system

Abstract

The system according to this embodiment aims to meet the temporary needs of companies by utilizing the expertise of middle-aged and older early retirees and those who have reached retirement age. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and an automation unit. The reception unit registers the user's expertise and career. The reception unit registers the company's work requests. The analysis unit analyzes the information registered by the reception unit and matches the most suitable personnel with the work. The automation unit automates the process from receiving work requests to matching and payment of compensation.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the high expertise of middle-aged and elderly early retirees and retirees at the age of retirement has not been utilized to efficiently meet the temporary needs of enterprises, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize the expertise of middle-aged and elderly early retirees and retirees at the age of retirement to meet the temporary needs of enterprises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and an automation unit. The reception unit registers the user's expertise and career. The reception unit registers the company's job requests. The analysis unit analyzes the information registered by the reception unit and matches the most suitable personnel with the job. The automation unit automates the process from receiving job requests to matching and payment of compensation. [Effects of the Invention]

[0007] The system according to this embodiment can leverage the expertise of middle-aged and older early retirees and those who have reached retirement age to meet the temporary needs of companies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-driven talent matching platform according to an embodiment of the present invention is a system designed to meet the temporary needs of companies by leveraging the high level of expertise possessed by early retirees and retired individuals in their 40s and older. In this system, users register their expertise and careers, and companies register short-term, one-off work requests. The AI ​​then analyzes this information and matches the most suitable personnel with the work. For example, when a user registers their expertise and career, they input detailed information such as past work experience, skills, and evaluations. For example, they might register their experience as an engineer or their project management skills. This information is input into the AI. Next, companies register short-term, one-off work requests. Companies input detailed requirements such as the necessary skills, experience, and the duration of the work. For example, they might register work requests such as consulting on a specific technology or short-term project management. This information is also input into the AI. The AI ​​analyzes the user and company information and matches the most suitable personnel with the work. Based on the user's skills and experience, past work evaluations, etc., the AI ​​recommends the personnel best suited to the company's requirements. For example, for consulting work related to a specific technology, the system recommends users with extensive experience in that technology. In this way, the system can quickly find the optimal talent to meet a company's temporary needs. Furthermore, the AI ​​automates the process from receiving job requests to matching and payment. This enables efficient matching and improves convenience for both companies and talent. For example, when a job request is registered, the AI ​​automatically recommends the most suitable talent, and once a match is made, the payment process is also handled automatically. This system leverages the high level of expertise of early retirees and retired workers in their middle and senior years, allowing companies to respond to their temporary needs quickly and at low cost. This supports the diversification of work styles for middle-aged and older workers and enables companies to secure immediately productive talent. It is also expected to improve the supply and demand balance in the labor market and contribute to alleviating labor shortages. In short, the AI-driven talent matching platform can respond to companies' temporary needs quickly and at low cost.

[0029] The AI-driven talent matching platform according to this embodiment comprises a reception unit, an analysis unit, and an automation unit. The reception unit registers the user's expertise and career. The user's expertise and career include, but are not limited to, work experience, skill sets, and qualifications. The reception unit allows, for example, the user to input detailed information such as past work experience, skills, and evaluations. For example, the user can register their experience as an engineer or their project management skills. The reception unit can also use AI to analyze the user's input information and convert it into an optimal format. The reception unit, which registers business requests from companies, allows companies to register short-term, one-off business requests. Business requests from companies include, for example, the type of project, required skills, and duration. The reception unit allows, for example, the company to input detailed requirements such as required skills and experience and the duration of the work. For example, the company can register business requests such as consulting on a specific technology or short-term project management. The reception unit can also use AI to analyze the company's input information and convert it into an optimal format. The analysis unit analyzes the information registered by the reception unit and matches the most suitable personnel with the work. The analysis unit recommends the most suitable personnel for a company's requirements based on factors such as the user's skills, experience, and past performance evaluations. For example, for consulting work related to a specific technology, it recommends a user with extensive experience in that technology. The analysis unit uses AI to analyze user and company information and perform optimal matching. The automation unit automates the process from receiving job requests to matching and payment of compensation. For example, when a job request is registered, the automation unit's AI automatically recommends the most suitable personnel, and once a match is made, it also automatically handles the payment process. The automation unit uses AI to efficiently manage the process from receiving job requests to matching and payment of compensation. As a result, the AI-driven personnel matching platform according to this embodiment can respond to a company's temporary needs quickly and at low cost.

[0030] The reception desk registers users' expertise and careers. This includes, but is not limited to, work experience, skill sets, and qualifications. The reception desk allows users to input detailed information such as past work experience, skills, and evaluations. For example, users can register their experience as an engineer or their project management skills. The reception desk can also use AI to analyze user input and convert it into an optimal format. Specifically, it analyzes user input using natural language processing technology and converts it into a standardized data format. This allows for consistent management of information entered in different formats. Furthermore, the reception desk can collect additional information, such as past work evaluations and letters of recommendation from third parties, to assess the reliability of user input. This enhances the accuracy of user expertise and careers. The reception desk for registering corporate work requests allows companies to register short-term, one-off work requests. Corporate work requests include, but are not limited to, project type, required skills, and duration. The reception desk allows companies to input detailed requirements such as required skills, experience, and duration. For example, the system can register requests for services such as consulting on specific technologies or short-term project management. The reception department can also use AI to analyze the information entered by companies and convert it into an optimal format. Specifically, it analyzes the content of the service requests entered by companies and organizes the necessary skills and experience in a standardized format. This allows for consistent management of service requests from different companies. Furthermore, the reception department can collect additional information, such as the history of past service requests and evaluations from third parties, to evaluate the reliability of the information entered by companies. This improves the accuracy of service requests from companies.

[0031] The analysis department analyzes information registered by the reception department and matches the most suitable personnel with the tasks. For example, the analysis department recommends personnel best suited to the company's requirements based on the user's skills, experience, and past work evaluations. For instance, for consulting work related to a specific technology, it recommends users with extensive experience in that technology. The analysis department uses AI to analyze user and company information and perform optimal matching. Specifically, the AI ​​uses machine learning algorithms to analyze the user's skill set and experience and compare it with the company's work requirements. For example, it uses natural language processing technology to analyze the user's resume and work evaluations to identify the most suitable personnel for the company's work request. The AI ​​also learns from past matching data and can make more accurate recommendations based on successful matching patterns. Furthermore, the analysis department can collect feedback from users and companies to continuously improve the accuracy of the matching algorithm. For example, it can adjust the algorithm parameters based on feedback from successful matches to improve the accuracy of the next match. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The automation department automates the process from receiving job requests to matching and payment. For example, when a job request is registered, the automation department's AI automatically recommends the most suitable personnel, and once a match is made, it automatically handles the payment process. The automation department uses AI to efficiently manage the process from receiving job requests to matching and payment. Specifically, when a job request is registered, the AI ​​automatically sends the information to the analysis department, which then recommends the most suitable personnel. Once a match is made, the automation department initiates the payment process and automatically generates a contract between the user and the company. Furthermore, the automation department can monitor the progress of the work and send reminders and notifications as needed. For example, when the deadline for a job is approaching, it sends a reminder to the user to encourage completion. Also, once the work is completed, it automatically handles the payment process and transfers the payment to the user's account. In this way, the automation department can efficiently manage the process from receiving job requests to matching and payment, reducing the burden on both users and companies. In addition, the automation department can collect feedback from users and companies and use it to improve the process. For example, based on user feedback, the system improves the format of work requests and payment procedures, providing a more user-friendly platform. Furthermore, the automation unit can reliably transmit information using multiple communication methods. For instance, it uses email, SMS, and push notifications in combination to ensure important information is delivered reliably. This allows the automation unit to provide information quickly and reliably to users and businesses, improving operational efficiency and reliability.

