and textual expression. This emotional data is sent to the server along with the response data.

[0783] Step 5:

[0784] The server analyzes the received response data and sentiment data to generate a profile that identifies the user's characteristics and strengths. This profile also includes sentiment data.

[0785] Step 6:

[0786] The server calculates the optimal role and placement for the user based on the generated profile. The calculation results reflect the user's emotional state. This result is sent to the terminal.

[0787] Step 7:

[0788] The terminal displays deployment suggestions from the server to the user. The user reviews the suggestions and provides feedback on their satisfaction level and any further requests. The feedback is sent to the server.

[0789] Step 8:

[0790] The server receives user feedback and uses it to fine-tune the emotion engine and generative models. This information helps improve the accuracy of the models.

[0791] (Example 2)

[0792] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0793] Traditional job placement systems primarily based placement suggestions on superficial information such as skill sets, but they failed to consider the user's emotions and psychological state, limiting their ability to maximize performance. This resulted in reduced accuracy of placement suggestions and insufficient user satisfaction.

[0794] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0795] In this invention, the server includes means for activating a generation AI model based on basic information received from the user and generating a list of individual questions to understand the user's characteristics; means for analyzing the response data and emotional data received from the user with an emotion engine and generating a profile that identifies the user's characteristics and emotional state; and means for referring to the profile and using an artificial intelligence algorithm to calculate and propose the most suitable job placement for the user. This makes it possible to propose a more accurate job placement that takes the user's emotional state into account.

[0796] "User" refers to an individual or group that operates the system and inputs information.

[0797] A "generative AI model" refers to a computational model that uses artificial intelligence to generate specific tasks or outputs based on input data.

[0798] A "customized question list" refers to a set of questions customized to delve into the characteristics of a specific user.

[0799] "Response data" refers to text or audio information provided by users in response to questions.

[0800] An "emotion engine" refers to a computing device or software that analyzes a user's emotional state from voice and text data.

[0801] A "profile" refers to a collection of data created to represent a user's characteristics, strengths, and emotional state.

[0802] An "artificial intelligence algorithm" refers to a method that enables advanced inference and calculation through programs that run on computers and perform data analysis and prediction.

[0803] "Job assignment" refers to the process of determining the role and position of users within an organization.

[0804] "Feedback" refers to the opinions and reactions that users provide to the content suggested by the system.

[0805] An "information recording device" refers to equipment or a system for storing and managing digital data.

[0806] "Output device" refers to equipment or systems used to display calculation results or data to users.

[0807] This system implements a program designed to achieve optimal job placement for users through a multi-stage process. The system is operated by a server (an information processing device), a terminal (an interface device), and the user.

[0808] 1. The user logs into the system using a terminal. They then enter basic information such as personal information, work experience, and interests. This information is entered according to the format specified on the terminal and sent from the terminal to the server after completion.

[0809] 2. The server analyzes the received information using a generative AI model and generates a personalized list of questions. This list of questions is customized to gain a more specific understanding of the user's characteristics.

[0810] 3. When the user answers the list of questions displayed on the device, they enter their answers as audio or text data. The device then sends this data to the server.

[0811] 4. The server uses an emotion engine to analyze the received response data and identify the user's emotional state from their voice tone and text expression. This information is used to generate a trait profile, which includes the user's skills, traits, and emotional tendencies.

[0812] 5. Based on the generated profile, the server uses an artificial intelligence algorithm to calculate and propose the optimal job placement for the user.

[0813] As a concrete example, if User B answers "Very confident" to the question, "Can you handle a tense situation calmly?", the emotion engine analyzes the degree of confidence from the audio data of that answer. Based on this, it is determined that User B has high stress tolerance and is suitable for a position that can handle pressure. As a final suggestion, placement based on these characteristics is presented to the user.

[0814] Examples of prompt statements include the following:

[0815] "Based on your work experience, what skills do you feel have been particularly useful?"

