system

Abstract

We provide the system. [Solution] This invention relates to a system that acquires diverse information such as employees' skills, experience, abilities, achievements, and aspirations from terminals, centrally manages it on a server along with requirements information requested by each department within the organization, and uses generative AI to comprehensively and multifacetedly analyze and compare employee information and departmental information to score the optimal transfer destination department for each employee, and automatically generates and presents recommendation reasons and career path advice. This objectively evaluates employees' abilities and aptitudes, simultaneously achieving optimal personnel placement and career development support, and contributes to the revitalization of the entire organization and improvement of employee retention rates.

Description

TECHNICAL FIELD

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

BACKGROUND ART

[0002] Patent Document 1 discloses a persona chatbot control method executed by at least one processor, the method comprising the steps of: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence associated with a description relating to a character of the chatbot; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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] The problems that this invention aims to solve are: the difficulty for employees within an organization to objectively and accurately determine in which department they can best utilize their skills, experience, abilities, and past work performance; the high likelihood that employees will miss opportunities for career advancement and self-realization because they cannot select an appropriate department when requesting a transfer; the lack of a system within the organization to objectively evaluate employees' abilities and aptitudes and achieve optimal personnel placement, or the reliance on subjective judgments and personal evaluations, leading to personnel mismatches, decreased motivation, increased turnover rates, and a lack of overall organizational vitality; the inability to quickly and efficiently provide specific and individually optimized advice and transfer destination suggestions to support each employee's career plan; and the need to realize a system that can comprehensively collect and analyze diverse employee information and propose the optimal department from an objective perspective, using generative AI, in order to solve these problems. [Means for solving the problem]

[0005] The means for solving the problems of the present invention is to construct a system that supports employees' transfer requests, and the system comprises an employee information acquisition unit, a department information management unit, and an aptitude analysis and recommendation unit. The employee information acquisition unit has the function of inputting or acquiring information such as skills, experience, abilities, past work performance, and desired department for each employee. The department information management unit has the function of managing information such as the skills, experience, abilities, and work content required for each department within the organization. The aptitude analysis and recommendation unit uses a generative AI to compare employee information acquired by the employee information acquisition unit with information for each department managed by the department information management unit, and after analyzing the employee's skills, experience, abilities, performance, and preferences from multiple perspectives and objectively, scores the most suitable department for each employee and executes a process to recommend the department with the highest degree of suitability. This configuration makes it possible to objectively evaluate the abilities and aptitudes of employees, realize optimal personnel placement, support employee career development, and revitalize the entire organization. [Brief explanation of the drawing]

[0006] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This 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] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This 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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0007] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0008] First, let's explain the terminology used in the following explanation.

[0009] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0010] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0011] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0012] 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).

[0013] 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 only A, only B, or a combination of A and B. In addition, in the present specification, when three or more items are expressed in a connected manner using "and / or", the same concept as that for "A and / or B" is applied.

[0014] [First Embodiment]

[0015] FIG. 1 illustrates an example of the configuration of a data processing system 10 according to the first embodiment.

[0016] As shown in FIG. 1, the data processing system 10 includes a data processing apparatus 12 and a smart device 14. An example of the data processing apparatus 12 is a server.

[0017] The data processing apparatus 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of the "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network), and the like.

[0018] The smart device 14 includes a computer 36, a reception apparatus 38, an output apparatus 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception apparatus 38, output apparatus 40, camera 42, and communication I / F 44 are also connected to the bus 52.

[0019] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A receives user input via contact of an indicator (for example, a pen, a finger, or the like) by detecting the contact of the indicator. The microphone 38B receives user input via voice by detecting the user's voice. The control unit 46A in the processor 46 transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0020] The output device 40 includes a display 40A, a speaker 40B, and the like, and presents data to the user 20 by outputting the data in a representation form (for example, voice and / or text) that the user 20 can perceive. The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs voice in accordance with instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0021] The communication I / F 44 is connected to the network 54. The communication I / Fs 44 and 26 manage the exchange of various types of information between the processor 46 and the processor 28 via the network 54.

[0022] FIG. 2 illustrates an example of the main functions of the data processing device 12 and the smart device 14.

[0023] 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.

[0024] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0025] 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 the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models. 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.

[0026] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0027] (Example 1)

[0028] The flow of a specific process in Example 1 will now be described. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0029] (Embodiments for carrying out the present invention)

[0030] Embodiments of the present invention will be described in more detail. The system of the present invention consists of a server and a terminal, and has three main functional units: an employee information acquisition unit, a departmental information management unit, and an aptitude analysis and recommendation unit. The configuration, functions, processing flow, methods of utilizing the generative AI, the division of roles between the server and terminal, and specific usage examples of each unit will be described in great detail below.

[0031] First, the employee information acquisition function is implemented on the terminal side. Various information terminals such as PCs, tablets, and smartphones can be used, and employees input their own information from these terminals. The input fields are diverse, and are designed to allow for a multifaceted understanding of employees' abilities, aptitudes, and aspirations, such as "qualifications held," "language skills," "years of work experience," "name of assigned project," "role in project," "leadership experience," "number of people managed," "sales performance," "awards," "external activities," "self-introduction," "career vision," "desired work style (remote, in-office, flexible, etc.)," ​​"desired work location," "reason for transfer request," "past transfer experience," "history of internal and external training," "specialized field," "areas of interest," "stress tolerance," "communication style," "values," and "future goals." For example, an employee can input detailed information from their terminal such as "holds qualifications related to AI technology, has 2 years of experience as an AI project leader, is fluent in English and Chinese at a business level, was a top salesperson in the sales department for 3 years, has received 2 internal awards, and desires a management position at an overseas branch in the future." Furthermore, the employee information acquisition unit can integrate with the company's internal personnel evaluation system and business management system to automatically acquire past evaluation reports, goal management sheets, 360-degree evaluations, project management data, attendance data, training attendance history, and more. For example, information that employees have forgotten to enter can be automatically completed by the system.

[0032] Next, the Department Information Management Department is implemented on the server side. The Department Information Management Department centrally manages extremely diverse and detailed information for all departments within the organization, including required skill sets, minimum years of experience, desired abilities, job descriptions, department missions, short-term and medium-to-long-term goals, team structure, leadership requirements, expertise requirements, communication style, department culture, trends of past transferees, department growth strategies, career paths within departments, personnel supply and demand forecasts for each department, department workload, department turnover rates, department employee satisfaction, department evaluation criteria, department major customers and business partners, department language used, department work style (remote work possible, etc.), and department work location. For example, the Development Department might require "AI technology, cloud infrastructure, project management, logical thinking skills, English proficiency, leadership, and agile development experience," while the Sales Department might register "corporate sales experience, proposal skills, negotiation skills, communication skills, experience using customer management systems, presentation skills, and motivation to achieve goals." The Human Resources Department manages information such as "recruitment experience, evaluation system design, training program planning, empathy, labor management knowledge, and diversity promotion experience." This departmental information can be entered and updated by administrators via terminals, but it can also be automatically retrieved and updated from the organization's HR database, past transfer data, departmental performance data, and employee survey results. For example, if a department's workload increases or a new project is launched, the departmental information management department can automatically update the personnel requirements and reflect them in the aptitude analysis and recommendation department.

[0033] The aptitude analysis and recommendation system is implemented on the server side, where generative AI plays a central role. Generative AI combines technologies such as natural language processing, semantic analysis, keyword extraction, contextual understanding, summary generation, similarity calculation, clustering, machine learning, and deep learning to achieve a high level of matching between employee information and departmental information. For example, from an employee's self-introduction and career vision, the generative AI extracts characteristics such as "leadership," "challenging spirit," "global orientation," "interest in new business development," and "emphasis on teamwork," and scores their affinity with the ideal candidate profile required by each department. Furthermore, the generative AI analyzes unstructured text data such as an employee's past work performance, evaluation comments, project participation history, awards, and performance data after transfers, and automatically extracts and quantifies achievements such as "served as a leader in launching a new business with a 120% target achievement rate," "experience in establishing overseas branches," "received three company awards," and "highly rated for project management." In addition, the generative AI analyzes the results of employee aptitude tests and questionnaires to identify characteristics such as "high logical thinking ability," "strong stress tolerance," "high cooperativeness," "originality," and "strong leadership orientation."

