System

Abstract

An object of a system according to an embodiment is to efficiently collect accurate information of employees and utilize the information for a human resource visualization tool.SOLUTION: A system includes an information collection part, an information extraction part, and a registration part. The information collection part collects the mail of the employee, the communication tool and the content of the material. An information extraction part extracts and summarizes the business contents and skills of the employees on the basis of the contents of the mails, communication tools and materials collected by the information collection part. The registration unit registers the work contents and skills extracted and summarized by the information extraction unit in the human resource visualization tool.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently collect accurate employee information and use it in human resource visualization tools.

[0005] The system according to the embodiment aims to efficiently collect accurate information about employees and use it as a tool for visualizing human resources. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an information extraction unit, and a registration unit. The information collection unit collects the contents of employees' emails, communication tools, and documents. The information extraction unit extracts and summarizes the employee's job content and skills based on the contents of the emails, communication tools, and documents collected by the information collection unit. The registration unit registers the job content and skills extracted and summarized by the information extraction unit in a human resources visualization tool. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect accurate information about employees and use it as a tool for visualizing human resources. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The human resource visualization system according to an embodiment of the present invention inputs the contents of employees' emails, communication tools, and documents into a generation AI, and based on that information, extracts and summarizes the employee's work content and skills, and automatically registers them in the human resource visualization tool. This allows the human resource visualization system to efficiently collect accurate information about each employee, enabling the use of human resource information without hassle.

[0029] The human resource visualization system according to the embodiment includes an information collection unit, an information extraction unit, and a registration unit. The information collection unit collects the contents of employees' emails, communication tools, and documents. For example, the information collection unit automatically collects employees' business emails. The information collection unit can also collect conversations on chat apps and internal social networking sites. The information collection unit can also collect business documents and store them as digital data. For example, the information collection unit retrieves business emails from an email server and stores them in an analysis database. The information collection unit collects conversation contents using a chat app's API and stores them in a database. The business documents are retrieved from a file server and stored as digital data. The information extraction unit extracts and summarizes employees' work content and skills based on the contents of emails, communication tools, and documents collected by the information collection unit. For example, the information extraction unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to extract project progress and assigned tasks from email content. The information extraction unit can also identify technical skills and knowledge from conversations on chat apps. The information extraction unit can also analyze the content of business documents and summarize employee skills and knowledge. For example, the generation AI analyzes email content and extracts project progress. It analyzes chat app conversation content and identifies technical skills. It analyzes the content of business documents and summarizes employee skills. The registration unit registers the job content and skills extracted and summarized by the information extraction unit in a human resources visualization tool. For example, the registration unit automatically registers the information extracted by the generation AI in the human resources visualization tool's database. The registration unit can also periodically update the database to reflect the latest information. The registration unit can also provide a function to visualize employee skills and job content based on the registered information. For example, the registration unit registers the information extracted by the generation AI in a database and generates an employee skill matrix. The database is periodically updated to reflect the latest information. A dashboard that visualizes employee skills and job content is provided based on the registered information.As a result, the human resource visualization system according to the embodiment can efficiently collect accurate information about each employee and utilize the human resource information without much effort. For example, it can quickly identify employees with project management skills and assign them to appropriate projects. It can also find employees with specific technical skills and use them to solve technical problems. This improves the operational efficiency of the entire company and makes it possible to make the most of employee skills.

[0030] The information collection unit collects data from employees' calendars and task management tools to extract work priorities and schedule management capabilities. For example, the information collection unit inputs data on meetings and tasks registered in employees' calendars into the generation AI and analyzes work priorities and schedule management capabilities. For example, it evaluates an employee's time management capabilities based on the frequency of meetings and the completion status of tasks. The information collection unit also inputs the progress and completion rate of tasks registered in the task management tool into the generation AI to analyze an employee's work performance capabilities. For example, it evaluates an employee's work efficiency based on the speed at which tasks are completed and how priorities are set. The information collection unit also analyzes data from calendars and task management tools to estimate an employee's workload and stress level. For example, it evaluates workload based on a busy schedule or the number of incomplete tasks. This makes it possible to understand an employee's work priorities and schedule management capabilities.

[0031] The information gathering department can collect external data related to employees' work and evaluate the currency of their knowledge and their ability to respond to industry trends. For example, the information gathering department inputs data from industry news and technical blogs into the generation AI to evaluate how up-to-date the employee is with the latest information. For example, the currency of knowledge is evaluated based on the browsing history of the latest articles on a specific technology. The information gathering department also analyzes how well an employee's work content and skills correspond to industry trends based on external data. For example, it can identify employees who have knowledge of the latest technology trends. The information gathering department also analyzes data from industry news and technical blogs to evaluate the depth and breadth of an employee's knowledge. For example, the depth of knowledge is evaluated based on the frequency of browsing articles on a specific technology field. This makes it possible to evaluate the currency of an employee's knowledge and their ability to respond to industry trends.