[0033] The analysis department can analyze users' past work performance evaluations and skills. For example, the analysis department can prioritize matching users with high performance evaluations based on their past performance evaluations. For example, it can analyze users' past performance evaluations and prioritize matching users with specific skills or experience. The analysis department can also consider users' past performance evaluations and provide additional training and support to users with low performance evaluations. This allows for more appropriate matching by analyzing users' past performance evaluations and skills. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input users' past performance evaluation data into a generating AI and have the generating AI perform a skills evaluation.

[0034] The recommendation department can recommend the most suitable personnel. For example, the recommendation department recommends personnel who are best suited to the company's requirements based on the user's skills, experience, and past performance evaluations. For example, for consulting work related to a specific technology, it can recommend a user with extensive experience in that technology. The recommendation department can also recommend personnel who are best suited to the company's requirements by considering the user's skills, experience, and past performance evaluations. In this way, by recommending the most suitable personnel, the company can find the personnel who are best suited to its requirements. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input user skill data into a generating AI and have the generating AI perform the recommendation of the most suitable personnel.

[0035] The analysis unit can recommend the most suitable personnel for a company's requirements based on the user's skills, experience, and past work evaluations. For example, the analysis unit can recommend the most suitable personnel for a company's requirements based on the user's skills, experience, and past work evaluations. For example, for consulting work related to a specific technology, it can recommend a user with extensive experience in that technology. The analysis unit can also recommend the most suitable personnel by considering the user's skills, experience, and past work evaluations. This improves the accuracy of matching by recommending the most suitable personnel for a company's requirements based on the user's skills, experience, and past work evaluations. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user skill data into a generating AI and have the generating AI recommend the most suitable personnel.

[0036] The automation unit can automate the process from receiving job requests to matching and payment of compensation. For example, when a job request is registered, the automation unit's AI automatically recommends the most suitable personnel, and once a match is made, it also automatically handles the payment process. The automation unit can efficiently manage the process from receiving job requests to matching and payment of compensation using AI. This enables efficient matching by automating the process from receiving job requests to matching and payment of compensation. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input job request data into a generating AI and have the generating AI perform the automation of matching and payment of compensation.

[0037] The reception department can analyze a user's past work history and provide the most suitable registration format. For example, the reception department can automatically extract relevant skills and experience based on the user's past work experience and reflect them in the registration format. For instance, it can provide a format tailored to a specific industry or job type based on the user's work history. The reception department can also analyze the user's work history and suggest a format that highlights past evaluations and achievements. This allows for more appropriate registration by analyzing the user's past work history and providing the most suitable registration format. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's work history data into a generating AI and have the generating AI provide the most suitable registration format.

[0038] The reception desk can filter users based on their current job status and areas of interest when they register their expertise and career. For example, the reception desk can prioritize registering relevant skills and experience based on the job content the user is currently engaged in. For example, it can highlight relevant work experience and skills based on the user's areas of interest. The reception desk can also register skills and experience that will be useful in the future, taking into account the user's current job status. This allows for more appropriate registration by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's job status data into a generating AI and have the generating AI perform the filtering.

[0039] The reception desk can prioritize registering highly relevant information when users register their expertise and careers, taking into account their geographical location. For example, the reception desk can prioritize registering work experience and skills related to a region based on the user's current geographical location. For instance, it can consider information about regions where the user has previously worked and highlight relevant work experience. The reception desk can also register region-specific skills and experience based on the user's geographical location. This allows for more appropriate registration by prioritizing highly relevant information while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI register highly relevant information.

[0040] The reception department can analyze a user's social media activity and register relevant information when they register their expertise and career. For example, the reception department can extract and register relevant work experience and skills from a user's social media activity. For example, it can register relevant information based on projects and achievements that a user has shared on social media. The reception department can also analyze a user's social media activity and register information related to a specific industry or job type. This allows for more appropriate registration by analyzing a user's social media activity and registering relevant information. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input a user's social media data into a generating AI and have the generating AI perform the registration of relevant information.

[0041] The reception department can analyze a company's past business request history and provide the most suitable registration format. For example, the reception department can automatically extract relevant skills and experience based on the content of past business requests and reflect them in the registration format. For instance, it can provide a format tailored to a specific industry or job type based on a company's business request history. The reception department can also analyze a company's business request history and propose a format that emphasizes past evaluations and achievements. This allows for more appropriate registration by analyzing a company's past business request history and providing the most suitable registration format. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input a company's business request history data into a generating AI and have the generating AI provide the most suitable registration format.

[0042] The reception department can filter job requests based on the company's current project status and required skills when they are registered. For example, the reception department can prioritize registering relevant skills and experience based on the company's current projects. For example, it can highlight required skills and experience based on the company's project status. The reception department can also register skills and experience that will be needed in the future, taking into account the company's current project status. This allows for more appropriate registration by filtering based on the company's current project status and required skills. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the company's project status data into a generating AI and have the generating AI perform the filtering.

[0043] The reception department can prioritize registering highly relevant information when registering business requests, taking into account the company's geographical location. For example, the reception department can prioritize registering business requests related to a region based on the company's current geographical location. For example, it can consider information about regions where the company has made requests in the past and highlight relevant business requests. The reception department can also register region-specific business requests based on the company's geographical location. This allows for more appropriate registration by prioritizing highly relevant information while considering the company's geographical location. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the company's geographical location data into a generating AI and have the generating AI perform the registration of highly relevant information.

[0044] The reception department can analyze a company's social media activity and register relevant information when registering a business request. For example, the reception department can extract and register relevant business requests from a company's social media activity. For example, it can register relevant information based on projects and results shared by a company on social media. The reception department can also analyze a company's social media activity and register information related to specific industries or job types. This allows for more appropriate registration by analyzing a company's social media activity and registering relevant information. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input a company's social media data into a generating AI and have the generating AI perform the registration of relevant information.

[0045] The analysis unit can improve the accuracy of matching by considering the user's past work performance evaluations during the matching process. For example, the analysis unit can prioritize matching users with high evaluations based on their past work performance evaluations. For example, it can analyze users' past work performance evaluations and prioritize matching users with specific skills and experience. The analysis unit can also consider users' past work performance evaluations and provide additional training and support to users with low evaluations. This improves the accuracy of matching by considering users' past work performance evaluations. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input users' past work performance evaluation data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0046] The analysis unit can select the most suitable personnel during the matching process by considering the detailed information of the company's business request. For example, the analysis unit can prioritize selecting users with the necessary skills and experience based on the detailed information of the company's business request. For example, it can analyze the detailed information of the company's business request and select users who specialize in a particular industry or job type. The analysis unit can also consider the detailed information of the company's business request and select users with skills and experience that will be needed in the future. In this way, the most suitable personnel can be selected by considering the detailed information of the company's business request. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the company's business request data into a generating AI and have the generating AI perform the selection of the most suitable personnel.