[0034] The generative AI compares this diverse employee information with detailed departmental requirements managed by the Departmental Information Management Department. It comprehensively evaluates multiple factors, such as the number of matching skills, experience, abilities, achievements, whether the department is a preference, aptitude test results, past transfer trends, talent supply and demand situation for each department, departmental growth strategies, and the overall talent balance of the organization, using weighted averages and machine learning models to calculate a suitability score for each department for each employee. For example, the AI ​​might determine that "Employee A has the highest suitability score for the Development Department because they meet all the requirements for AI technology, project management, English proficiency, and leadership, and have also achieved high results in past AI projects." Similarly, for Employee B, a multifaceted evaluation is possible, such as "Employee B has the proposal skills, negotiation skills, communication skills, and corporate sales experience required for the Sales Department, and has a track record of being a top sales performer, making them highly suitable for the Sales Department. However, they also wish to work for the Human Resources Department and have experience in education and training planning, so they are also a candidate for recommendation from the Human Resources Department."

[0035] Furthermore, the generative AI selects several top-performing departments as recommended departments based on the suitability score calculated for each employee, and automatically generates recommendation reasons and career plan advice using natural language generation capabilities. For example, it can provide individually optimized recommendation reasons for each employee, such as: "Your AI skills and leadership are particularly valuable in the development department. Your achievements in past AI projects and your English skills will also be valued, and you can expect to participate in global projects," "In the sales department, your proposal and negotiation skills will be immediately valuable. Your top sales performance will directly lead to acquiring new customers and securing large deals," or "In the human resources department, your experience in education and training planning and your empathy will be highly valued. You can expect to have a career as a talent development leader in the future." It can also generate specific career path advice, such as: "If you aim for a management position at an overseas branch in the future, experience in global projects in the development department will be advantageous for your career development," or "If you have a strong interest in new business development, experience in the planning department will lead to your growth as a future executive candidate."

[0036] In addition, generative AI can learn from employees' past transfer histories and performance data after transfers, continuously improving its recommendation accuracy. For example, it can analyze the performance data of employees who previously transferred from the sales department to the development department, extract the characteristics of those who achieved high results after the transfer, and reflect them in future recommendation algorithms. Furthermore, generative AI can also consider the overall talent allocation balance of the organization and forecasts of future talent demand, enabling recommendations based on organizational strategy, such as "Since the demand for AI engineers in the development department will increase in the future, we will prioritize recommending employees with AI-related skills."

[0037] Furthermore, employees and managers can not only view recommendation results and advice from their devices, but also receive detailed feedback on the reasons for the recommendation, why that department is a good fit, which skills and experience were valued, and what skills they should develop to further improve their fit for their desired department. For example, specific growth advice is generated, such as, "To further improve your fit for the development department, it is recommended that you acquire knowledge of cloud infrastructure," or "If you aim for a leadership position in the HR department, it would be effective to gain experience in designing performance evaluation systems."

[0038] Thus, the system of the present invention is configured such that the employee information acquisition unit is implemented on the terminal side, and the department information management unit and aptitude analysis / recommendation unit are implemented on the server side. This allows employees to easily input information from their terminals, and the server side can perform advanced analysis and recommendation processing using a high-performance generative AI. For example, it is possible for employees to input their own information from their smartphones or PCs, for the generative AI on the server side to perform analysis and recommendations, and then feed the results back to the terminal. Furthermore, administrators can centrally manage department information on the server side, allowing for flexible responses to organizational changes and the establishment of new departments. For example, if a new project department is established, the administrator can register the necessary skills and job content on the server, and the aptitude analysis / recommendation unit can immediately extract suitable candidates for the new department.

[0039] Furthermore, generative AI can learn from employees' past transfer histories and performance data after transfers, continuously improving its recommendation accuracy. For example, it can analyze the performance data of employees who previously transferred from the sales department to the development department, extract the characteristics of those who achieved high results after the transfer, and reflect them in future recommendation algorithms. In addition, generative AI can also consider the overall talent allocation balance of the organization and forecasts of future talent demand, enabling recommendations based on organizational strategy, such as "Since the demand for AI engineers in the development department will increase in the future, we will prioritize recommending employees with AI-related skills."

[0040] Thus, according to the embodiments of the present invention, by utilizing generative AI, diverse information about employees can be comprehensively and objectively analyzed, and individually optimized recommendations for transfer destinations and career support information can be automatically generated and provided, thereby simultaneously achieving personnel placement that maximizes the capabilities of employees and revitalizing the organization.

[0041] (System Configuration)

[0042] The system according to this embodiment comprises an employee information acquisition unit, a department information management unit, an aptitude analysis and recommendation unit, a generation AI integration unit, a results presentation unit, and an administrator operation unit. The employee information acquisition unit allows employees to input a wide range of information from a terminal, such as their skills, experience, abilities, past work performance, qualifications, language skills, assigned projects, leadership experience, number of people managed, sales performance, awards, external activities, self-introduction, career vision, desired work style, desired work location, reasons for wanting to transfer, past transfer experience, training history both inside and outside the company, specialized fields, areas of interest, stress tolerance, communication style, values, and future goals. For example, an employee can input detailed information such as "I hold qualifications related to AI technology, I have 2 years of experience as a leader of an AI project, I am fluent in English and Chinese at a business level, I was the top salesperson in the sales department for 3 years, I have received 2 company awards, and I hope to take on a management position at an overseas branch in the future." Furthermore, the employee information acquisition unit can integrate with the company's internal personnel evaluation system and business management system to automatically acquire past evaluation reports, goal management sheets, 360-degree evaluations, project management data, attendance data, training attendance history, and more. For example, it can automatically complete information that employees have forgotten to enter.

[0043] The Departmental Information Management Department centrally manages extremely diverse and detailed information for all departments within the organization, including required skill sets, minimum years of experience, desired abilities, job descriptions, departmental missions, short-term and medium-to-long-term goals, team structure, leadership requirements, expertise requirements, communication styles, departmental culture, trends among past transferees, departmental growth strategies, internal career paths, departmental talent supply and demand forecasts, departmental workload, departmental turnover rates, departmental employee satisfaction, departmental evaluation criteria, departmental major customers and business partners, departmental language use, departmental work arrangements, and departmental work locations. For example, the Development Department requires "AI technology, cloud infrastructure, project management, logical thinking skills, English proficiency, leadership, and agile development experience," the Sales Department registers "corporate sales experience, proposal skills, negotiation skills, communication skills, experience using customer management systems, presentation skills, and motivation to achieve goals," and the Human Resources Department manages "recruitment experience, evaluation system design, training planning, empathy, labor management knowledge, and diversity promotion experience." This departmental information can be entered and updated by administrators via their terminals, but it can also be automatically retrieved and updated from the organization's personnel database, past transfer data, departmental performance data, and employee survey results. For example, if a department's workload increases or a new project is launched, the departmental information management department can automatically update the personnel requirements and reflect them in the aptitude analysis and recommendation department.