[0032] The information collection unit can analyze recorded meeting data using voice recognition technology and identify skills and roles based on the content and frequency of statements. For example, the information collection unit converts recorded meeting data into text using voice recognition technology and analyzes the content of statements. For example, it identifies employees' skills and roles based on technical discussions and project progress reports. The information collection unit can also analyze frequency of statements and evaluate employees' roles and leadership based on the number of times and duration of statements in meetings. For example, it can identify employees who speak frequently as potential leaders. The information collection unit can also extract specific keywords and phrases from recorded meeting data using voice recognition technology to evaluate employees' expertise and skills. For example, it can identify employees who frequently use specific technical terms as experts. In this way, it is possible to analyze recorded meeting data and identify employees' skills and roles.

[0033] The information collection department can conduct regular surveys and feedback sessions and input the results into the generation AI. For example, the information collection department can conduct regular surveys to collect information on employees' work content and skills. For example, the surveys can be used to check project progress and technical issues. The information collection department can also hold regular feedback sessions to collect opinions and requests from employees. For example, the feedback sessions can be used to check areas for work improvement and the progress of new skills acquisition. The information collection department can also input the results of the surveys and feedback sessions into the generation AI to analyze employees' work content and skills. For example, the information collection department can identify employees' strengths and weaknesses based on the feedback content. This makes it possible to understand employees' work content and skills through regular surveys and feedback sessions.

[0034] The information extraction unit can predict an employee's career path and growth potential based on the information extracted by the generation AI, and suggest future roles and positions. The information extraction unit, for example, predicts an employee's career path based on the information extracted by the generation AI. For example, it analyzes past work content and skill growth and suggests future roles. The information extraction unit also analyzes the evolution of an employee's skills and knowledge based on the information extracted by the generation AI to evaluate growth potential. For example, it can suggest an employee who has mastered new technology as the next project leader. The information extraction unit also suggests an employee's future position based on the information extracted by the generation AI. For example, it evaluates the possibility of future promotion or transfer based on current work content and skill set. This makes it possible to predict an employee's career path and growth potential and suggest future roles and positions.

[0035] The information extraction unit can analyze an employee's strengths and weaknesses based on the extracted skills and job content, and automatically generate an individual training plan. The information extraction unit, for example, analyzes an employee's strengths and weaknesses based on the skills and job content extracted by the generation AI. For example, it proposes a training plan for employees who lack specific skills. The information extraction unit also builds a system that automatically generates individual training plans based on the extracted information. For example, it proposes training courses to strengthen an employee's weaknesses. The information extraction unit also proposes training plans to make use of an employee's strengths based on the information extracted by the generation AI. For example, it provides specialized training to further improve existing skills. This makes it possible to analyze an employee's strengths and weaknesses, and automatically generate individual training plans.

[0036] The information extraction unit can summarize the extracted information in visual charts and infographics to make it easier to understand visually. For example, the information extraction unit converts the extracted information into a visual chart to make it easier to understand visually. For example, it generates a skill matrix or a flowchart of work content. The information extraction unit also visually summarizes the extracted information using infographics. For example, it graphically displays employee skill sets and work content. The information extraction unit also builds a system that summarizes the extracted information in different formats to make it easier to understand visually. For example, it provides a tool that automatically generates visual charts and infographics. This allows the extracted information to be summarized in a way that is easier to understand visually.

[0037] The information extraction unit automatically translates the extracted information into different languages, allowing evaluations from a global perspective to be obtained. For example, the information extraction unit builds a system that automatically translates the extracted information into different languages ​​and allows evaluations from a global perspective. For example, the information extraction unit translates into multiple languages, such as English, French, and Chinese. The information extraction unit also registers the translated information in a multilingual human resources visualization tool and obtains feedback from users around the world. For example, the information extraction unit updates the information based on evaluations in different languages. The information extraction unit also collects advice and improvement suggestions from a global perspective based on the information translated into different languages. For example, the information extraction unit reflects feedback that takes cultural and market differences into account. This allows the extracted information to be automatically translated into different languages, allowing evaluations from a global perspective to be obtained.

[0038] The registration unit can develop an algorithm that automatically adjusts employee performance evaluations and compensation systems based on the registered information. The registration unit, for example, develops an algorithm that automatically adjusts employee performance evaluations based on the registered information. For example, it calculates an evaluation score based on data on job content and skills. The registration unit also builds a system that automatically adjusts compensation systems based on performance evaluations. For example, it determines bonuses and salary increases based on the evaluation scores. The registration unit also develops an algorithm that updates employee performance evaluations and compensation systems in real time based on the registered information. For example, it adjusts evaluations to reflect the latest work data. This makes it possible to develop an algorithm that automatically adjusts employee performance evaluations and compensation systems based on the registered information.

[0039] The registration department can propose the optimal composition of a project team based on the registered information and maximize team performance. The registration department, for example, builds a system that proposes the optimal composition of a project team based on the registered information. For example, it automatically selects employees with the required skill sets. The registration department also optimizes employee roles and responsibilities based on the registered information to maximize team performance. For example, it proposes employees with leadership skills as team leaders. The registration department also develops a system that adjusts the composition of a project team in real time based on the registered information. For example, it adds or changes team members depending on the progress of the project. This makes it possible to propose the optimal composition of a project team based on the registered information and maximize team performance.