[0047] The analysis unit can perform matching while considering the geographical distribution of users. For example, the analysis unit can prioritize matching nearby job requests based on the user's current geographical distribution. For example, it can analyze the geographical distribution of users and prioritize matching job requests that are specialized in a particular region. The analysis unit can also consider the geographical distribution of users and select users who are likely to move in the future. This allows for more appropriate matching by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the matching.

[0048] The analysis unit can improve the accuracy of matching by referring to the history of related work requests during the matching process. For example, the analysis unit can select the most suitable user for similar work requests based on the history of past work requests. For example, it can analyze the history of related work requests and prioritize matching users with specific skills and experience. The analysis unit can also refer to the history of related work requests and select users with skills and experience that will be needed in the future. This improves the accuracy of matching by referring to the history of related work requests. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the history of work requests into a generating AI and have the generating AI perform the matching accuracy improvement.

[0049] The automation unit can select the optimal procedure by referring to past process data during the automation process. For example, the automation unit can select the most efficient procedure based on past process data. For example, it can analyze past process data and provide the optimal procedure for a specific work request. The automation unit can also refer to past process data and select procedures that will be needed in the future. In this way, the optimal procedure can be selected by referring to past process data. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input past process data into a generating AI and have the generating AI perform the selection of the optimal procedure.

[0050] The automation unit can customize procedures during the automation process by taking into account the detailed information of the company's business requests. For example, the automation unit can customize the necessary procedures based on the detailed information of the company's business requests. For example, it can analyze the detailed information of the company's business requests and provide procedures tailored to specific industries or job types. The automation unit can also customize procedures that may be needed in the future, taking into account the detailed information of the company's business requests. In this way, procedures can be customized by taking into account the detailed information of the company's business requests. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input the company's business request data into a generating AI and have the generating AI perform the procedure customization.

[0051] The automation unit can select the optimal procedure during the automation process, taking into account the user's geographical location information. For example, the automation unit can prioritize and complete region-related procedures based on the user's current geographical location information. For instance, it can analyze the user's geographical location information and provide procedures specific to a particular region. The automation unit can also select procedures that will be needed in the future, taking into account the user's geographical location information. This allows for the selection of the optimal procedure by considering the user's geographical location information. Some or all of the above-described processes in the automation unit may be performed using AI or not. For example, the automation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the procedure selection.

[0052] The automation unit can optimize procedures by referring to the history of relevant work requests during the automation process. For example, the automation unit can select the most efficient procedure based on the history of past work requests. For example, it can analyze the history of relevant work requests and provide procedures tailored to specific industries or job types. The automation unit can also refer to the history of relevant work requests and optimize procedures that will be needed in the future. This allows for procedure optimization by referring to the history of relevant work requests. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input the history of work requests into a generating AI and have the generating AI perform procedure optimization.

[0053] The analysis unit can perform a detailed analysis of a user's past work performance during the analysis process to assess their skills. For example, the analysis unit can prioritize evaluating skills that are highly rated based on the user's past work performance. For instance, it can analyze a user's past work performance and prioritize evaluating users with specific skills or experience. The analysis unit can also consider the user's past work performance and provide additional training or support for skills that are poorly rated. This improves skill evaluation by thoroughly analyzing the user's past work performance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past work performance data into a generating AI and have the generating AI perform the skill evaluation.

[0054] The analysis unit can evaluate skills while considering the user's current job situation during analysis. For example, the analysis unit can prioritize evaluating relevant skills and experience based on the user's current job situation. For instance, it can analyze the user's current job situation and evaluate skills specific to a particular industry or job type. The analysis unit can also consider the user's current job situation and evaluate skills that will be needed in the future. This improves the skill evaluation by considering the user's current job situation. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's job situation data into a generating AI and have the generating AI perform the skill evaluation.

[0055] The analysis unit can evaluate skills while considering the user's geographical location information during analysis. For example, the analysis unit can prioritize evaluating region-related skills and experience based on the user's current geographical location information. For example, it can analyze the user's geographical location information and evaluate skills specific to a particular region. The analysis unit can also consider the user's geographical location information and evaluate skills that will be needed in the future. This improves skill evaluation by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the skill evaluation.

[0056] The analysis department can optimize skill evaluations by referring to the history of relevant work evaluations during analysis. For example, the analysis department can evaluate the most suitable skills for similar work evaluations based on the history of past work evaluations. For example, it can analyze the history of relevant work evaluations and prioritize the evaluation of users with specific skills or experience. The analysis department can also refer to the history of relevant work evaluations and evaluate skills that will be needed in the future. This improves skill evaluations by referring to the history of relevant work evaluations. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input work evaluation history data into a generating AI and have the generating AI perform skill evaluations.

[0057] The recommendation department can recommend the most suitable candidates by considering the user's past performance evaluations. For example, the recommendation department can prioritize recommending users with high performance evaluations based on the user's past performance evaluations. For example, it can analyze the user's past performance evaluations and prioritize recommending users with specific skills and experience. The recommendation department can also consider the user's past performance evaluations and provide additional training and support to users with low performance evaluations. In this way, the recommendation department can recommend the most suitable candidates by considering the user's past performance evaluations. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input the user's past performance evaluation data into a generating AI and have the generating AI perform the recommendation of the most suitable candidates.

[0058] The recommendation department can select the most suitable personnel by considering the detailed information of the company's business request during the recommendation process. For example, the recommendation department can prioritize selecting users with the necessary skills and experience based on the detailed information of the company's business request. For example, it can analyze the detailed information of the company's business request and select users who specialize in a particular industry or job type. The recommendation department can also consider the detailed information of the company's business request and select users with skills and experience that will be needed in the future. In this way, the recommendation department can select the most suitable personnel by considering the detailed information of the company's business request. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input the company's business request data into a generating AI and have the generating AI perform the selection of the most suitable personnel.

[0059] The recommendation system can make recommendations while considering the geographical distribution of users. For example, the recommendation system can prioritize recommending nearby job requests based on the user's current geographical distribution. For example, it can analyze the geographical distribution of users and prioritize recommending job requests that are specialized in a particular region. The recommendation system can also select users who are likely to relocate in the future, taking their geographical distribution into consideration. This allows for more appropriate recommendations by considering the geographical distribution of users. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user geographical distribution data into a generating AI and have the generating AI perform the recommendations.

[0060] The recommendation department can improve the accuracy of recommendations by referring to the history of related work requests. For example, the recommendation department can select the most suitable user for similar work requests based on the history of past work requests. For example, it can analyze the history of related work requests and prioritize recommending users with specific skills and experience. The recommendation department can also refer to the history of related work requests and select users with skills and experience that will be needed in the future. This improves the accuracy of recommendations by referring to the history of related work requests. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input the history of work requests into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The registration department can analyze a user's past work history and provide the most suitable registration format when registering their expertise and career. For example, it can automatically extract relevant skills and experience based on the user's past job duties and reflect them in the registration format. This allows for the provision of a format tailored to specific industries or job types based on the user's work history. The registration department can also analyze the user's work history and suggest a format that emphasizes past evaluations and achievements. By analyzing the user's past work history and providing the most suitable registration format, more appropriate registration becomes possible.

[0063] The recommendation system can make recommendations while considering the geographical distribution of users. For example, it can prioritize recommending nearby job requests based on the user's current geographical distribution. It can also analyze the geographical distribution of users and prioritize recommending job requests that are specialized in a particular region. Furthermore, it can select users who are likely to relocate in the future, taking their geographical distribution into consideration. This allows for more appropriate recommendations by considering the geographical distribution of users.