[0044] The Aptitude Analysis and Recommendation Department compares employee information obtained from the Employee Information Acquisition Department with information from each department managed by the Department Information Management Department. Through the Generative AI Integration Department, it utilizes generative AI to comprehensively and multifaceted analyze employees' skills, experience, abilities, achievements, aspirations, self-assessments, and aptitude test results. For example, the generative AI extracts keywords and performs semantic analysis from employees' free-form self-introduction statements and career visions, automatically extracting their strengths and weaknesses, values, orientations, leadership tendencies, stress tolerance, and cooperativeness. Furthermore, the generative AI analyzes text data such as employees' past work performance, evaluation comments, project participation history, and internal and external awards to quantify and qualitatively assess their achievements and results. For instance, it automatically extracts and scores achievements such as "Served as a leader in launching a new project with a 120% target achievement rate," "Experience in establishing overseas branches," "Received three internal awards," and "Highly rated for project management." In addition, the generative AI analyzes employee aptitude tests and survey results to identify characteristics such as "high logical thinking ability," "strong stress tolerance," "high cooperativeness," "originality," and "strong leadership orientation." The Aptitude Analysis and Recommendation Department compares this diverse employee information with detailed departmental requirements managed by the Departmental Information Management Department. It comprehensively evaluates multiple factors, including the number of matching skills, experience, abilities, achievements, whether the department is a desired department, aptitude test results, past transfer trends, talent supply and demand situation for each department, departmental growth strategies, and the overall talent balance of the organization, using weighted averages and machine learning models to calculate a suitability score for each department for each employee. For example, the AI ​​can determine that "Employee A meets all the AI ​​technology, project management, English language skills, and leadership requirements for the Development Department, and has also achieved high results in past AI projects, therefore has the highest suitability score for the Development Department."

[0045] The Generative AI Collaboration Department is responsible for instructing the Generative AI to perform processes such as natural language processing, semantic analysis, summary generation, similarity calculation, clustering, machine learning, and deep learning, which are required by the Aptitude Analysis and Recommendation Department, using prompt statements, and receiving responses from the Generative AI. For example, it sends a prompt statement to the Generative AI such as, "Based on the following employee and department information, extract the employee's strengths and weaknesses, score which department they are best suited for, and explain the reason in natural language." A concrete example of a prompt statement would be, "Employee information: Skills = AI technology, English, project management; Achievements = AI project leader for 2 years, top salesperson in sales department for 3 years, 2 awards received; Desired = Overseas branch management. Department information: Development department = AI technology, English, project management, global projects; Sales department = sales experience, proposal skills, negotiation skills; Human resources department = education and training, empathy. Based on the above, score which department the employee is best suited for and explain the reason." Furthermore, the generative AI integration unit can also generate prompts that summarize free-form texts such as employees' self-introductions and career visions, and perform keyword extraction and semantic analysis. For example, a prompt such as, "Extract the employee's strengths, weaknesses, values, and aspirations from the following free-form text and output them in bullet points. Free-form text: I have a strong interest in developing new businesses utilizing AI technology, and I have previously served as the leader of an AI project. I actively participate in global projects and am proficient in communication in English and Chinese," might be used.

[0046] The Results Presentation Department presents analysis results obtained from the Aptitude Analysis & Recommendation Department and the Generation AI Integration Department, along with recommended departments, reasons for recommendation, career path advice, suitability scores, skills and experience to be developed, suitability comparisons by department, detailed reasons for recommendation, and future growth advice, in an easy-to-understand manner to employees' and managers' terminals. For example, it can present individually optimized reasons for recommendation for each employee, such as: "Your AI skills and leadership are particularly valuable in the Development Department. Your achievements in past AI projects and English proficiency will also be valued, and you can expect to participate in global projects," "In the Sales Department, your proposal and negotiation skills will be immediately valuable. Your top sales performance will directly lead to acquiring new customers and securing large deals," or "In the Human Resources Department, your experience in education and training planning and your empathy will be highly valued. You can expect a career as a talent development leader in the future." It can also generate specific growth advice, such as: "To further improve your suitability for the Development Department, it is recommended that you acquire knowledge of cloud infrastructure," or "If you aim for a leadership position in the Human Resources Department, gaining experience in designing evaluation systems will be effective."

[0047] The Administrator Operations Unit is where administrators can perform a wide range of management tasks, including inputting and updating department information, viewing and managing employee information, adjusting parameters for recommendation algorithms, extracting lists of transfer candidates, registering performance data after transfers, checking the overall personnel allocation balance of the organization, setting future personnel supply and demand forecasts, analyzing transfer results, monitoring system operation status, security management, and setting access permissions. For example, when an administrator establishes a new project department, they can register the necessary skills and job content, allowing the aptitude analysis and recommendation unit to immediately extract suitable candidates for the new department. Furthermore, by registering the performance data of employees after transfers and using it as training data for generative AI, it is possible to continuously improve the accuracy of recommendations.

[0048] As described above, the system according to this embodiment includes an employee information acquisition unit, a department information management unit, an aptitude analysis and recommendation unit, a generation AI collaboration unit, a results presentation unit, and an administrator operation unit. Each unit works in cooperation to comprehensively and objectively analyze diverse employee information and automatically generate and provide individually optimized recommendations for transfer destination departments and career support information. Specific examples of prompts to be fed into the generation AI include: "Based on employee information and department information, extract the employee's strengths and weaknesses, score which department they are best suited for, and explain the reason in natural language," "From the following free-text descriptions, extract the employee's strengths, weaknesses, values, and aspirations, and output them as a bulleted list," and "Compare the employee's past work performance with department requirements, calculate a suitability score, and recommend the most suitable department."

[0049] (Implementation Steps)

[0050] Step 1: Entering and obtaining employee information Employees input a wide range of information from their terminals, including their skills, experience, abilities, past work performance, qualifications, language skills, assigned projects, leadership experience, number of people managed, sales performance, awards, external activities, self-introduction, career vision, desired work style, desired work location, reasons for transfer requests, past transfer experience, training history both inside and outside the company, area of ​​expertise, areas of interest, stress tolerance, communication style, values, and future goals. Furthermore, the employee information acquisition department integrates with the company's personnel evaluation system and work management system to automatically acquire past evaluation reports, goal management sheets, 360-degree evaluations, project management data, attendance data, training history, and more, and automatically fills in any information that may have been forgotten to be entered.

[0051] Step 2: Register and manage department information Administrators can register and manage a variety of information for each department within the organization from their terminals, including required skill sets, minimum years of experience, desired abilities, job responsibilities, departmental mission, short-term and medium-to-long-term goals, team structure, leadership requirements, expertise requirements, communication style, departmental culture, past employee turnover trends, departmental growth strategies, career paths within the department, personnel supply and demand forecasts for each department, departmental workload, departmental turnover rate, departmental employee satisfaction, departmental evaluation criteria, departmental major customers and business partners, departmental language used, departmental work style, and departmental work location. Departmental information can also be automatically retrieved and updated from the HR database, past employee turnover data, departmental performance data, and employee survey results.

[0052] Step 3: Analysis of employee information using generated AI The Aptitude Analysis and Recommendation Department inputs unstructured data obtained from the Employee Information Acquisition Department, such as free-form employee descriptions, self-introductions, career visions, past work performance, evaluation comments, project participation history, awards, aptitude tests, and survey results, into a generating AI. The AI ​​then performs natural language processing, semantic analysis, keyword extraction, contextual understanding, and summary generation. An example of a prompt sentence to be fed into the generating AI is: "Extract the strengths, weaknesses, values, and aspirations from the following employee's self-introduction and career vision, and output them in bullet points. Self-introduction: I have a strong interest in new business development utilizing AI technology, and I have previously served as the leader of an AI project. I actively participate in global projects and am proficient in communication in English and Chinese."

[0053] Step 4: Matching department information with generated AI and calculating a suitability score. The generating AI compares analyzed employee information with detailed departmental requirements managed by the Departmental Information Management Department. It comprehensively evaluates multiple factors such as the number of skill matches, experience matches, ability matches, achievements, whether the department is a preference, aptitude test results, past transfer trends, talent supply and demand situation for each department, departmental growth strategy, and overall talent balance of the organization using weighted averages and machine learning models to calculate a suitability score for each department for each employee. A concrete example of a prompt message to be fed into the generating AI is: "Employee information: Skills = AI technology, English, project management; Achievements = AI project leader for 2 years, top salesperson in sales department for 3 years, 2 awards; Preferences = Overseas branch management. Department information: Development department = AI technology, English, project management, global projects; Sales department = sales experience, proposal skills, negotiation skills; Human resources department = education and training, empathy. Based on the above, score which department the employee is best suited for and explain the reason."