[0040] The registration department can integrate the registered information with databases of other companies and industries to perform benchmark analysis. For example, the registration department integrates the registered information with databases of other companies and industries to build a system for benchmark analysis. For example, it evaluates employee skill levels by comparing them with data from companies in the same industry. The registration department also updates the registered information based on the benchmark analysis and performs evaluations in accordance with industry standards. For example, it evaluates employee skills by comparing them with the average skill level in the industry. The registration department also integrates with databases of other companies and industries to develop a system for performing benchmark analysis in real time. For example, it adjusts the evaluations to reflect the latest industry data. This allows the registered information to be integrated with databases of other companies and industries to perform benchmark analysis.

[0041] The registration unit can build a platform that matches employee skills and provides career advice based on the registered information. The registration unit, for example, builds a platform that matches employee skills based on the registered information. For example, it automatically selects employees with the best skill sets for a specific project. The registration unit also develops a system that suggests career paths for employees based on the registered information in order to provide career advice. For example, it suggests career plans based on skill development. The registration unit also builds a platform that matches employee skills and provides career advice in real time based on the registered information. For example, it updates the advice by reflecting the latest business data. In this way, it is possible to build a platform that matches employee skills and provides career advice based on the registered information.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The information collection department collects data from employees' calendars and task management tools to extract information about work priorities and schedule management abilities. For example, data about meetings and tasks registered in an employee's calendar is input into the generation AI to analyze work priorities and schedule management abilities. The employee's time management abilities are evaluated based on the frequency of meetings and the completion status of tasks. The progress and completion rate of tasks registered in the task management tool are also input into the generation AI to analyze the employee's work performance abilities. The employee's work efficiency is evaluated based on the speed at which tasks are completed and how priorities are set. Furthermore, data from the calendar and task management tool is analyzed to estimate the employee's workload and stress level. Workload is evaluated based on a busy schedule or the number of incomplete tasks. This makes it possible to understand the employee's work priorities and schedule management abilities.

[0044] The Information Gathering Department can collect external data related to employees' work and evaluate the currency of their knowledge and their ability to respond to industry trends. For example, data from industry news and technical blogs can be input into the generation AI to evaluate how up-to-date an employee's knowledge is. The currency of knowledge can be evaluated based on the browsing history of the latest articles on specific technologies. External data can also be used to analyze how well an employee's work content and skills correspond to industry trends. Employees with knowledge of the latest technology trends can be identified. Furthermore, data from industry news and technical blogs can be analyzed to evaluate the depth and breadth of an employee's knowledge. The depth of knowledge can be evaluated based on the frequency of browsing articles on specific technology fields. This makes it possible to evaluate the currency of an employee's knowledge and their ability to respond to industry trends.

[0045] The information collection department can use voice recognition technology to analyze recorded meeting data and identify skills and roles based on what is said and how often it is said. For example, the recorded meeting data is converted into text using voice recognition technology, and the content of what is said is analyzed. Employee skills and roles are identified based on technical discussions and project progress reports. The frequency of speech is also analyzed, and employees' roles and leadership are evaluated based on the number of times and duration of speech in meetings. Employees who speak frequently can be identified as potential leaders. Furthermore, voice recognition technology can be used to extract specific keywords and phrases from recorded meeting data to evaluate employees' expertise and skills. Employees who frequently use specific technical terms can be identified as experts. This makes it possible to analyze recorded meeting data and identify employees' skills and roles.

[0046] The information collection department can conduct regular surveys and feedback sessions and input the results into the generation AI. For example, they can conduct regular surveys to collect information on employees' work content and skills. They can use surveys to check project progress and technical issues. They can also hold regular feedback sessions to collect opinions and requests from employees. They can use feedback sessions to check areas for work improvement and the progress of new skills acquisition. They can also input the results of the surveys and feedback sessions into the generation AI to analyze employees' work content and skills. They can identify employees' strengths and weaknesses based on the feedback content. This makes it possible to understand employees' work content and skills through regular surveys and feedback sessions.

[0047] The information extraction unit can predict an employee's career path and growth potential based on the information extracted by the generation AI, and suggest future roles and positions. For example, it predicts an employee's career path based on the information extracted by the generation AI. It analyzes past work content and skill growth and suggests future roles. It also analyzes the evolution of an employee's skills and knowledge based on the information extracted by the generation AI to evaluate growth potential. It can suggest an employee who has mastered new skills as the next project leader. It also suggests an employee's future position based on the information extracted by the generation AI. It evaluates the possibility of future promotion or transfer based on current work content and skill set. This makes it possible to predict an employee's career path and growth potential, and suggest future roles and positions.

[0048] The information extraction unit can analyze employees' strengths and weaknesses based on the extracted skills and job content, and automatically generate individual training plans. For example, the generation AI analyzes employees' strengths and weaknesses based on the extracted skills and job content. It proposes training plans for employees who lack specific skills. It also builds a system that automatically generates individual training plans based on the extracted information. It proposes training courses to strengthen employees' weaknesses. It also proposes training plans to make the most of employees' strengths based on the information extracted by the generation AI. It provides specialized training to further improve existing skills. This makes it possible to analyze employees' strengths and weaknesses and automatically generate individual training plans.