[0064] The analysis unit can improve matching accuracy by considering users' past work performance evaluations. For example, it can prioritize matching users with high evaluations based on their past performance. By analyzing users' past performance evaluations, it can prioritize matching users with specific skills and experience. Furthermore, it can provide additional training and support to users with low evaluations, taking their past performance evaluations into account. In this way, considering users' past performance evaluations improves matching accuracy.

[0065] The recommendation department can select the most suitable personnel by considering the detailed information of a company's business request. For example, it can prioritize selecting users with the necessary skills and experience based on the detailed information of the company's business request. By analyzing the detailed information of the company's business request, it can select users who specialize in specific industries or job types. It can also select users with skills and experience that will be needed in the future, considering the detailed information of the company's business request. In this way, the most suitable personnel can be selected by considering the detailed information of the company's business request.

[0066] The automation unit can select the optimal procedure during the automation process by referring to past process data. For example, it can select the most efficient procedure based on past process data. It can analyze past process data and provide the optimal procedure for a specific task request. It can also refer to past process data to select procedures that will be needed in the future. In this way, the optimal procedure can be selected by referring to past process data.

[0067] The recommendation system can improve the accuracy of recommendations by referring to the history of related work requests. For example, it can select the most suitable user for similar work requests based on past work request history. By analyzing the history of related work requests, it can prioritize recommending users with specific skills and experience. It can also refer to the history of related work requests to select users with skills and experience that will be needed in the future. In this way, the accuracy of recommendations is improved by referring to the history of related work requests.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk registers the user's expertise and career. This includes work experience, skill sets, and qualifications. For example, it registers experience as an engineer and project management skills. The reception desk can also use AI to analyze the user's input information and convert it into the optimal format. Step 2: The reception desk registers the company's business request. The business request includes the type of project, required skills, and duration. For example, it registers business requests such as consulting on a specific technology or short-term project management. The reception desk can also use AI to analyze the company's input information and convert it into the optimal format. Step 3: The analysis department analyzes the information registered by the reception department and matches the most suitable personnel with the tasks. Based on the user's skills, experience, and past work evaluations, the analysis department recommends the personnel best suited to the company's requirements. For example, for consulting work related to a specific technology, it recommends a user with extensive experience in that technology. The analysis department uses AI to analyze user and company information and make the best match. Step 4: The automation department automates the process from receiving job requests to matching and payment. When a job request is registered, the automation department's AI automatically recommends the most suitable personnel, and once a match is made, it also automatically handles the payment process. The automation department uses AI to efficiently manage the process from receiving job requests to matching and payment.

[0070] (Example of form 2) The AI-driven talent matching platform according to an embodiment of the present invention is a system designed to meet the temporary needs of companies by leveraging the high level of expertise possessed by early retirees and retired individuals in their 40s and older. In this system, users register their expertise and careers, and companies register short-term, one-off work requests. The AI ​​then analyzes this information and matches the most suitable personnel with the work. For example, when a user registers their expertise and career, they input detailed information such as past work experience, skills, and evaluations. For example, they might register their experience as an engineer or their project management skills. This information is input into the AI. Next, companies register short-term, one-off work requests. Companies input detailed requirements such as the necessary skills, experience, and the duration of the work. For example, they might register work requests such as consulting on a specific technology or short-term project management. This information is also input into the AI. The AI ​​analyzes the user and company information and matches the most suitable personnel with the work. Based on the user's skills and experience, past work evaluations, etc., the AI ​​recommends the personnel best suited to the company's requirements. For example, for consulting work related to a specific technology, the system recommends users with extensive experience in that technology. In this way, the system can quickly find the optimal talent to meet a company's temporary needs. Furthermore, the AI ​​automates the process from receiving job requests to matching and payment. This enables efficient matching and improves convenience for both companies and talent. For example, when a job request is registered, the AI ​​automatically recommends the most suitable talent, and once a match is made, the payment process is also handled automatically. This system leverages the high level of expertise of early retirees and retired workers in their middle and senior years, allowing companies to respond to their temporary needs quickly and at low cost. This supports the diversification of work styles for middle-aged and older workers and enables companies to secure immediately productive talent. It is also expected to improve the supply and demand balance in the labor market and contribute to alleviating labor shortages. In short, the AI-driven talent matching platform can respond to companies' temporary needs quickly and at low cost.

[0071] The AI-driven talent matching platform according to this embodiment comprises a reception unit, an analysis unit, and an automation unit. The reception unit registers the user's expertise and career. The user's expertise and career include, but are not limited to, work experience, skill sets, and qualifications. The reception unit allows, for example, the user to input detailed information such as past work experience, skills, and evaluations. For example, the user can register their experience as an engineer or their project management skills. The reception unit can also use AI to analyze the user's input information and convert it into an optimal format. The reception unit, which registers business requests from companies, allows companies to register short-term, one-off business requests. Business requests from companies include, for example, the type of project, required skills, and duration. The reception unit allows, for example, the company to input detailed requirements such as required skills and experience and the duration of the work. For example, the company can register business requests such as consulting on a specific technology or short-term project management. The reception unit can also use AI to analyze the company's input information and convert it into an optimal format. The analysis unit analyzes the information registered by the reception unit and matches the most suitable personnel with the work. The analysis unit recommends the most suitable personnel for a company's requirements based on factors such as the user's skills, experience, and past performance evaluations. For example, for consulting work related to a specific technology, it recommends a user with extensive experience in that technology. The analysis unit uses AI to analyze user and company information and perform optimal matching. The automation unit automates the process from receiving job requests to matching and payment of compensation. For example, when a job request is registered, the automation unit's AI automatically recommends the most suitable personnel, and once a match is made, it also automatically handles the payment process. The automation unit uses AI to efficiently manage the process from receiving job requests to matching and payment of compensation. As a result, the AI-driven personnel matching platform according to this embodiment can respond to a company's temporary needs quickly and at low cost.

[0072] The reception desk registers users' expertise and careers. This includes, but is not limited to, work experience, skill sets, and qualifications. The reception desk allows users to input detailed information such as past work experience, skills, and evaluations. For example, users can register their experience as an engineer or their project management skills. The reception desk can also use AI to analyze user input and convert it into an optimal format. Specifically, it analyzes user input using natural language processing technology and converts it into a standardized data format. This allows for consistent management of information entered in different formats. Furthermore, the reception desk can collect additional information, such as past work evaluations and letters of recommendation from third parties, to assess the reliability of user input. This enhances the accuracy of user expertise and careers. The reception desk for registering corporate work requests allows companies to register short-term, one-off work requests. Corporate work requests include, but are not limited to, project type, required skills, and duration. The reception desk allows companies to input detailed requirements such as required skills, experience, and duration. For example, the system can register requests for services such as consulting on specific technologies or short-term project management. The reception department can also use AI to analyze the information entered by companies and convert it into an optimal format. Specifically, it analyzes the content of the service requests entered by companies and organizes the necessary skills and experience in a standardized format. This allows for consistent management of service requests from different companies. Furthermore, the reception department can collect additional information, such as the history of past service requests and evaluations from third parties, to evaluate the reliability of the information entered by companies. This improves the accuracy of service requests from companies.