[0054] Step 5: Generation of recommended departments and career advice using AI. The generating AI selects the top-ranking departments as recommended departments based on the calculated suitability score, and automatically generates advice on the reasons for the recommendation and career plans using natural language generation capabilities. Specific examples of prompts to be fed into the generating AI include: "Based on employee and department information, extract the employee's strengths and weaknesses, score which department they are best suited for, and explain the reasons and provide advice on future career development in natural language."

[0055] Step 6: Presentation of results and feedback The results presentation unit clearly displays analysis results obtained from the aptitude analysis and recommendation unit and the generation AI integration unit, as well as recommended departments, reasons for recommendation, career path advice, suitability scores, skills and experience to be developed in the future, suitability comparisons by department, detailed reasons for recommendation, and future growth advice to employees and managers on their terminals. For example, it presents individually optimized reasons for recommendation and growth advice for each employee, such as: "Your AI skills and leadership are particularly valuable in the development unit. Your achievements in past AI projects and English language skills will also be valued, and you can expect to participate in global projects," "In the sales unit, your proposal and negotiation skills will be immediately valuable. Your top sales performance will directly lead to acquiring new customers and securing large deals," and "In the human resources unit, your experience in education and training planning and your empathy will be highly valued. You can expect to have a career as a talent development leader in the future."

[0056] (Specific use cases)

[0057] For example, let's consider a case where the system of the present invention is used when an employee is considering a transfer request. In the employee information acquisition department, the employee inputs "AI technology," "programming," "English," and "project management" as their skills from a terminal, inputs "2 years as an AI project leader," "3 years in corporate sales," and "experience in establishing overseas branches" as experience, inputs "logical thinking ability," "leadership," and "communication ability" as abilities, inputs "120% goal achievement rate in an AI project," "top sales in the sales department," and "2 company awards" as past work achievements, and in the self-introduction section they write "I have a strong interest in developing new businesses utilizing AI technology, I actively participate in global projects, and I am proficient in communication in English and Chinese," and in the career vision section they write "I hope to take on a management position at an overseas branch in the future." The employee information acquisition department is linked with the company's personnel evaluation system and automatically acquires past evaluation reports, 360-degree evaluations, project management data, training attendance history, etc., to comprehensively collect employee information.

[0058] The Departmental Information Management Department registers required skills for the Development Department, such as "AI technology," "cloud infrastructure," "project management," "English proficiency," "leadership," and "agile development experience." For the Sales Department, it registers "corporate sales experience," "proposal skills," "negotiation skills," "communication skills," "experience using customer management systems," and "motivation to achieve goals." For the Human Resources Department, it registers "recruitment experience," "performance evaluation system design," "training and development planning," "empathy," "labor management knowledge," and "diversity promotion experience." Furthermore, detailed information such as the work content, mission, team structure, unique culture, past employee transfer trends, growth strategy, career paths within the department, talent supply and demand forecasts for each department, workload, turnover rate, employee satisfaction, evaluation criteria, major customers and business partners, language used, work style, and work location are also managed.

[0059] The Aptitude Analysis and Recommendation Department inputs unstructured data obtained from the Employee Information Acquisition Department, such as employee self-introductions, career visions, past work performance, evaluation comments, project participation history, awards, aptitude tests, and survey results, into a generating AI. The AI ​​then performs natural language processing, semantic analysis, keyword extraction, contextual understanding, and summary generation. A concrete example of a prompt sentence to be fed into the generating AI is: "Extract the strengths, weaknesses, values, and aspirations from the following employee self-introduction and career vision, and output them in bullet points. Self-introduction: I have a strong interest in new business development utilizing AI technology, and have previously served as the leader of an AI project. I actively participate in global projects and am proficient in communication in English and Chinese. Career vision: I hope to hold a management position at an overseas branch in the future."

[0060] The generating AI compares analyzed employee information with detailed departmental requirements managed by the Departmental Information Management Department. It comprehensively evaluates multiple factors such as the number of skill matches, experience matches, ability matches, achievements, whether the department is a preference, aptitude test results, past transfer trends, talent supply and demand situation for each department, departmental growth strategy, and overall talent balance of the organization using weighted averages and machine learning models to calculate a suitability score for each department for each employee. A concrete example of a prompt message to be fed into the generating AI is: "Employee information: Skills = AI technology, English, project management; Achievements = AI project leader for 2 years, top salesperson in sales department for 3 years, 2 awards; Preferences = Overseas branch management. Department information: Development department = AI technology, English, project management, global projects; Sales department = sales experience, proposal skills, negotiation skills; Human resources department = education and training, empathy. Based on the above, score which department the employee is best suited for and explain the reason."

[0061] The generating AI selects the top-ranking departments as recommended departments based on the calculated suitability score, and automatically generates advice on the reasons for the recommendation and career plans using natural language generation capabilities. Specific examples of prompts to be fed into the generating AI include: "Based on employee and department information, extract the employee's strengths and weaknesses, score which department they are best suited for, and explain the reasons and provide advice on future career development in natural language."

[0062] The results presentation unit clearly displays analysis results obtained from the aptitude analysis and recommendation unit and the generation AI integration unit, as well as recommended departments, reasons for recommendation, career path advice, suitability scores, skills and experience to be developed in the future, suitability comparisons by department, detailed reasons for recommendation, and future growth advice to employees and managers on their terminals. For example, it presents individually optimized reasons for recommendation and growth advice for each employee, such as: "Your AI skills and leadership are particularly valuable in the development unit. Your achievements in past AI projects and English language skills will also be valued, and you can expect to participate in global projects," "In the sales unit, your proposal and negotiation skills will be immediately valuable. Your top sales performance will directly lead to acquiring new customers and securing large deals," and "In the human resources unit, your experience in education and training planning and your empathy will be highly valued. You can expect to have a career as a talent development leader in the future."

[0063] Thus, the system of the present invention can comprehensively and objectively analyze diverse employee information, automatically generate and provide individually optimized recommendations for transfer destinations and career support information, and simultaneously achieve talent allocation that maximizes employee capabilities and revitalizes the organization.

[0064] (Application Example 1) The flow of a specific process in Application Example 1 will be explained below. Each part of the system described below is implemented by a data processing device 12 and a smart device 14. The data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0065] (Embodiments for carrying out the present invention)

[0066] Embodiments of the present invention will be described in more specific and detail. The system of the present invention is composed of a staff information acquisition unit, a work information management unit, and an aptitude analysis and recommendation unit as its core components in order to optimize the assignment of tasks to staff in nursing care settings. The staff information acquisition unit has the function of collecting and managing extremely diverse and detailed information for each staff member. For example, basic staff information such as name, age, gender, contact information, employment status (full-time, part-time, temporary, etc.), affiliated facility, and affiliated department can be registered. Furthermore, skill information such as "physical strength," "hygiene management," "observational skills," "empathy," "exercise instruction," "emergency response," "record management," "recreation planning," "dementia care," "vital sign measurement," "medication management," "meal assistance," "excretion assistance," "bathing assistance," "transfer assistance," and "rehabilitation assistance" can be subdivided and registered. Experience information such as "years of experience in bathing assistance," "years of experience in meal assistance," "years of experience in night shifts," "years of experience in rehabilitation assistance," "dementia care experience," "end-of-life care experience," "emergency response experience," "accident / near-miss response experience," "new employee training experience," and "multidisciplinary collaboration experience" can be entered. Multiple qualifications can be registered, such as "Certified Care Worker," "Care Worker Introductory Training," "Care Worker Practical Training," "Physical Therapist," "Occupational Therapist," "Nurse," "Dementia Care Specialist," "Assistive Devices Specialist," and "Care Manager." Physical characteristics include recording "Physical Fitness Level (High, Medium, Low)," "History of Lower Back Pain," "Presence or Absence of Chronic Illnesses," "Height and Weight," "Grip Strength," "Vision and Hearing," and "Allergy Information." Communication skills are evaluated and registered from multiple perspectives, including "Empathy," "Encouragement," "Calmness," "Politeness," "Explanatory Skills," "Active Listening," "Humor," and "Multilingual Support." Past work experience is recorded in detail for each task, such as "Bathing Assistance," "Meal Assistance," "Rehabilitation Support," "Night Shift," "Recreation Management," "Record Keeping," "Family Support," "Emergency Response," and "End-of-Life Care," including the number of times and duration of assignments, feedback from users, whether accidents occurred, and performance evaluation scores. Stress tolerance is managed based on self-reports such as "High," "Average," and "Low," as well as stress check results, past resignation / leave of absence history, and mental health training participation history.For desired shifts, staff can specify details such as "day shift," "night shift," "early shift," "late shift," "weekend shift," "holiday shift," "consecutive shift availability," and "short-time work preference." For desired work content, multiple selections are possible, such as "bathing assistance," "night shift," "rehabilitation support," "record keeping," and "recreational activity management." For available work areas, staff can select multiple facilities such as "Facility A," "Facility B," and "Facility C," and can also register their commute time, mode of transportation, and whether they require transportation. Furthermore, staff can input and retrieve their career vision, self-introduction, future goals, training history, awards, recommendation comments from users and colleagues, evaluations on social media and internal communication tools, health checkup results, and vaccination history. This information can be entered by staff themselves via terminals, or automatically retrieved and updated by linking with HR systems, work management systems, health management systems, evaluation systems, shift management systems, and external qualification authentication systems.