[0049] The information extraction unit can summarize the extracted information in visual charts and infographics to make it easier to understand visually. For example, it converts the extracted information into visual charts to make it easier to understand visually. It generates skill matrices and flow charts of work content. It also visually summarizes the extracted information using infographics. It graphically displays employee skill sets and work content. It also builds a system that summarizes the extracted information in different formats to make it easier to understand visually. It provides a tool that automatically generates visual charts and infographics, which allows the extracted information to be summarized in a way that makes it easier to understand visually.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The information collection department collects the contents of employees' emails, communication tools, and documents. For example, the information collection department retrieves business emails from the email server and stores them in an analytical database. It also uses the chat app's API to collect conversation content and stores it in the database. Business documents are retrieved from the file server and stored as digital data. Step 2: The information extraction department extracts and summarizes employees' work content and skills based on the content of emails, communication tools, and documents collected by the information collection department. For example, using generative AI, it can extract project progress and assigned tasks from email content, and identify technical skills and knowledge from chat app conversations. It also analyzes the content of work documents and summarizes employees' skills and knowledge. Step 3: The registration unit registers the job content and skills extracted and summarized by the information extraction unit in the human resources visualization tool. For example, the information extracted by the generation AI is automatically registered in the human resources visualization tool's database, and the database is regularly updated to reflect the latest information. In addition, a dashboard that visualizes employee skills and job content is provided based on the registered information.

[0052] (Example 2) The human resource visualization system according to an embodiment of the present invention inputs the contents of employees' emails, communication tools, and documents into a generation AI, and based on that information, extracts and summarizes the employee's work content and skills, and automatically registers them in the human resource visualization tool. This allows the human resource visualization system to efficiently collect accurate information about each employee, enabling the use of human resource information without hassle.

[0053] The human resource visualization system according to the embodiment includes an information collection unit, an information extraction unit, and a registration unit. The information collection unit collects the contents of employees' emails, communication tools, and documents. For example, the information collection unit automatically collects employees' business emails. The information collection unit can also collect conversations on chat apps and internal social networking sites. The information collection unit can also collect business documents and store them as digital data. For example, the information collection unit retrieves business emails from an email server and stores them in an analysis database. The information collection unit collects conversation contents using a chat app's API and stores them in a database. The business documents are retrieved from a file server and stored as digital data. The information extraction unit extracts and summarizes employees' work content and skills based on the contents of emails, communication tools, and documents collected by the information collection unit. For example, the information extraction unit uses a generation AI (e.g., a text generation AI or a multimodal generation AI) to extract project progress and assigned tasks from email content. The information extraction unit can also identify technical skills and knowledge from conversations on chat apps. The information extraction unit can also analyze the content of business documents and summarize employee skills and knowledge. For example, the generation AI analyzes email content and extracts project progress. It analyzes chat app conversation content and identifies technical skills. It analyzes the content of business documents and summarizes employee skills. The registration unit registers the job content and skills extracted and summarized by the information extraction unit in a human resources visualization tool. For example, the registration unit automatically registers the information extracted by the generation AI in the human resources visualization tool's database. The registration unit can also periodically update the database to reflect the latest information. The registration unit can also provide a function to visualize employee skills and job content based on the registered information. For example, the registration unit registers the information extracted by the generation AI in a database and generates an employee skill matrix. The database is periodically updated to reflect the latest information. A dashboard that visualizes employee skills and job content is provided based on the registered information.As a result, the human resource visualization system according to the embodiment can efficiently collect accurate information about each employee and utilize the human resource information without much effort. For example, it can quickly identify employees with project management skills and assign them to appropriate projects. It can also find employees with specific technical skills and use them to solve technical problems. This improves the operational efficiency of the entire company and makes it possible to make the most of employee skills.

[0054] The information collection unit collects data from employees' calendars and task management tools to extract work priorities and schedule management capabilities. For example, the information collection unit inputs data on meetings and tasks registered in employees' calendars into the generation AI and analyzes work priorities and schedule management capabilities. For example, it evaluates an employee's time management capabilities based on the frequency of meetings and the completion status of tasks. The information collection unit also inputs the progress and completion rate of tasks registered in the task management tool into the generation AI to analyze an employee's work performance capabilities. For example, it evaluates an employee's work efficiency based on the speed at which tasks are completed and how priorities are set. The information collection unit also analyzes data from calendars and task management tools to estimate an employee's workload and stress level. For example, it evaluates workload based on a busy schedule or the number of incomplete tasks. This makes it possible to understand an employee's work priorities and schedule management capabilities.

[0055] The information gathering department can collect external data related to employees' work and evaluate the currency of their knowledge and their ability to respond to industry trends. For example, the information gathering department inputs data from industry news and technical blogs into the generation AI to evaluate how up-to-date the employee is with the latest information. For example, the currency of knowledge is evaluated based on the browsing history of the latest articles on a specific technology. The information gathering department also analyzes how well an employee's work content and skills correspond to industry trends based on external data. For example, it can identify employees who have knowledge of the latest technology trends. The information gathering department also analyzes data from industry news and technical blogs to evaluate the depth and breadth of an employee's knowledge. For example, the depth of knowledge is evaluated based on the frequency of browsing articles on a specific technology field. This makes it possible to evaluate the currency of an employee's knowledge and their ability to respond to industry trends.