[0073] The analysis department analyzes information registered by the reception department and matches the most suitable personnel with the tasks. For example, the analysis department recommends personnel best suited to the company's requirements based on the user's skills, experience, and past work evaluations. For instance, for consulting work related to a specific technology, it recommends users with extensive experience in that technology. The analysis department uses AI to analyze user and company information and perform optimal matching. Specifically, the AI ​​uses machine learning algorithms to analyze the user's skill set and experience and compare it with the company's work requirements. For example, it uses natural language processing technology to analyze the user's resume and work evaluations to identify the most suitable personnel for the company's work request. The AI ​​also learns from past matching data and can make more accurate recommendations based on successful matching patterns. Furthermore, the analysis department can collect feedback from users and companies to continuously improve the accuracy of the matching algorithm. For example, it can adjust the algorithm parameters based on feedback from successful matches to improve the accuracy of the next match. In addition, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0074] The automation department automates the process from receiving job requests to matching and payment. For example, when a job request is registered, the automation department's AI automatically recommends the most suitable personnel, and once a match is made, it automatically handles the payment process. The automation department uses AI to efficiently manage the process from receiving job requests to matching and payment. Specifically, when a job request is registered, the AI ​​automatically sends the information to the analysis department, which then recommends the most suitable personnel. Once a match is made, the automation department initiates the payment process and automatically generates a contract between the user and the company. Furthermore, the automation department can monitor the progress of the work and send reminders and notifications as needed. For example, when the deadline for a job is approaching, it sends a reminder to the user to encourage completion. Also, once the work is completed, it automatically handles the payment process and transfers the payment to the user's account. In this way, the automation department can efficiently manage the process from receiving job requests to matching and payment, reducing the burden on both users and companies. In addition, the automation department can collect feedback from users and companies and use it to improve the process. For example, based on user feedback, the system improves the format of work requests and payment procedures, providing a more user-friendly platform. Furthermore, the automation unit can reliably transmit information using multiple communication methods. For instance, it uses email, SMS, and push notifications in combination to ensure important information is delivered reliably. This allows the automation unit to provide information quickly and reliably to users and businesses, improving operational efficiency and reliability.

[0075] The analysis department can analyze users' past work performance evaluations and skills. For example, the analysis department can prioritize matching users with high performance evaluations based on their past performance evaluations. For example, it can analyze users' past performance evaluations and prioritize matching users with specific skills or experience. The analysis department can also consider users' past performance evaluations and provide additional training and support to users with low performance evaluations. This allows for more appropriate matching by analyzing users' past performance evaluations and skills. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input users' past performance evaluation data into a generating AI and have the generating AI perform a skills evaluation.

[0076] The recommendation department can recommend the most suitable personnel. For example, the recommendation department recommends personnel who are best suited to the company's requirements based on the user's skills, experience, and past performance evaluations. For example, for consulting work related to a specific technology, it can recommend a user with extensive experience in that technology. The recommendation department can also recommend personnel who are best suited to the company's requirements by considering the user's skills, experience, and past performance evaluations. In this way, by recommending the most suitable personnel, the company can find the personnel who are best suited to its requirements. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input user skill data into a generating AI and have the generating AI perform the recommendation of the most suitable personnel.

[0077] The analysis unit can recommend the most suitable personnel for a company's requirements based on the user's skills, experience, and past work evaluations. For example, the analysis unit can recommend the most suitable personnel for a company's requirements based on the user's skills, experience, and past work evaluations. For example, for consulting work related to a specific technology, it can recommend a user with extensive experience in that technology. The analysis unit can also recommend the most suitable personnel by considering the user's skills, experience, and past work evaluations. This improves the accuracy of matching by recommending the most suitable personnel for a company's requirements based on the user's skills, experience, and past work evaluations. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user skill data into a generating AI and have the generating AI recommend the most suitable personnel.

[0078] The automation unit can automate the process from receiving job requests to matching and payment of compensation. For example, when a job request is registered, the automation unit's AI automatically recommends the most suitable personnel, and once a match is made, it also automatically handles the payment process. The automation unit can efficiently manage the process from receiving job requests to matching and payment of compensation using AI. This enables efficient matching by automating the process from receiving job requests to matching and payment of compensation. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input job request data into a generating AI and have the generating AI perform the automation of matching and payment of compensation.

[0079] The reception desk can estimate the user's emotions and adjust the registration method for their expertise and career based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if the user is relaxed, it can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if the user is in a hurry, allowing for quick registration of expertise and career. This allows for more appropriate registration by adjusting the registration method for expertise and career according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the registration method.

[0080] The reception department can analyze a user's past work history and provide the most suitable registration format. For example, the reception department can automatically extract relevant skills and experience based on the user's past work experience and reflect them in the registration format. For instance, it can provide a format tailored to a specific industry or job type based on the user's work history. The reception department can also analyze the user's work history and suggest a format that highlights past evaluations and achievements. This allows for more appropriate registration by analyzing the user's past work history and providing the most suitable registration format. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the user's work history data into a generating AI and have the generating AI provide the most suitable registration format.

[0081] The reception desk can filter users based on their current job status and areas of interest when they register their expertise and career. For example, the reception desk can prioritize registering relevant skills and experience based on the job content the user is currently engaged in. For example, it can highlight relevant work experience and skills based on the user's areas of interest. The reception desk can also register skills and experience that will be useful in the future, taking into account the user's current job status. This allows for more appropriate registration by filtering based on the user's current job status and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's job status data into a generating AI and have the generating AI perform the filtering.

[0082] The reception desk can estimate the user's emotions and determine the priority of information to register based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize important information and postpone detailed information. For example, if the user is relaxed, it can prioritize detailed information and suggest a customizable input method. The reception desk can also enable the user to quickly register the most important information if the user is in a hurry. This allows for more appropriate registration by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform emotion estimation and information prioritization.

[0083] The reception desk can prioritize registering highly relevant information when users register their expertise and careers, taking into account their geographical location. For example, the reception desk can prioritize registering work experience and skills related to a region based on the user's current geographical location. For instance, it can consider information about regions where the user has previously worked and highlight relevant work experience. The reception desk can also register region-specific skills and experience based on the user's geographical location. This allows for more appropriate registration by prioritizing highly relevant information while considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI register highly relevant information.

[0084] The reception department can analyze a user's social media activity and register relevant information when they register their expertise and career. For example, the reception department can extract and register relevant work experience and skills from a user's social media activity. For example, it can register relevant information based on projects and achievements that a user has shared on social media. The reception department can also analyze a user's social media activity and register information related to a specific industry or job type. This allows for more appropriate registration by analyzing a user's social media activity and registering relevant information. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input a user's social media data into a generating AI and have the generating AI perform the registration of relevant information.

[0085] The reception desk can estimate the emotions of company representatives and adjust the method of registering work requests based on the estimated emotions. For example, if a company representative is stressed, the reception desk can provide a simple interface and minimize the input steps. For example, if a company representative is relaxed, it can provide detailed input options and suggest a customizable input method. The reception desk can also prioritize voice input if a company representative is in a hurry, allowing for quick registration of work requests. This allows for more appropriate registration by adjusting the work request registration method according to the emotions of the company representative. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the emotions of company representatives into a generative AI and have the generative AI perform emotion estimation and adjustment of the registration method.