[0067] The Business Information Management Department centrally manages extremely diverse and detailed information about all tasks in the caregiving field, including required skills, experience, qualifications, physical characteristics, communication characteristics, task content, difficulty level, time of day, urgency level, user characteristics, location, frequency, duration, number of people assigned to the task, risk level, availability of manuals, performance evaluation criteria, history of past accidents and near misses, user satisfaction scores, busy and slow seasons, necessary supplies and equipment, and whether multidisciplinary collaboration is required. For example, for the "bathing assistance" job, you would register "physical strength (high)", "hygiene management", "bathing assistance experience (1 year or more)", "certified care worker", "polite communication", "elderly female users", "morning", "A facility bathroom", "2-person team", "high risk", "manual available", "2 accidents in the past 3 years", "user satisfaction 4.5 / 5", "busy season: summer", "necessary items: lift, non-slip mat", etc. For the "meal assistance" job, you would register "observational skills", "hygiene management", "meal assistance experience (6 months or more)", "initial care worker training", "empathy", "users with swallowing difficulties", "lunch / dinner", "B facility dining hall", "1-person team", "medium risk", "manual available", "1 accident in the past 3 years", "user satisfaction 4.2 / 5", "busy season: year-end and New Year", "necessary items: swallowing aid food", etc. For "rehabilitation assistance" duties, register items such as "exercise instruction," "observational skills," "rehabilitation assistance experience (1 year or more)," "physical therapist," "encouragement," "users with high care needs," "afternoon," "rehabilitation room at Facility C," "1-person system," "medium risk," "manual available," "0 accidents in the past 3 years," "user satisfaction rating of 4.7 / 5," "busy season: spring," and "necessary items: walker, rehabilitation equipment." For "night shift" duties, register items such as "emergency response," "observational skills," "night shift experience (1 year or more)," "certified care worker," "calmness," "users with dementia," "nighttime," "entire Facility A," "2-person system," "high risk," "manual available," "5 accidents in the past 3 years," "user satisfaction rating of 4.0 / 5," "busy season: winter," and "necessary items: nurse call terminal."The Business Information Management Department not only allows administrators to input and update business requirements from their terminals, but also integrates with business management systems, user management systems, accident management systems, evaluation systems, and equipment management systems to automatically reflect workload status, changes in user status, emergence of high-priority tasks, changes in work location and time, equipment failure and maintenance information, and automatic determination of busy and slow periods. For example, business information is updated in real time if a user's condition suddenly changes and emergency response is required, if the location of bathing assistance work changes due to bathroom renovation work, or if the number of staff or required skills change due to equipment failure.

[0068] The Aptitude Analysis and Recommendation Department uses generative AI to compare staff information acquired by the Staff Information Acquisition Department with information on each job managed by the Job Information Management Department. It then comprehensively and objectively analyzes staff skills, experience, qualifications, physical characteristics, communication skills, past performance, stress tolerance, desired shifts, desired job content, available work area, health status, career vision, self-introduction, evaluations from users and colleagues, accident / near-miss history, awards, and training history. Based on this analysis, it scores the most suitable job for each staff member and recommends jobs with a high degree of suitability. For example, the generative AI uses natural language processing to analyze unstructured data such as free-form self-introductions, past evaluation comments, stress tolerance, and communication style, and extracts aptitudes such as, "Staff with high physical strength and the ability to provide careful service are suitable for bathing assistance," "Staff with high empathy and excellent observational skills are suitable for meal assistance," "Staff who are good at encouraging others and have experience in exercise instruction are suitable for rehabilitation support," and "Staff with calm demeanor and high stress tolerance are suitable for night shift work." Furthermore, the generative AI also takes into account the staff member's past performance, accident / near-miss history, user feedback, degree of match with shift and work preferences, degree of match with available work area, health status and physical fitness level, qualification expiration dates, training history, awards, and consistency with career vision to calculate a score using factors such as the number of matching skills, experience, qualifications, physical characteristics, communication characteristics, past performance, stress tolerance suitability, degree of match with desired conditions, health suitability, and consistency with career path. For example, if staff member A possesses "physical fitness," "hygiene management," "observational skills," and "empathy," has "bathing assistance experience" and "meal assistance experience," holds "certified care worker" and "initial care worker training" qualifications, has high physical fitness, excellent empathy, has previously handled "bathing assistance" and "meal assistance," has high stress tolerance, desires night shifts, and wishes to work at facility A, then staff member A would receive high scores for bathing assistance and night shift work. On the other hand, the score for rehabilitation support work is low due to a lack of qualifications and experience.Furthermore, if Staff B possesses "rehabilitation assistance experience," a "physical therapist" qualification, and excels in "encouragement," "exercise guidance," and "observational skills," and wishes to work at Facility C, they can achieve the highest score in rehabilitation assistance work.

[0069] The aptitude analysis and recommendation department selects several top-performing tasks as recommended tasks based on the suitability score calculated for each staff member, and automatically generates recommendation reasons and career path advice using a generative AI's natural language generation function. For example, it can provide individually optimized recommendation reasons and career advice for each staff member, such as, "Your physical strength and careful approach would be particularly useful in bathing assistance," "Your empathy and observational skills are highly valued, and you are expected to excel in meal assistance," "Your encouraging communication and experience in exercise instruction make it advisable to challenge yourself in rehabilitation support work," or "Your calm demeanor and high stress tolerance would allow you to demonstrate leadership in night shift work." It can also generate specific advice on growth support and career development, such as, "Obtaining a physical therapist qualification would be effective in improving your suitability for rehabilitation support work," "Gaining experience in night shift work will open up a career path to management," or "Enhancing your expertise in dementia care will allow you to challenge yourself in new work areas." Furthermore, it can automatically adjust work assignments to avoid overwork and health risks, taking into account the staff member's health condition and workload. For example, it is possible to provide meticulous care, such as avoiding heavy transfer assistance tasks for staff with a history of lower back pain, not assigning certain tasks to staff with allergies, and reducing the workload if abnormalities are found in health checkups.

[0070] As described above, according to the embodiment of the present invention, the staff information acquisition unit, the work information management unit, and the aptitude analysis and recommendation unit work together to comprehensively and objectively analyze diverse staff information using generative AI, and automatically generate and provide individually optimized work assignments and career support information. This makes it possible to simultaneously achieve multifaceted effects such as work assignments that make the most of staff abilities and aptitudes, improved work efficiency, improved service quality, increased staff satisfaction and retention rates, improved user safety and satisfaction, accident prevention, reduced burden on the field, and support for staff growth. For example, even during busy periods or when sudden staff shortages occur, the system can immediately present the optimal staff assignment plan, enabling flexible operations on site. Furthermore, by supporting staff career development and skill improvement, it contributes to long-term human resource development and strengthening organizational capabilities. In addition, managers can continuously improve on-site operational efficiency and service quality by referring to the recommendation results and advice obtained from the system when creating shifts and assigning tasks.