[0816] "What do you consider most important when starting a new project?"

[0817] These factors enable the system to consider not only the user's characteristics but also their emotions, thereby achieving more personalized job assignments.

[0818] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0819] Step 1:

[0820] The user operates a terminal and logs into the system. After logging in, they enter basic information such as their name, work experience, and interests. Once this information is entered, the terminal checks the input data for consistency and integrity. If the information is correctly formatted, the terminal sends the data to the server. The input data is subjective information of the user, and the output data is a transmittable packet containing this information.

[0821] Step 2:

[0822] Upon receiving basic information from the terminal, the server activates a generative AI model. Using this model, the server generates a personalized list of questions tailored to each user. This process uses the user's basic information as input and outputs questions designed to delve deeper into their characteristics based on that information. The generated question list is then sent back to the terminal.

[0823] Step 3:

[0824] The user answers a list of individual questions displayed on the device. The device supports text and voice input and records the user's responses as digital data. The text and voice data serve as input data for analysis and as output data when transmitted from the device to the server.

[0825] Step 4:

[0826] The server processes the received response data and performs sentiment analysis using an emotion engine. This engine analyzes subtle nuances in voice tone and text as input and outputs data indicating the user's emotional state. The server then integrates this sentiment data as part of the user profile, generating a profile that includes traits, strengths, and emotional tendencies.

[0827] Step 5:

[0828] The server uses an artificial intelligence algorithm based on the generated profile to calculate the optimal job placement for the user. This calculation transforms the profile information as input and generates output in the form of optimal roles and placement suggestions for the user. The calculation results are sent to the terminal.

[0829] Step 6:

[0830] Users review the job placement suggestions displayed on their terminals and provide feedback on the system's suggestions. This feedback becomes crucial data for system readjustments and is sent to the server. This results in the provision of a final suggestion that reflects the user's opinions.

[0831] (Application Example 2)

[0832] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0833] In security services and various other operations, appropriate personnel allocation is required, but conventional methods have made it difficult to achieve optimal allocation considering the characteristics and emotional states of staff. This has limited the ability to improve operational efficiency and performance. This invention aims to solve this problem by precisely analyzing the characteristics and emotions of individuals and proposing the most suitable roles based on that analysis.

[0834] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0835] In this invention, the server includes means for analyzing voice and text data to analyze the user's emotional state and build an emotion engine; means for a generative model that generates a list of individual questions based on information received from the user; and means for generating a profile to identify the user's characteristics and strengths. This makes it possible to propose optimal role assignments that reflect an individual's characteristics and emotions.

[0836] A "generative model" is an algorithm that automatically generates a list of questions tailored to each user based on their input information.

[0837] An "emotion engine" is a technology that analyzes voice and text data to identify the emotional state of a user.

[0838] A "profile" is a dataset used to record a user's characteristics and strengths, and to manage individual attribute information, including their emotional state.

[0839] "Optimal placement" refers to a placement proposal that aims to distribute roles appropriately based on the characteristics and emotions of users.

[0840] "Feedback" refers to information collected from users, such as responses and opinions, which is used to improve generative models and proposed solutions.

[0841] The system for implementing this invention primarily consists of a server, a terminal, and user interaction. Using a terminal such as a smartphone or tablet, the user enters basic information and logs into the system. The server receives this information and uses a generative AI model to generate a list of individual questions to delve deeper into the user's characteristics.

[0842] The emotion engine analyzes speech-to-text data as the user answers presented questions to identify their emotional state. This process utilizes natural language processing technologies such as Google Cloud Speech-to-Text API and AWS Comprehend. The user's responses, along with the emotion data, are sent to the server, where a profile is generated that takes into account their characteristics and emotions.

[0843] Based on the user's profile, the server calculates the optimal role and placement for the user and returns suggestions to the user via the terminal. Individual emotional states are also taken into account, resulting in suggestions for placements that allow the user to perform optimally. Furthermore, user feedback can be collected to dynamically fine-tune the generative model.