[0086] The reception department can analyze a company's past business request history and provide the most suitable registration format. For example, the reception department can automatically extract relevant skills and experience based on the content of past business requests and reflect them in the registration format. For instance, it can provide a format tailored to a specific industry or job type based on a company's business request history. The reception department can also analyze a company's business request history and propose a format that emphasizes past evaluations and achievements. This allows for more appropriate registration by analyzing a company's past business request history and providing the most suitable registration format. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input a company's business request history data into a generating AI and have the generating AI provide the most suitable registration format.

[0087] The reception department can filter job requests based on the company's current project status and required skills when they are registered. For example, the reception department can prioritize registering relevant skills and experience based on the company's current projects. For example, it can highlight required skills and experience based on the company's project status. The reception department can also register skills and experience that will be needed in the future, taking into account the company's current project status. This allows for more appropriate registration by filtering based on the company's current project status and required skills. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the company's project status data into a generating AI and have the generating AI perform the filtering.

[0088] The reception desk can estimate the emotions of company representatives and determine the priority of work requests to be registered based on those estimated emotions. For example, if a company representative is stressed, the reception desk can prioritize important work requests and postpone detailed information. For example, if a company representative is relaxed, the reception desk can prioritize detailed work requests and suggest customizable input methods. The reception desk can also enable the quick registration of the most important work requests if the company representative is in a hurry. This allows for more appropriate registration by determining the priority of work requests to be registered according to the emotions of the company representative. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input emotion data of company representatives into a generative AI and have the generative AI perform emotion estimation and determine the priority of work requests.

[0089] The reception department can prioritize registering highly relevant information when registering business requests, taking into account the company's geographical location. For example, the reception department can prioritize registering business requests related to a region based on the company's current geographical location. For example, it can consider information about regions where the company has made requests in the past and highlight relevant business requests. The reception department can also register region-specific business requests based on the company's geographical location. This allows for more appropriate registration by prioritizing highly relevant information while considering the company's geographical location. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the company's geographical location data into a generating AI and have the generating AI perform the registration of highly relevant information.

[0090] The reception department can analyze a company's social media activity and register relevant information when registering a business request. For example, the reception department can extract and register relevant business requests from a company's social media activity. For example, it can register relevant information based on projects and results shared by a company on social media. The reception department can also analyze a company's social media activity and register information related to specific industries or job types. This allows for more appropriate registration by analyzing a company's social media activity and registering relevant information. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input a company's social media data into a generating AI and have the generating AI perform the registration of relevant information.

[0091] The analysis unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply simple matching criteria and perform a quick match. For example, if the user is relaxed, it can apply detailed matching criteria and suggest a customizable match. The analysis unit can also prioritize the most important criteria when the user is in a hurry. This allows for more appropriate matching by adjusting the matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of matching criteria.

[0092] The analysis unit can improve the accuracy of matching by considering the user's past work performance evaluations during the matching process. For example, the analysis unit can prioritize matching users with high evaluations based on their past work performance evaluations. For example, it can analyze users' past work performance evaluations and prioritize matching users with specific skills and experience. The analysis unit can also consider users' past work performance evaluations and provide additional training and support to users with low evaluations. This improves the accuracy of matching by considering users' past work performance evaluations. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input users' past work performance evaluation data into a generating AI and have the generating AI perform the matching accuracy improvement.

[0093] The analysis unit can select the most suitable personnel during the matching process by considering the detailed information of the company's business request. For example, the analysis unit can prioritize selecting users with the necessary skills and experience based on the detailed information of the company's business request. For example, it can analyze the detailed information of the company's business request and select users who specialize in a particular industry or job type. The analysis unit can also consider the detailed information of the company's business request and select users with skills and experience that will be needed in the future. In this way, the most suitable personnel can be selected by considering the detailed information of the company's business request. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the company's business request data into a generating AI and have the generating AI perform the selection of the most suitable personnel.

[0094] The analysis unit can estimate the user's emotions and adjust the display order of matching results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying the most important matching results. For example, if the user is relaxed, it can display detailed matching results and suggest a customizable display method. The analysis unit can also quickly display the most important matching results if the user is in a hurry. This allows for a more appropriate display by adjusting the display order of matching results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display order.

[0095] The analysis unit can perform matching while considering the geographical distribution of users. For example, the analysis unit can prioritize matching nearby job requests based on the user's current geographical distribution. For example, it can analyze the geographical distribution of users and prioritize matching job requests that are specialized in a particular region. The analysis unit can also consider the geographical distribution of users and select users who are likely to move in the future. This allows for more appropriate matching by considering the geographical distribution of users. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user geographical distribution data into a generating AI and have the generating AI perform the matching.

[0096] The analysis unit can improve the accuracy of matching by referring to the history of related work requests during the matching process. For example, the analysis unit can select the most suitable user for similar work requests based on the history of past work requests. For example, it can analyze the history of related work requests and prioritize matching users with specific skills and experience. The analysis unit can also refer to the history of related work requests and select users with skills and experience that will be needed in the future. This improves the accuracy of matching by referring to the history of related work requests. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the history of work requests into a generating AI and have the generating AI perform the matching accuracy improvement.

[0097] The automation unit can estimate the user's emotions and adjust the steps of the automated process based on the estimated emotions. For example, if the user is stressed, the automation unit can provide simple steps and complete the process quickly. For example, if the user is relaxed, it can provide detailed steps and suggest a customizable process. The automation unit can also prioritize the most important steps and complete the process if the user is in a hurry. This allows for a more appropriate process by adjusting the steps of the automated process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and procedure adjustment.

[0098] The automation unit can select the optimal procedure by referring to past process data during the automation process. For example, the automation unit can select the most efficient procedure based on past process data. For example, it can analyze past process data and provide the optimal procedure for a specific work request. The automation unit can also refer to past process data and select procedures that will be needed in the future. In this way, the optimal procedure can be selected by referring to past process data. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input past process data into a generating AI and have the generating AI perform the selection of the optimal procedure.

[0099] The automation unit can customize procedures during the automation process by taking into account the detailed information of the company's business requests. For example, the automation unit can customize the necessary procedures based on the detailed information of the company's business requests. For example, it can analyze the detailed information of the company's business requests and provide procedures tailored to specific industries or job types. The automation unit can also customize procedures that may be needed in the future, taking into account the detailed information of the company's business requests. In this way, procedures can be customized by taking into account the detailed information of the company's business requests. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input the company's business request data into a generating AI and have the generating AI perform the procedure customization.

[0100] The automation unit can estimate the user's emotions and prioritize automated processes based on those emotions. For example, if the user is stressed, the automation unit can prioritize completing important processes and postpone detailed steps. For example, if the user is relaxed, it can prioritize completing detailed processes and suggest customizable steps. The automation unit can also quickly complete the most important processes if the user is in a hurry. This allows for more appropriate processes by prioritizing automated processes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the automation unit may be performed using AI or not. For example, the automation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and priority determination.

[0101] The automation unit can select the optimal procedure during the automation process, taking into account the user's geographical location information. For example, the automation unit can prioritize and complete region-related procedures based on the user's current geographical location information. For instance, it can analyze the user's geographical location information and provide procedures specific to a particular region. The automation unit can also select procedures that will be needed in the future, taking into account the user's geographical location information. This allows for the selection of the optimal procedure by considering the user's geographical location information. Some or all of the above-described processes in the automation unit may be performed using AI or not. For example, the automation unit can input the user's geographical location information data into a generating AI and have the generating AI perform the procedure selection.