[0071] (System Configuration)

[0072] The system according to this embodiment includes a staff information acquisition unit, a business information management unit, an aptitude analysis and recommendation unit, a generation AI integration unit, a results presentation unit, and an administrator operation unit.The Staff Information Acquisition Department registers basic information for each staff member working in the caregiving field, such as name, age, gender, contact information, work schedule, affiliated facility, and department. It also collects skill information such as physical fitness, hygiene management, observation skills, empathy, exercise instruction, emergency response, record keeping, recreation planning, dementia care, vital sign measurement, medication management, meal assistance, toileting assistance, bathing assistance, transfer assistance, and rehabilitation assistance. Furthermore, it records experience in bathing assistance, meal assistance, night shifts, rehabilitation assistance, dementia care, end-of-life care, emergency response, accident / near-miss response, and new employee training. Information on experience such as work experience and experience in interprofessional collaboration; information on qualifications such as certified care worker, care worker introductory training, practical training, physical therapist, occupational therapist, nurse, dementia care specialist, welfare equipment specialist consultant, care manager; physical characteristics such as physical strength level, history of back pain, presence of chronic illnesses, height and weight, grip strength, vision and hearing, allergy information; communication skills such as empathy, encouragement, calmness, politeness, explanatory ability, listening ability, humor, and multilingual ability; past experience such as bathing assistance, meal assistance, rehabilitation assistance, night shifts, recreation management, record keeping, family support, emergency response, and end-of-life care. For each care task, detailed records are kept of the number of times and duration of assignments, feedback from users, whether accidents occurred, and performance evaluation scores. Stress tolerance is also managed through self-reports (high, average, low), stress check results, past resignation / leave of absence history, and mental health training attendance history. Desired shifts can be specified in detail, including day shifts, night shifts, early shifts, late shifts, weekend shifts, holiday shifts, ability to work consecutive shifts, and preference for short-time work. Multiple selections of desired tasks are possible, such as bathing assistance, night shifts, rehabilitation assistance, record keeping, and recreation management. Multiple facility selections (e.g., Facility A, Facility B, Facility C), commute time, mode of transport, and whether transportation is needed are also registered. Career vision, self-introduction, future goals, training attendance history, awards, recommendation comments from users and colleagues, evaluations on social media and internal communication tools, health check results, and vaccination history can also be entered and retrieved. This information can be entered by staff themselves via terminals, as well as automatically retrieved and updated in conjunction with HR systems, work management systems, health management systems, evaluation systems, shift management systems, and external qualification authentication systems.

[0073] The Business Information Management Department centrally manages all aspects of operations in the caregiving field, including required skills, experience, qualifications, physical characteristics, communication characteristics, job content, difficulty level, time of day, urgency level, user characteristics, location, frequency, duration, number of staff, risk level, availability of manuals, evaluation criteria, history of past accidents and near misses, user satisfaction scores, busy and slow seasons, necessary supplies and equipment, and the necessity of multidisciplinary collaboration. For example, bathing assistance. For assistant duties, the requirements are: high physical strength, hygiene management, experience in bathing assistance (1 year or more), certified care worker, polite communication, elderly female users, mornings, bathroom at Facility A, 2-person system, high risk, manual available, 2 accidents in the past 3 years, user satisfaction 4.5 / 5, peak season: summer, necessary items: lift, non-slip mat, etc. For meal assistance duties, the requirements are: observation skills, hygiene management, experience in meal assistance (6 months or more), initial care worker training, empathy, users with swallowing difficulties, lunch and dinner, dining hall at Facility B, 1-person system, medium risk, manual available, 1 accident in the past 3 years. The first job posting, with a user satisfaction rating of 4.2 / 5, is for the busy year-end and New Year period. Required items include swallowing aids, and rehabilitation support duties include exercise instruction, observation skills, rehabilitation support experience (1 year or more), physical therapist, encouragement, high-level care users, afternoon shifts, C facility rehabilitation room, 1-person system, medium risk, manual available, 0 accidents in the past 3 years. The second job posting, with a user satisfaction rating of 4.7 / 5, is for the busy year-end and New Year period. Required items include walkers and rehabilitation equipment. Night shift duties include emergency response, observation skills, night shift experience (1 year or more), certified care worker, composure, dementia users, nighttime, A facility (entire building). The system registers information such as a two-person team, high risk, manuals, five accidents in the past three years, user satisfaction of 4.0 / 5, peak season: winter, and required equipment: nurse call terminals. This operational information allows administrators to input and update operational requirements from their terminals, and is also linked to the operational management system, user management system, accident management system, evaluation system, and equipment management system. This allows for the automatic reflection of workload status, changes in user status, occurrence of high-priority tasks, changes in work location and time, equipment failure and maintenance information, and automatic determination of peak and off-peak seasons.

[0074] The Aptitude Analysis and Recommendation Department, via the Generation AI Integration Department, compares staff information acquired by the Staff Information Acquisition Department with information on each job managed by the Job Information Management Department. It then comprehensively and objectively analyzes staff members' skills, experience, qualifications, physical characteristics, communication abilities, past performance, stress tolerance, desired shifts, desired job content, available work area, health status, career vision, self-introduction, evaluations from users and colleagues, accident / near-miss history, awards, training history, etc. Based on this analysis, it scores the most suitable job for each staff member and recommends jobs with a high degree of suitability. For example, the AI ​​generation unit inputs a prompt to the AI ​​such as, "Staff A: High physical strength, hygiene management, observational skills, empathy, 3 years of experience in bathing assistance, certified care worker, night shift preference, desires to work at facility A. Job information: Bathing assistance = high physical strength, hygiene management, certified care worker, mornings, facility A bathroom. Based on the above, score which job Staff A is best suited for and explain the reason." The AI ​​then comprehensively evaluates the degree of match between skills, experience, qualifications, and desired conditions, and outputs the optimal job assignment and the reason in natural language. Furthermore, the AI ​​generation unit analyzes unstructured data such as the staff's free-form self-introduction, past evaluation comments, stress tolerance, and communication style, and extracts aptitudes such as, "A staff member with high physical strength and the ability to provide careful service is suitable for bathing assistance," or "A staff member with high empathy and excellent observational skills is suitable for meal assistance," and reflects these in the scoring. Specific examples of prompt messages include: "Staff information: Skills = physical strength, hygiene management, observation skills, empathy; experience = 3 years of bathing assistance, 2 years of meal assistance; qualifications = certified care worker; preference = night shift, Facility A. Job information: bathing assistance = high physical strength, hygiene management, certified care worker, mornings, Facility A bathroom; night shift = emergency response, observation skills, night shift experience, certified care worker, nighttime, entire Facility A. Score which job the staff member is best suited for and explain your reasoning."

[0075] The results presentation unit clearly displays analysis results obtained from the aptitude analysis / recommendation unit and the generation AI collaboration unit, recommended tasks, reasons for recommendation, career path advice, suitability scores, skills and experience to be developed in the future, suitability comparisons for each task, detailed reasons for recommendation, and advice for future growth to staff and managers' terminals. For example, it presents individually optimized reasons for recommendation and growth advice for each staff member, such as, "Your physical strength and careful approach can be particularly utilized in bathing assistance tasks," "Your empathy and observational skills are highly valued, and you are expected to excel in meal assistance tasks," "Your encouraging communication and experience in exercise instruction make it recommended that you take on rehabilitation support tasks," and "Your calm demeanor and high stress tolerance allow you to demonstrate leadership in night shift tasks."

[0076] The administrator control panel allows administrators to perform a wide range of management tasks, including inputting and updating business information, viewing and managing staff information, adjusting parameters for recommendation algorithms, extracting lists of candidates for job assignments, registering performance data after job assignments, checking the overall staffing balance of the organization, setting future staff supply and demand forecasts, analyzing job assignment results, monitoring system operation status, security management, and setting access permissions. For example, when an administrator adds a new job, they can register the required skills and job details, allowing the aptitude analysis and recommendation panel to immediately extract suitable candidates for the new job. Furthermore, by registering staff performance data after job assignments and using it as training data for the generated AI, it is possible to continuously improve the accuracy of recommendations.