[0844] For example, if a security staff member at a specific shopping mall uses this system, questions will be generated based on their experience and characteristics. Monitoring tasks requiring high concentration and composure will be recommended, and this role will be confirmed through sentiment analysis of voice data. Feedback on the final placement suggestions contributes to improving the system's accuracy.

[0845] Specifically, an example of a prompt statement for the generative model of this system is as follows:

[0846] "Please propose a user role assignment. Username: Mr. / Ms. A, Characteristics: Calm, Interests: Electronics, Emotional state: High concentration, Low stress level."

[0847] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0848] Step 1:

[0849] Users log in to the system using a terminal. After logging in, users enter basic information such as their name, work experience, and interests. This information is sent from the terminal to the server. The input data includes personal information in text format. The server receives this information and uses it as basic data to create an initial profile.

[0850] Step 2:

[0851] The server activates a generative AI model based on the user's basic information received. The list of questions generated here is designed to delve deeper into the user's characteristics. Analyzing the input basic information, the generative AI model creates a prompt statement containing appropriate questions. This prompt statement is output in the form of questions presented to the user.

[0852] Step 3:

[0853] Users answer a generated list of questions. Responses are entered via the device in either voice or text format. For voice data, the device uses the Google Cloud Speech-to-Text API to convert the audio to text, which is then sent to the server. The server analyzes the received responses and uses a sentiment engine to identify the emotional state. This sentiment analysis uses tools such as AWS Comprehend, and outputs metadata indicating the emotional state.

[0854] Step 4:

[0855] The server generates a profile that shows the user's characteristics and strengths based on the analyzed responses and their emotional states. In this profile generation, emotional states are integrated with the user's characteristic data. The profile is stored on the server and output in a format that can be used as foundational data for role suggestions within the organization.

[0856] Step 5:

[0857] The server calculates the optimal role and placement for the user based on the generated profile. This calculation takes into account characteristic data and emotional state. The optimized role suggestions are output to the user in the form of a display via the terminal.

[0858] Step 6:

[0859] The user provides feedback on the presented role suggestions. This feedback is sent to the server via the terminal. The server uses this feedback to fine-tune the generated AI model and improve the accuracy of future suggestions. The output of this feedback process includes improved tuning parameters for the model and data reflecting the user's further interests.

[0860] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0861] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0862] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0863] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0864] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0865] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0866] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0867] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0868] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0869] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0870] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0871] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0872] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0873] 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.

[0874] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0875] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0876] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0877] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0878] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0879] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0880] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0881] The following is further disclosed regarding the embodiments described above.

[0882] (Claim 1)

[0883] The system includes a generative model that generates a list of questions based on information received from the user, and means for providing the user with interactive questions to delve deeper into their characteristics.

[0884] A means for analyzing the responses received from the user and generating a profile to identify the user's characteristics and strengths,

[0885] A means for calculating the optimal roles and placements based on the aforementioned profile and proposing the optimal placement to the user,

[0886] A means for receiving feedback from the user and for fine-tuning the generative model,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, wherein the aforementioned profile is stored in a database and reflected in personnel allocation for the entire organization.

[0890] (Claim 3)

[0891] The system according to claim 1, further comprising a terminal that displays the optimal arrangement, wherein the terminal receives feedback from the user regarding satisfaction and areas for improvement.

[0892] "Example 1"

[0893] (Claim 1)

[0894] A means by which users can access the system via a terminal and input basic information,

[0895] A means of sending the input information to a server, activating a generating AI model to generate a user-specific list of questions,

[0896] A means of transferring a list of questions from a server to a terminal and providing interactive questions to the user,

[0897] A means for analyzing responses received from users and generating a profile that identifies the user's characteristics and strengths,

[0898] A means of calculating the optimal roles and placements based on the generated profile and proposing the optimal placement to the user,

[0899] A means of receiving feedback from users and fine-tuning the generative AI models used on the server,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, which stores the generated profile in a storage device and reflects it in the industry layout of the entire organization.