[0102] The automation unit can optimize procedures by referring to the history of relevant work requests during the automation process. For example, the automation unit can select the most efficient procedure based on the history of past work requests. For example, it can analyze the history of relevant work requests and provide procedures tailored to specific industries or job types. The automation unit can also refer to the history of relevant work requests and optimize procedures that will be needed in the future. This allows for procedure optimization by referring to the history of relevant work requests. Some or all of the above processes in the automation unit may be performed using AI or not. For example, the automation unit can input the history of work requests into a generating AI and have the generating AI perform procedure optimization.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is stressed, the analysis unit can apply simple analysis criteria and perform a quick analysis. For example, if the user is relaxed, it can apply detailed analysis criteria and suggest a customizable analysis. Also, if the user is in a hurry, the analysis unit can prioritize the most important criteria for the analysis. This allows for a more appropriate analysis by adjusting the analysis criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of analysis criteria.

[0104] The analysis unit can perform a detailed analysis of a user's past work performance during the analysis process to assess their skills. For example, the analysis unit can prioritize evaluating skills that are highly rated based on the user's past work performance. For instance, it can analyze a user's past work performance and prioritize evaluating users with specific skills or experience. The analysis unit can also consider the user's past work performance and provide additional training or support for skills that are poorly rated. This improves skill evaluation by thoroughly analyzing the user's past work performance. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's past work performance data into a generating AI and have the generating AI perform the skill evaluation.

[0105] The analysis unit can evaluate skills while considering the user's current job situation during analysis. For example, the analysis unit can prioritize evaluating relevant skills and experience based on the user's current job situation. For instance, it can analyze the user's current job situation and evaluate skills specific to a particular industry or job type. The analysis unit can also consider the user's current job situation and evaluate skills that will be needed in the future. This improves the skill evaluation by considering the user's current job situation. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's job situation data into a generating AI and have the generating AI perform the skill evaluation.

[0106] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display method.

[0107] The analysis unit can evaluate skills while considering the user's geographical location information during analysis. For example, the analysis unit can prioritize evaluating region-related skills and experience based on the user's current geographical location information. For example, it can analyze the user's geographical location information and evaluate skills specific to a particular region. The analysis unit can also consider the user's geographical location information and evaluate skills that will be needed in the future. This improves skill evaluation by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input the user's geographical location information data into a generating AI and have the generating AI perform the skill evaluation.

[0108] The analysis department can optimize skill evaluations by referring to the history of relevant work evaluations during analysis. For example, the analysis department can evaluate the most suitable skills for similar work evaluations based on the history of past work evaluations. For example, it can analyze the history of relevant work evaluations and prioritize the evaluation of users with specific skills or experience. The analysis department can also refer to the history of relevant work evaluations and evaluate skills that will be needed in the future. This improves skill evaluations by referring to the history of relevant work evaluations. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input work evaluation history data into a generating AI and have the generating AI perform skill evaluations.

[0109] The recommendation system can estimate the user's emotions and adjust recommendation criteria based on those emotions. For example, if the user is stressed, the recommendation system can apply simple recommendation criteria and provide quick recommendations. If the user is relaxed, for example, it can apply detailed recommendation criteria and suggest customizable recommendations. Furthermore, if the user is in a hurry, the recommendation system can prioritize the most important criteria. This allows for more appropriate recommendations by adjusting recommendation criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of recommendation criteria.

[0110] The recommendation department can recommend the most suitable candidates by considering the user's past performance evaluations. For example, the recommendation department can prioritize recommending users with high performance evaluations based on the user's past performance evaluations. For example, it can analyze the user's past performance evaluations and prioritize recommending users with specific skills and experience. The recommendation department can also consider the user's past performance evaluations and provide additional training and support to users with low performance evaluations. In this way, the recommendation department can recommend the most suitable candidates by considering the user's past performance evaluations. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input the user's past performance evaluation data into a generating AI and have the generating AI perform the recommendation of the most suitable candidates.

[0111] The recommendation department can select the most suitable personnel by considering the detailed information of the company's business request during the recommendation process. For example, the recommendation department can prioritize selecting users with the necessary skills and experience based on the detailed information of the company's business request. For example, it can analyze the detailed information of the company's business request and select users who specialize in a particular industry or job type. The recommendation department can also consider the detailed information of the company's business request and select users with skills and experience that will be needed in the future. In this way, the recommendation department can select the most suitable personnel by considering the detailed information of the company's business request. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input the company's business request data into a generating AI and have the generating AI perform the selection of the most suitable personnel.

[0112] The recommendation section can estimate the user's emotions and adjust the display order of recommendation results based on the estimated emotions. For example, if the user is stressed, the recommendation section will prioritize displaying the most important recommendations. For example, if the user is relaxed, it can display detailed recommendations and suggest customizable display methods. The recommendation section can also quickly display the most important recommendations if the user is in a hurry. This allows for more appropriate displays by adjusting the display order of recommendations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation section may be performed using AI or not. For example, the recommendation section can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the display order.

[0113] The recommendation system can make recommendations while considering the geographical distribution of users. For example, the recommendation system can prioritize recommending nearby job requests based on the user's current geographical distribution. For example, it can analyze the geographical distribution of users and prioritize recommending job requests that are specialized in a particular region. The recommendation system can also select users who are likely to relocate in the future, taking their geographical distribution into consideration. This allows for more appropriate recommendations by considering the geographical distribution of users. Some or all of the above processes in the recommendation system may be performed using AI or not. For example, the recommendation system can input user geographical distribution data into a generating AI and have the generating AI perform the recommendations.

[0114] The recommendation department can improve the accuracy of recommendations by referring to the history of related work requests. For example, the recommendation department can select the most suitable user for similar work requests based on the history of past work requests. For example, it can analyze the history of related work requests and prioritize recommending users with specific skills and experience. The recommendation department can also refer to the history of related work requests and select users with skills and experience that will be needed in the future. This improves the accuracy of recommendations by referring to the history of related work requests. Some or all of the above processes in the recommendation department may be performed using AI or not. For example, the recommendation department can input the history of work requests into a generating AI and have the generating AI perform the recommendation accuracy improvement.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The registration department can analyze a user's past work history and provide the most suitable registration format when registering their expertise and career. For example, it can automatically extract relevant skills and experience based on the user's past job duties and reflect them in the registration format. This allows for the provision of a format tailored to specific industries or job types based on the user's work history. The registration department can also analyze the user's work history and suggest a format that emphasizes past evaluations and achievements. By analyzing the user's past work history and providing the most suitable registration format, more appropriate registration becomes possible.

[0117] The analysis unit can estimate the user's emotions and adjust the matching criteria based on those estimates. For example, if the user is stressed, a simple matching criterion can be applied for a quick match. If the user is relaxed, a more detailed matching criterion can be applied to suggest a customizable match. Furthermore, if the user is in a hurry, the system can prioritize the most important criteria for matching. This allows for more appropriate matching by adjusting the matching criteria according to the user's emotions.

[0118] The recommendation system can make recommendations while considering the geographical distribution of users. For example, it can prioritize recommending nearby job requests based on the user's current geographical distribution. It can also analyze the geographical distribution of users and prioritize recommending job requests that are specialized in a particular region. Furthermore, it can select users who are likely to relocate in the future, taking their geographical distribution into consideration. This allows for more appropriate recommendations by considering the geographical distribution of users.