[0077] As described above, the system according to this embodiment includes a staff information acquisition unit, a work information management unit, an aptitude analysis and recommendation unit, a generation AI collaboration unit, a results presentation unit, and an administrator operation unit. These units work together to comprehensively and objectively analyze diverse staff information and automatically generate and provide individually optimized work assignments and career support information. Specific examples of prompts to be fed into the generation AI include: "Based on staff information and work information, extract the strengths and weaknesses of the staff, score which work they are best suited for, and explain the reason in natural language," "From the following free-text descriptions, extract the staff's strengths, weaknesses, values, and preferences, and output them as a bulleted list," and "Compare the staff's past work performance with work requirements, calculate a suitability score, and recommend the most suitable work."

[0078] (Implementation Steps)

[0079] Step 1: Enter and automatically retrieve detailed staff information. In the staff information acquisition department, basic information such as name, age, gender, contact information, work style, affiliated facility, and department is entered for each staff member. In addition, skill information such as physical strength, hygiene management, observation skills, empathy, exercise instruction, emergency response, record keeping, recreation planning, dementia care, vital sign measurement, medication management, meal assistance, toileting assistance, bathing assistance, transfer assistance, and rehabilitation assistance is entered. Experience information such as years of experience in bathing assistance, years of experience in meal assistance, years of experience in night shifts, years of experience in rehabilitation assistance, dementia care experience, end-of-life care experience, emergency response experience, accident / near-miss response experience, new employee training experience, and multidisciplinary collaboration experience is entered. Information such as qualifications for care managers, physical characteristics including fitness level, history of back pain, presence of chronic illnesses, height and weight, grip strength, vision and hearing, and allergy information; communication skills including empathy, encouragement, calmness, politeness, explanatory ability, listening ability, humor, and multilingual ability; past work experience, stress tolerance, desired shifts, desired job content, available work area, career vision, self-introduction, future goals, training history, awards, recommendation comments from users and colleagues, evaluations on social media and internal communication tools, health checkup results, and vaccination history are entered via terminals or automatically acquired through integration with HR systems, work management systems, health management systems, evaluation systems, shift management systems, and external qualification authentication systems.

[0080] Step 2: Detailed registration and management of business information The Business Information Management Department centrally registers and manages all tasks in the caregiving field, including required skills, experience, qualifications, physical characteristics, communication characteristics, task content, difficulty level, time of day, urgency level, user characteristics, location, frequency, duration, number of people assigned to the task, risk level, availability of manuals, evaluation criteria, past accident / near-miss history, user satisfaction score, busy / slow seasons, necessary supplies / equipment, and whether multidisciplinary collaboration is required. Administrators input and update work requirements from terminals, and the system also automatically reflects workload status, changes in user condition, occurrence of high-urgency tasks, changes in location and time of day, equipment failure and maintenance information, and automatic determination of busy / slow seasons by linking with the business management system, user management system, accident management system, evaluation system, and equipment management system.

[0081] Step 3: Analysis of staff information using generated AI The Aptitude Analysis and Recommendation Department, via the Generation AI Integration Department, inputs unstructured data such as free-form self-introductions from staff, past evaluation comments, stress tolerance, and communication style into the Generation AI, which then performs natural language processing, semantic analysis, keyword extraction, contextual understanding, and summary generation. A concrete example of a prompt sentence to be fed into the Generation AI is: "Extract strengths, weaknesses, values, and aspirations from the following staff member's self-introduction and career vision, and output them in bullet points. Self-introduction: I have a strong interest in dementia care and bathing assistance, and I previously served as a leader in bathing assistance. I am praised by users and colleagues for my empathy and courteous approach. Career vision: I would like to become a dementia care specialist in the future."

[0082] Step 4: Matching business information with generated AI and calculating a suitability score. The AI ​​generation and collaboration unit compares the analyzed staff information with the detailed work requirements managed by the work information management unit, and calculates a score using factors such as the number of matching skills, the number of matching experience, the number of matching qualifications, the number of matching physical characteristics, the number of matching communication characteristics, whether or not there is past work experience, the degree of suitability of stress tolerance, the degree of suitability with desired conditions, the degree of suitability of health status, and consistency with career path as scoring elements.Specific examples of prompt sentences to be fed into the generation AI include: "Staff information: Skills = physical strength, hygiene management, observation skills, empathy; experience = 3 years of bathing assistance, 2 years of meal assistance; qualifications = certified care worker; desired = night shift, facility A. Work information: bathing assistance = high physical strength, hygiene management, certified care worker, morning, facility A bathroom; night shift = emergency response, observation skills, night shift experience, certified care worker, night, entire facility A. Score which work the staff member is best suited for and explain the reason."

[0083] Step 5: Generating recommendation and career advice using AI. The AI ​​generation and integration unit selects the top-ranking tasks as recommended tasks based on the calculated suitability score, and automatically generates reasons for the recommendations and advice on the staff's career path using natural language generation functionality. Specific examples of prompts to be fed into the AI ​​include: "Based on staff information and task information, extract the staff's strengths and weaknesses, score which tasks they are best suited for, and explain the reasons and provide advice on future career development in natural language."

[0084] Step 6: Presentation of results and feedback The results presentation unit clearly displays analysis results obtained from the aptitude analysis / recommendation unit and the generation AI collaboration unit, recommended tasks, reasons for recommendation, career path advice, suitability scores, skills and experience to be developed in the future, suitability comparisons for each task, detailed reasons for recommendation, and advice for future growth to staff and managers' terminals. For example, it presents individually optimized reasons for recommendation and growth advice for each staff member, such as, "Your physical strength and careful approach can be particularly utilized in bathing assistance tasks," "Your empathy and observational skills are highly valued, and you are expected to excel in meal assistance tasks," "Your encouraging communication and experience in exercise instruction make it recommended that you take on rehabilitation support tasks," and "Your calm demeanor and high stress tolerance allow you to demonstrate leadership in night shift tasks."

[0085] (Specific use cases)

[0086] For example, let's describe a case where the system of the present invention is applied to optimize staff work assignments in a nursing care facility. The staff information acquisition unit inputs or automatically acquires information about staff A from a terminal, including name, age, gender, work style, affiliated facility, high physical fitness level, hygiene management skills, observation skills, empathy, emergency response skills, 3 years of experience in bathing assistance, 2 years of experience in meal assistance, 1 year of experience in night shifts, qualifications as a certified care worker and initial training for care workers, no history of back pain, height of 170cm, no allergies, excellent communication skills with calmness and politeness, past experience in bathing assistance, meal assistance and night shifts, high stress tolerance, desire to work night shifts and bathing assistance, desire to work at facility A, career vision of aiming to become a dementia care specialist in the future, self-introduction stating that "users and colleagues highly value my empathy and polite approach", training attendance history, awards history, health checkup results, vaccination history, etc. The Business Information Management Department registers the following for bathing assistance duties: physical strength (high), hygiene management, bathing assistance experience (1 year or more), certified care worker, polite communication, elderly female users, mornings, bathroom at Facility A, 2-person system, high risk, manual available, 2 accidents in the past 3 years, user satisfaction 4.5 / 5, busy season: summer, necessary items: lift, non-slip mat, etc. For night shift duties: emergency response, observation skills, night shift experience (1 year or more), certified care worker, calmness, users with dementia, nighttime, entire Facility A, 2-person system, high risk, manual available, 5 accidents in the past 3 years, user satisfaction 4.0 / 5, busy season: winter, necessary items: nurse call terminal, etc.