[0903] (Claim 3)

[0904] The system according to claim 1, further comprising a terminal capable of visually displaying the optimal arrangement, the terminal receiving feedback from users regarding satisfaction and areas for improvement.

[0905] "Application Example 1"

[0906] (Claim 1)

[0907] The system includes a generative model that generates a list of questions based on information received from the user, and means for providing the user with interactive questions to delve deeper into their characteristics.

[0908] A means for analyzing the responses received from the user and generating a profile to identify the user's characteristics and strengths,

[0909] A means for calculating the optimal roles and placements based on the aforementioned profile and proposing the optimal placement to the user,

[0910] A means for receiving feedback from the user and for fine-tuning the generative model,

[0911] A means of analyzing the characteristics of robots and proposing the optimal work arrangement within a factory,

[0912] A system that includes this.

[0913] (Claim 2)

[0914] The system according to claim 1, wherein the aforementioned profile is stored in a database and reflected in the personnel allocation of the entire organization.

[0915] (Claim 3)

[0916] The system according to claim 1, further comprising a device for displaying the optimal arrangement, wherein the device receives feedback from the user regarding satisfaction and areas for improvement.

[0917] "Example 2 of combining an emotion engine"

[0918] (Claim 1)

[0919] A means to activate an AI model based on basic information received from the user and generate a list of individual questions to understand their characteristics,

[0920] A means for analyzing response data and emotional data received from users using an emotion engine to generate profiles that identify characteristics and emotional states,

[0921] A means for calculating and proposing the optimal job placement for the user using an artificial intelligence algorithm, based on the aforementioned profile,

[0922] A means of adjusting proposals based on user feedback and determining the final placement,

[0923] A system that includes this.

[0924] (Claim 2)

[0925] The system according to claim 1, wherein the aforementioned profile is stored in an information recording device and used for workplace placement throughout the organization.

[0926] (Claim 3)

[0927] The system according to claim 1, further comprising an output device that displays the optimal arrangement and accepts feedback from users.

[0928] "Application example 2 of combining emotional engines"

[0929] (Claim 1)

[0930] The system includes a generative model that generates a list of questions based on information received from the user, and means for providing the user with interactive questions to delve deeper into their characteristics.

[0931] In order to analyze the emotional state of the aforementioned user, a means for analyzing voice data and text data and constructing an emotion engine,

[0932] A means for analyzing the responses and emotional states received from the user and generating a profile to identify the user's characteristics and strengths,

[0933] A means for calculating the optimal roles and placements based on the aforementioned profile and proposing the optimal placement to the user,

[0934] A means for receiving feedback from the user and for fine-tuning the generative model,

[0935] A system that includes this.

[0936] (Claim 2)

[0937] The system according to claim 1, wherein the aforementioned profile is stored in a database and used to optimize the efficiency of operations such as security services by reflecting it in the role assignments of the entire organization.

[0938] (Claim 3)

[0939] The system according to claim 1, further comprising a terminal that displays the optimal placement, wherein the terminal receives feedback from the user regarding satisfaction and areas for improvement, and dynamically updates the profile based on this feedback. [Explanation of Symbols]

[0940] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. The system includes a generative model that generates a list of questions based on information received from the user, and means for providing the user with interactive questions to delve deeper into their characteristics. A means for analyzing the responses received from the user and generating a profile to identify the user's characteristics and strengths, A means for calculating the optimal roles and placements based on the aforementioned profile and proposing the optimal placement to the user, A means for receiving feedback from the user and for fine-tuning the generative model, A system that includes this.

2. The system according to claim 1, wherein the aforementioned profile is stored in a database and reflected in personnel allocation for the entire organization.

3. The system according to claim 1, further comprising a terminal that displays the optimal arrangement, wherein the terminal receives feedback from the user regarding satisfaction and areas for improvement.

Images