[0056] The information collection unit uses the emotion estimation function to estimate employees' stress levels and motivation from the content of emails and chats, which can be useful in improving work efficiency and team dynamics. The information collection unit, for example, analyzes the content of emails and chats and estimates employees' stress levels using the emotion estimation function. For example, the stress level is evaluated based on negative expressions and the intensity of emotions. The information collection unit also uses the emotion estimation function to analyze employees' motivation in real time. For example, the level of motivation is evaluated based on positive expressions and the intensity of emotions. The information collection unit also analyzes emotion estimation data based on the content of emails and chats, which can be useful in improving work efficiency and team dynamics. For example, it can suggest workload adjustments for employees with high stress levels. This makes it possible to understand employees' stress levels and motivation, which can be useful in improving work efficiency and team dynamics.

[0057] The information collection unit can analyze recorded meeting data using voice recognition technology and identify skills and roles based on the content and frequency of statements. For example, the information collection unit converts recorded meeting data into text using voice recognition technology and analyzes the content of statements. For example, it identifies employees' skills and roles based on technical discussions and project progress reports. The information collection unit can also analyze frequency of statements and evaluate employees' roles and leadership based on the number of times and duration of statements in meetings. For example, it can identify employees who speak frequently as potential leaders. The information collection unit can also extract specific keywords and phrases from recorded meeting data using voice recognition technology to evaluate employees' expertise and skills. For example, it can identify employees who frequently use specific technical terms as experts. In this way, it is possible to analyze recorded meeting data and identify employees' skills and roles.

[0058] The information collection department can conduct regular surveys and feedback sessions and input the results into the generation AI. For example, the information collection department can conduct regular surveys to collect information on employees' work content and skills. For example, the surveys can be used to check project progress and technical issues. The information collection department can also hold regular feedback sessions to collect opinions and requests from employees. For example, the feedback sessions can be used to check areas for work improvement and the progress of new skills acquisition. The information collection department can also input the results of the surveys and feedback sessions into the generation AI to analyze employees' work content and skills. For example, the information collection department can identify employees' strengths and weaknesses based on the feedback content. This makes it possible to understand employees' work content and skills through regular surveys and feedback sessions.

[0059] The information collection unit uses the emotion estimation function to analyze the emotions of employees when they enter feedback about their work in real time, thereby promoting positive feedback. For example, the information collection unit uses the emotion estimation function to analyze the emotions of employees in real time when they enter feedback. For example, if the feedback contains a strong positive emotion, the feedback content is highlighted. The information collection unit also uses the emotion estimation function to detect feedback containing a strong negative emotion and make improvement suggestions. For example, in response to negative feedback, it displays suggestions from a positive perspective. The information collection unit also provides an interface that promotes positive feedback based on the emotion estimation data when feedback is entered. For example, it displays feedback containing a strong positive emotion preferentially. This makes it possible to analyze the emotions of employees when they enter feedback in real time and promote positive feedback.

[0060] The information extraction unit can predict an employee's career path and growth potential based on the information extracted by the generation AI, and suggest future roles and positions. The information extraction unit, for example, predicts an employee's career path based on the information extracted by the generation AI. For example, it analyzes past work content and skill growth and suggests future roles. The information extraction unit also analyzes the evolution of an employee's skills and knowledge based on the information extracted by the generation AI to evaluate growth potential. For example, it can suggest an employee who has mastered new technology as the next project leader. The information extraction unit also suggests an employee's future position based on the information extracted by the generation AI. For example, it evaluates the possibility of future promotion or transfer based on current work content and skill set. This makes it possible to predict an employee's career path and growth potential and suggest future roles and positions.

[0061] The information extraction unit can analyze an employee's strengths and weaknesses based on the extracted skills and job content, and automatically generate an individual training plan. The information extraction unit, for example, analyzes an employee's strengths and weaknesses based on the skills and job content extracted by the generation AI. For example, it proposes a training plan for employees who lack specific skills. The information extraction unit also builds a system that automatically generates individual training plans based on the extracted information. For example, it proposes training courses to strengthen an employee's weaknesses. The information extraction unit also proposes training plans to make use of an employee's strengths based on the information extracted by the generation AI. For example, it provides specialized training to further improve existing skills. This makes it possible to analyze an employee's strengths and weaknesses, and automatically generate individual training plans.

[0062] The information extraction unit can use the emotion estimation function to analyze employees' emotional reactions to the extracted information and generate summaries that emphasize positive feedback. For example, the information extraction unit can analyze employees' emotional reactions to the extracted information using the emotion estimation function and generate summaries that emphasize positive feedback. For example, parts with strong positive emotions are reflected in the summary text. The information extraction unit also uses the emotion estimation function to preferentially summarize information with few negative emotional reactions. For example, information with a lot of positive feedback is included in the summary text. The information extraction unit also builds a system that automatically generates summaries that emphasize positive feedback based on the emotional reactions to the extracted information. For example, parts with high emotion scores are reflected in the summary text. This makes it possible to analyze employees' emotional reactions to the extracted information and generate summaries that emphasize positive feedback.