[0087] The Aptitude Analysis and Recommendation Department, via the Generation AI Integration Department, compares information such as Staff A's skills, experience, qualifications, physical characteristics, communication ability, past work experience, stress tolerance, desired shifts, desired job content, available work area, career vision, self-introduction, and health check results with the requirements information for bathing assistance and night shift work. It calculates a score using factors such as the number of matching skills, experience, qualifications, physical characteristics, communication characteristics, presence or absence of past work experience, suitability of stress tolerance, suitability with desired conditions, suitability of health status, and consistency with career path as scoring elements. The Generation AI extracts characteristics such as "empathy," "courteousness," and "inclination towards dementia care" from Staff A's self-introduction and career vision, and evaluates their affinity with the job requirements. Furthermore, since Staff A desires night shift work and possesses night shift experience, emergency response skills, and composure, their suitability for night shift work is also highly rated.

[0088] Specific examples of prompt text to be fed into the generating AI include: "Staff information: Skills = physical strength, hygiene management, observation skills, empathy, emergency response; experience = 3 years of bathing assistance, 2 years of meal assistance, 1 year of night shift; qualifications = certified care worker, initial training for care workers; physical characteristics = high physical strength, no history of back pain; communication = calm, polite; past assignments = bathing assistance, meal assistance, night shift; stress tolerance = high; preferences = night shift, bathing assistance, Facility A; career vision = aspiring dementia care specialist; self-introduction = highly rated for empathy and polite service. Job information: bathing assistance = high physical strength, hygiene management, certified care worker, polite communication, morning, Facility A bathroom; night shift = emergency response, observation skills, night shift experience, certified care worker, calm, nighttime, entire Facility A. Score which job Staff A is best suited for, and explain the reason and provide advice on future career development in natural language."

[0089] The generating AI analyzes Staff A's information and job requirements from multiple angles and automatically generates recommendation reasons and career advice such as, "Your physical strength and careful approach are particularly valuable in bathing assistance. You have 3 years of experience in bathing assistance, a certified care worker qualification, a preference for morning shifts, and a preference for Facility A. Your experience in night shifts, emergency response skills, composure, and high stress tolerance are also highly valued, making you suitable for caring for dementia patients. In the future, you can aim to obtain a dementia care specialist qualification and build your career by gaining experience in night shifts."

[0090] Thus, the system of the present invention can use AI to comprehensively analyze detailed staff information and work requirements, automatically generating and presenting optimal work assignments and individually optimized career support information. This enables work assignments that maximize staff abilities and aptitudes, improved service quality, increased staff satisfaction and retention rates, enhanced user safety, reduced workload on-site, accident prevention, and long-term human resource development all at once.

[0091] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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.

[0092] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0093] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0094] For example, the data collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart device 14 and processes it with the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the output device 40 of the smart device 14 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0095] 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.

[0096] [Second Embodiment]

[0097] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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).

[0100] 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, camera 42, and communication interface 44 are also connected to the bus 52.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0107] 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. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the identification processing unit 290 using these models.

[0108] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is realized by the data processing device 12 and the smart glasses 214. 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".

[0109] (Example 1)

[0110] Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0111] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0112] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Furthermore, the processing by the data processing system 10 described above is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the smart glasses 214 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] 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.

[0116] [Third Embodiment]

[0117] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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).

[0120] 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, display 343, and communication interface 44 are also connected to the bus 52.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the headset terminal 314. 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".

[0129] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0130] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0131] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the headset terminal 314 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using generation AI. For example, the serving unit is implemented by the speaker 240 and display 343 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0134] 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.

[0135] [Fourth Embodiment]

[0136] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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).

[0139] 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, controlled object 443, and communication interface 44 are also connected to the bus 52.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. Each part of the system described below is implemented by the data processing device 12 and the robot 414. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0149] (Example 1) Since the process flow is the same as in Example 1 described in the First Embodiment above, the explanation will be omitted.

[0150] (Application Example 1) Since the process flow is the same as in Application Example 1 described in the first embodiment above, the explanation will be omitted.

[0151] The specific processing unit 290 transmits the result of the specific processing obtained in the embodiment 1 or application example 1 described above 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.

[0152] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or external devices, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or external devices.

[0153] For example, the data collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the acquisition unit acquires step count data using the camera 42 and communication I / F 44 of the robot 414 and processes it using the specific processing unit 290 of the data processing device 12. For example, the analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the data from the data collection unit and the acquisition unit. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a cooking menu using a generation AI. For example, the serving unit is implemented by the speaker 240 and controlled object 443 of the robot 414 and the specific processing unit 290 of the data processing device 12 and provides the generated cooking menu to the user. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 this memory.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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 to be incorporated by reference.

[0173] Furthermore, the following additional information is disclosed regarding the above explanation.

[0174] (Claim 1)

[0175] This system supports staff assignment in care settings and comprises a staff information acquisition unit, a work information management unit, and an aptitude analysis and recommendation unit. The staff information acquisition unit has the function of inputting or automatically acquiring information such as skills, experience, qualifications, physical characteristics, communication ability, past work performance, stress tolerance, desired shifts, desired work content, and available work area for each staff member via a terminal. The work information management unit has the function of centrally managing information such as skills, experience, qualifications, physical characteristics, communication characteristics, work content, difficulty level, time of day, urgency level, user characteristics, and location required for each task in care facilities and home care services. The aptitude analysis and recommendation unit uses generative AI to compare staff information acquired by the staff information acquisition unit with information on each task managed by the work information management unit, and after analyzing the staff's skills, experience, qualifications, physical characteristics, communication ability, past work performance, stress tolerance, desired shifts, desired work content, and available work area from multiple perspectives and objectively, scores the most suitable task for each staff member and performs a process to recommend tasks with a high degree of suitability. (Claim 2)

[0176] The system according to claim 1, wherein the aptitude analysis and recommendation department matches information on staff's skills, experience, qualifications, physical characteristics, communication ability, past work experience, stress tolerance, desired shifts, desired job content, and available work area with the skills, experience, qualifications, physical characteristics, communication characteristics, job content, difficulty level, time of day, urgency level, user characteristics, and location required for each job, calculates a score using factors such as the number of matching skills, experience, qualifications, physical characteristics, communication characteristics, presence or absence of past work experience, suitability of stress tolerance, degree of match with desired shifts and desired job content, and degree of match with available work area, extracts the staff's strengths and weaknesses, job suitability, workload tolerance, communication tendencies, stress tolerance, etc., and reflects these in the final suitability score to determine recommended jobs. (Claim 3)

[0177] The system according to claim 1, wherein the aptitude analysis and recommendation department presents multiple top-ranking tasks as recommended tasks based on the suitability score calculated for each staff member, and further automatically generates reasons for the recommendations and advice on the staff member's career path using a natural language generation function of a generative AI, thereby providing the staff member with objective and specific task assignment proposals and career support information. [Explanation of symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. This system supports employees' requests for transfers and comprises an employee information acquisition unit, a department information management unit, and an aptitude analysis and recommendation unit. The employee information acquisition unit has the function of inputting or acquiring information such as skills, experience, abilities, past work performance, and desired department for each employee. The department information management unit has the function of managing information such as the skills, experience, abilities, and work content required for each department within the organization. The aptitude analysis and recommendation unit uses a generative AI to compare employee information acquired by the employee information acquisition unit with information for each department managed by the department information management unit, analyzes the employee's skills, experience, abilities, performance, and preferences from multiple perspectives and objectively, scores the most suitable department for each employee, and performs a process to recommend the department with the highest degree of suitability.

2. The system according to claim 1, wherein the aptitude analysis and recommendation department compares information on an employee's skills, experience, abilities, achievements, and desired department with the skills, experience, abilities, and job content required for each department, calculates a score using the number of matching skills, the number of matching experiences, the number of matching abilities, the number of achievements, and whether or not it is the desired department as additional scoring elements, extracts the employee's strengths and weaknesses using natural language processing by generative AI, and reflects these in the final suitability score to determine the recommended department.

3. The system according to claim 1, wherein the aptitude analysis and recommendation department presents multiple top-ranking departments as recommended departments based on the suitability score calculated for each employee, and further automatically generates reasons for the recommendations and advice on the employee's career plan using a natural language generation function of a generative AI, thereby providing the employee with objective and specific transfer destination suggestions and career support information.

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