[0063] The information extraction unit can summarize the extracted information in visual charts and infographics to make it easier to understand visually. For example, the information extraction unit converts the extracted information into a visual chart to make it easier to understand visually. For example, it generates a skill matrix or a flowchart of work content. The information extraction unit also visually summarizes the extracted information using infographics. For example, it graphically displays employee skill sets and work content. The information extraction unit also builds a system that summarizes the extracted information in different formats to make it easier to understand visually. For example, it provides a tool that automatically generates visual charts and infographics. This allows the extracted information to be summarized in a way that is easier to understand visually.

[0064] The information extraction unit automatically translates the extracted information into different languages, allowing evaluations from a global perspective to be obtained. For example, the information extraction unit builds a system that automatically translates the extracted information into different languages ​​and allows evaluations from a global perspective. For example, the information extraction unit translates into multiple languages, such as English, French, and Chinese. The information extraction unit also registers the translated information in a multilingual human resources visualization tool and obtains feedback from users around the world. For example, the information extraction unit updates the information based on evaluations in different languages. The information extraction unit also collects advice and improvement suggestions from a global perspective based on the information translated into different languages. For example, the information extraction unit reflects feedback that takes cultural and market differences into account. This allows the extracted information to be automatically translated into different languages, allowing evaluations from a global perspective to be obtained.

[0065] The information extraction unit uses the emotion estimation function to collect users' emotional reactions to the summarized information and can improve the accuracy of the summary based on those reactions. The information extraction unit, for example, collects users' emotional reactions to the summarized information in real time and improves the accuracy of the summary based on that data. For example, it prioritizes the adoption of summary sentences with a high number of positive reactions. The information extraction unit also uses the emotion estimation function to collect feedback on the summarized information and regenerates the summary sentence if there are a high number of negative reactions. The information extraction unit also analyzes the users' emotional reaction data and identifies areas for improvement in the summary sentence based on the results. For example, it makes suggestions to revise parts with low emotion scores. In this way, it is possible to collect users' emotional reactions to the summarized information and improve the accuracy of the summary based on those reactions.

[0066] The registration unit can develop an algorithm that automatically adjusts employee performance evaluations and compensation systems based on the registered information. The registration unit, for example, develops an algorithm that automatically adjusts employee performance evaluations based on the registered information. For example, it calculates an evaluation score based on data on job content and skills. The registration unit also builds a system that automatically adjusts compensation systems based on performance evaluations. For example, it determines bonuses and salary increases based on the evaluation scores. The registration unit also develops an algorithm that updates employee performance evaluations and compensation systems in real time based on the registered information. For example, it adjusts evaluations to reflect the latest work data. This makes it possible to develop an algorithm that automatically adjusts employee performance evaluations and compensation systems based on the registered information.

[0067] The registration department can propose the optimal composition of a project team based on the registered information and maximize team performance. The registration department, for example, builds a system that proposes the optimal composition of a project team based on the registered information. For example, it automatically selects employees with the required skill sets. The registration department also optimizes employee roles and responsibilities based on the registered information to maximize team performance. For example, it proposes employees with leadership skills as team leaders. The registration department also develops a system that adjusts the composition of a project team in real time based on the registered information. For example, it adds or changes team members depending on the progress of the project. This makes it possible to propose the optimal composition of a project team based on the registered information and maximize team performance.

[0068] The registration unit can use the emotion estimation function to analyze employees' emotional reactions to the registered information and develop a registration method that emphasizes positive feedback. For example, the registration unit can analyze employees' emotional reactions to the registered information using the emotion estimation function and develop a registration method that emphasizes positive feedback. For example, information with strong positive emotions is preferentially displayed. The registration unit also uses the emotion estimation function to preferentially register information with few negative emotional reactions. For example, information with a lot of positive feedback is stored in a database. The registration unit also builds a system that automatically generates a registration method that emphasizes positive feedback based on the emotional reactions to the registered information. For example, parts with high emotion scores are highlighted. This makes it possible to analyze employees' emotional reactions to the registered information and develop a registration method that emphasizes positive feedback.

[0069] The registration department can integrate the registered information with databases of other companies and industries to perform benchmark analysis. For example, the registration department integrates the registered information with databases of other companies and industries to build a system for benchmark analysis. For example, it evaluates employee skill levels by comparing them with data from companies in the same industry. The registration department also updates the registered information based on the benchmark analysis and performs evaluations in accordance with industry standards. For example, it evaluates employee skills by comparing them with the average skill level in the industry. The registration department also integrates with databases of other companies and industries to develop a system for performing benchmark analysis in real time. For example, it adjusts the evaluations to reflect the latest industry data. This allows the registered information to be integrated with databases of other companies and industries to perform benchmark analysis.

[0070] The registration unit can build a platform that matches employee skills and provides career advice based on the registered information. The registration unit, for example, builds a platform that matches employee skills based on the registered information. For example, it automatically selects employees with the best skill sets for a specific project. The registration unit also develops a system that suggests career paths for employees based on the registered information in order to provide career advice. For example, it suggests career plans based on skill development. The registration unit also builds a platform that matches employee skills and provides career advice in real time based on the registered information. For example, it updates the advice by reflecting the latest business data. In this way, it is possible to build a platform that matches employee skills and provides career advice based on the registered information.

[0071] The registration unit uses the emotion estimation function to monitor the user's emotional response to the registered information in real time and continuously search for the optimal registration method. The registration unit, for example, uses the emotion estimation function to develop a system that monitors the user's emotional response to the registered information in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The registration unit also evaluates the registered information based on the user's emotional response data and continuously searches for the optimal registration method. For example, it prioritizes registering information with a high number of positive emotional responses. The registration unit also builds a system that collects emotion estimation data in real time and uses it to improve the registered information. For example, it dynamically adjusts the registration method according to changes in the user's emotions. This makes it possible to monitor the user's emotional response to the registered information in real time and continuously search for the optimal registration method.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The information collection department collects data from employees' calendars and task management tools to extract information about work priorities and schedule management abilities. For example, data about meetings and tasks registered in an employee's calendar is input into the generation AI to analyze work priorities and schedule management abilities. The employee's time management abilities are evaluated based on the frequency of meetings and the completion status of tasks. The progress and completion rate of tasks registered in the task management tool are also input into the generation AI to analyze the employee's work performance abilities. The employee's work efficiency is evaluated based on the speed at which tasks are completed and how priorities are set. Furthermore, data from the calendar and task management tool is analyzed to estimate the employee's workload and stress level. Workload is evaluated based on a busy schedule or the number of incomplete tasks. This makes it possible to understand the employee's work priorities and schedule management abilities.

[0074] The Information Gathering Department can collect external data related to employees' work and evaluate the currency of their knowledge and their ability to respond to industry trends. For example, data from industry news and technical blogs can be input into the generation AI to evaluate how up-to-date an employee's knowledge is. The currency of knowledge can be evaluated based on the browsing history of the latest articles on specific technologies. External data can also be used to analyze how well an employee's work content and skills correspond to industry trends. Employees with knowledge of the latest technology trends can be identified. Furthermore, data from industry news and technical blogs can be analyzed to evaluate the depth and breadth of an employee's knowledge. The depth of knowledge can be evaluated based on the frequency of browsing articles on specific technology fields. This makes it possible to evaluate the currency of an employee's knowledge and their ability to respond to industry trends.

[0075] The information collection department uses the emotion estimation function to estimate employees' stress levels and motivation from the content of emails and chats, which can be used to improve work efficiency and team dynamics. For example, the content of emails and chats is analyzed and the emotion estimation function is used to estimate employees' stress levels. The stress level is evaluated based on negative expressions and the intensity of emotions. The emotion estimation function is also used to analyze employees' motivation in real time. The level of motivation is evaluated based on positive expressions and the intensity of emotions. Furthermore, the emotion estimation data is analyzed based on the content of emails and chats, which can be used to improve work efficiency and team dynamics. For employees with high stress levels, adjustments to their workload are suggested. This allows employees' stress levels and motivation to be understood, which can be used to improve work efficiency and team dynamics.

[0076] The information collection department can use voice recognition technology to analyze recorded meeting data and identify skills and roles based on what is said and how often it is said. For example, the recorded meeting data is converted into text using voice recognition technology, and the content of what is said is analyzed. Employee skills and roles are identified based on technical discussions and project progress reports. The frequency of speech is also analyzed, and employees' roles and leadership are evaluated based on the number of times and duration of speech in meetings. Employees who speak frequently can be identified as potential leaders. Furthermore, voice recognition technology can be used to extract specific keywords and phrases from recorded meeting data to evaluate employees' expertise and skills. Employees who frequently use specific technical terms can be identified as experts. This makes it possible to analyze recorded meeting data and identify employees' skills and roles.

[0077] The information collection department can conduct regular surveys and feedback sessions and input the results into the generation AI. For example, they can conduct regular surveys to collect information on employees' work content and skills. They can use surveys to check project progress and technical issues. They can also hold regular feedback sessions to collect opinions and requests from employees. They can use feedback sessions to check areas for work improvement and the progress of new skills acquisition. They can also input the results of the surveys and feedback sessions into the generation AI to analyze employees' work content and skills. They can identify employees' strengths and weaknesses based on the feedback content. This makes it possible to understand employees' work content and skills through regular surveys and feedback sessions.

[0078] The information collection unit uses the emotion estimation function to analyze the emotions of employees when they enter feedback about their work in real time, thereby promoting positive feedback. For example, the emotion estimation function is used to analyze the emotions of employees in real time when they enter feedback. If the feedback contains a strong positive emotion, the feedback content is highlighted. The emotion estimation function is also used to detect feedback with a strong negative emotion and make suggestions for improvement. Suggestions from a positive perspective are displayed for negative feedback. Furthermore, when feedback is entered, an interface is provided that promotes positive feedback based on the emotion estimation data. Feedback with a strong positive emotion is displayed preferentially. This allows the emotions of employees when they provide feedback to be analyzed in real time, promoting positive feedback.

[0079] The information extraction unit can predict an employee's career path and growth potential based on the information extracted by the generation AI, and suggest future roles and positions. For example, it predicts an employee's career path based on the information extracted by the generation AI. It analyzes past work content and skill growth and suggests future roles. It also analyzes the evolution of an employee's skills and knowledge based on the information extracted by the generation AI to evaluate growth potential. It can suggest an employee who has mastered new skills as the next project leader. It also suggests an employee's future position based on the information extracted by the generation AI. It evaluates the possibility of future promotion or transfer based on current work content and skill set. This makes it possible to predict an employee's career path and growth potential, and suggest future roles and positions.

[0080] The information extraction unit can analyze employees' strengths and weaknesses based on the extracted skills and job content, and automatically generate individual training plans. For example, the generation AI analyzes employees' strengths and weaknesses based on the extracted skills and job content. It proposes training plans for employees who lack specific skills. It also builds a system that automatically generates individual training plans based on the extracted information. It proposes training courses to strengthen employees' weaknesses. It also proposes training plans to make the most of employees' strengths based on the information extracted by the generation AI. It provides specialized training to further improve existing skills. This makes it possible to analyze employees' strengths and weaknesses and automatically generate individual training plans.

[0081] The information extraction unit can use the emotion estimation function to analyze employees' emotional reactions to the extracted information and generate summaries that emphasize positive feedback. For example, the emotion estimation function analyzes employees' emotional reactions to the extracted information and generates summaries that emphasize positive feedback. Parts with strong positive emotions are reflected in the summary text. The emotion estimation function is also used to prioritize summarizing information with few negative emotional reactions. Information with a lot of positive feedback is included in the summary text. Furthermore, a system is constructed that automatically generates summaries that emphasize positive feedback based on the emotional reactions to the extracted information. Parts with high emotion scores are reflected in the summary text. This makes it possible to analyze employees' emotional reactions to the extracted information and generate summaries that emphasize positive feedback.

[0082] The information extraction unit can summarize the extracted information in visual charts and infographics to make it easier to understand visually. For example, it converts the extracted information into visual charts to make it easier to understand visually. It generates skill matrices and flow charts of work content. It also visually summarizes the extracted information using infographics. It graphically displays employee skill sets and work content. It also builds a system that summarizes the extracted information in different formats to make it easier to understand visually. It provides a tool that automatically generates visual charts and infographics, which allows the extracted information to be summarized in a way that makes it easier to understand visually.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The information collection department collects the contents of employees' emails, communication tools, and documents. For example, the information collection department retrieves business emails from the email server and stores them in an analytical database. It also uses the chat app's API to collect conversation content and stores it in the database. Business documents are retrieved from the file server and stored as digital data. Step 2: The information extraction department extracts and summarizes employees' work content and skills based on the content of emails, communication tools, and documents collected by the information collection department. For example, using generative AI, it can extract project progress and assigned tasks from email content, and identify technical skills and knowledge from chat app conversations. It also analyzes the content of work documents and summarizes employees' skills and knowledge. Step 3: The registration unit registers the job content and skills extracted and summarized by the information extraction unit in the human resources visualization tool. For example, the information extracted by the generation AI is automatically registered in the human resources visualization tool's database, and the database is regularly updated to reflect the latest information. In addition, a dashboard that visualizes employee skills and job content is provided based on the registered information.

[0085] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0087] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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 and / or a LAN.

[0091] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0092] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0093] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0094] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0095] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0102] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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 and / or a LAN.

[0106] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0110] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0113] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0120] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. 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 and / or a LAN.

[0121] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0122] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0123] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0124] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0125] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0126] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0129] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0131] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0133] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0134] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0135] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0136] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0137] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0138] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0139] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0140] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0141] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0142] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0143] 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.

[0144] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0145] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0146] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0147] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0148] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0149] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0150] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0151] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0152] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An information gathering department that collects employee emails, communication tools, and document contents; an information extraction unit that extracts and summarizes the work content and skills of the employee based on the contents of the emails, communication tools, and documents collected by the information collection unit; a registration unit that registers the job content and the skills extracted and summarized by the information extraction unit in a human resource visualization tool. A system characterized by:

2. The information collecting unit Collect data from the employee's calendar and task management tools to identify work priorities and schedule management capabilities 2. The system of claim 1.

3. The information collecting unit Collect external data relevant to the employee's work to assess the currency of their knowledge and their ability to respond to industry trends 2. The system of claim 1.

4. The information collecting unit Estimate the employee's stress level and motivation from the content of the emails and chats, and use this information to improve work efficiency and team dynamics.

2. The system of claim 1.

5. The information collecting unit Using voice recognition technology, the recorded data of the meeting is analyzed, and the skills and roles are identified based on the content and frequency of statements.

2. The system of claim 1.

6. The information collecting unit Conduct regular surveys and feedback sessions and feed the results into generative AI 2. The system of claim 1.

7. The information collecting unit Analyze the sentiment of employees when they provide feedback on their work in real time to encourage positive feedback 2. The system of claim 1.

8. The information extraction unit Based on the information extracted by the generative AI, the employee's career path and growth potential are predicted, and future roles and positions are proposed.

2. The system of claim 1.

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