[0119] The automation unit can estimate the user's emotions and adjust the steps of the automated process based on those emotions. For example, if the user is stressed, it can provide simple steps and complete the process quickly. If the user is relaxed, it can provide detailed steps and suggest a customizable process. If the user is in a hurry, it can prioritize the most important steps and complete the process quickly. This allows for a more appropriate process by adjusting the steps of the automated process according to the user's emotions.

[0120] The reception desk can estimate the emotions of the company representative and adjust the process of registering the work request based on that estimation. For example, if the company representative is stressed, it can provide a simple interface and minimize the input steps. If the company representative is relaxed, it can provide detailed input options and suggest a customizable input method. If the company representative is in a hurry, it can prioritize voice input to allow for quick work request registration. This allows for more appropriate registration by adjusting the work request registration process according to the emotions of the company representative.

[0121] The analysis unit can improve matching accuracy by considering users' past work performance evaluations. For example, it can prioritize matching users with high evaluations based on their past performance. By analyzing users' past performance evaluations, it can prioritize matching users with specific skills and experience. Furthermore, it can provide additional training and support to users with low evaluations, taking their past performance evaluations into account. In this way, considering users' past performance evaluations improves matching accuracy.

[0122] The recommendation department can select the most suitable personnel by considering the detailed information of a company's business request. For example, it can prioritize selecting users with the necessary skills and experience based on the detailed information of the company's business request. By analyzing the detailed information of the company's business request, it can select users who specialize in specific industries or job types. It can also select users with skills and experience that will be needed in the future, considering the detailed information of the company's business request. In this way, the most suitable personnel can be selected by considering the detailed information of the company's business request.

[0123] The automation unit can select the optimal procedure during the automation process by referring to past process data. For example, it can select the most efficient procedure based on past process data. It can analyze past process data and provide the optimal procedure for a specific task request. It can also refer to past process data to select procedures that will be needed in the future. In this way, the optimal procedure can be selected by referring to past process data.

[0124] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on those estimates. For example, if the user is stressed, it can apply simple analysis criteria and perform a quick analysis. If the user is relaxed, it can apply detailed analysis criteria and suggest a customizable analysis. If the user is in a hurry, it can prioritize the most important criteria for the analysis. This allows for more accurate analysis by adjusting the analysis criteria according to the user's emotions.

[0125] The recommendation system can improve the accuracy of recommendations by referring to the history of related work requests. For example, it can select the most suitable user for similar work requests based on past work request history. By analyzing the history of related work requests, it can prioritize recommending users with specific skills and experience. It can also refer to the history of related work requests to select users with skills and experience that will be needed in the future. In this way, the accuracy of recommendations is improved by referring to the history of related work requests.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The reception desk registers the user's expertise and career. This includes work experience, skill sets, and qualifications. For example, it registers experience as an engineer and project management skills. The reception desk can also use AI to analyze the user's input information and convert it into the optimal format. Step 2: The reception desk registers the company's business request. The business request includes the type of project, required skills, and duration. For example, it registers business requests such as consulting on a specific technology or short-term project management. The reception desk can also use AI to analyze the company's input information and convert it into the optimal format. Step 3: The analysis department analyzes the information registered by the reception department and matches the most suitable personnel with the tasks. Based on the user's skills, experience, and past work evaluations, the analysis department recommends the personnel best suited to the company's requirements. For example, for consulting work related to a specific technology, it recommends a user with extensive experience in that technology. The analysis department uses AI to analyze user and company information and make the best match. Step 4: The automation department automates the process from receiving job requests to matching and payment. When a job request is registered, the automation department's AI automatically recommends the most suitable personnel, and once a match is made, it also automatically handles the payment process. The automation department uses AI to efficiently manage the process from receiving job requests to matching and payment.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, analysis unit, and automation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and registers the user's expertise and career, as well as the company's job request. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user and company information to perform optimal matching. The automation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates the process from receiving the job request to matching and payment of compensation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, analysis unit, and automation unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and registers the user's expertise and career, as well as the company's job request. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the user and company information to perform optimal matching. The automation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and automates the process from receiving the job request to matching and payment of compensation. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the reception unit, analysis unit, and automation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and registers the user's expertise and career, as well as the company's job request. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user and company information to perform optimal matching. The automation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates the process from receiving the job request to matching and payment of compensation. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the reception unit, analysis unit, and automation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and registers the user's expertise and career, as well as the company's job request. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user and company information to perform optimal matching. The automation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates the process from receiving the job request to matching and payment of compensation. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) A reception desk where users register their expertise and career, A reception desk for registering business requests from companies, The aforementioned reception department analyzes the information registered and matches the most suitable personnel with the tasks, It includes an automation unit that automates the process from receiving job requests to matching and payment of compensation. A system characterized by the following features. (Note 2) It has an analytics department that analyzes users' past work performance and skills. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a recommendation department to recommend the most suitable candidates. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on the user's skills, experience, and past performance evaluations, we recommend the person best suited to the company's requirements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned automation unit, Automate the entire process from receiving job requests to matching them with clients and paying out compensation. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates user sentiment and adjusts the registration method for expertise and careers based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the user's past work history and provide the optimal registration format. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When registering expertise and careers, filtering is performed based on the user's current job status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the information to be registered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When registering expertise and career information, the system prioritizes registering highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users register their expertise and careers, the system analyzes their social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is The system estimates the emotions of company representatives and adjusts the method of registering work requests based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is We analyze a company's past business request history and provide the optimal registration format. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When registering a job request, filtering is performed based on the company's current project status and required skills. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is The system estimates the emotions of company representatives and prioritizes the business requests to be registered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is When registering a business request, the system prioritizes registering highly relevant information by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is When registering a business request, the system analyzes the company's social media activity and registers relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and adjusts the matching criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the matching process, we improve the accuracy of the matching by considering the user's past work performance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the matching process, the most suitable personnel are selected by considering the detailed information of the company's work request. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, It estimates the user's emotions and adjusts the display order of matching results based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, During the matching process, the geographical distribution of users is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During the matching process, we improve the accuracy of the matching by referring to the history of related work requests. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned automation unit, It estimates the user's emotions and adjusts the steps of the automated process based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned automation unit, During the automation process, the optimal procedure is selected by referring to past process data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned automation unit, During the automation process, customize the steps by taking into account the detailed information of the company's business request. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned automation unit, It estimates the user's emotions and prioritizes automated processes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned automation unit, During the automated process, the system selects the optimal procedure by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned automation unit, During the automation process, refer to the history of related work requests to optimize the procedure. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis criteria based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned analysis unit is During the analysis, the user's past work performance evaluations are analyzed in detail to assess their skills. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned analysis unit is During the analysis, skills are evaluated while taking into account the user's current job situation. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned analysis unit is During analysis, the user's geographical location information is taken into consideration when evaluating skills. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned analysis unit is During analysis, refer to the history of relevant performance evaluations to optimize skill assessment. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned recommendation department, It estimates the user's emotions and adjusts the recommendation criteria based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned recommendation department, When making recommendations, the system considers the user's past performance evaluations to recommend the most suitable candidates. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned recommendation department, When making a recommendation, we select the most suitable candidate by considering the detailed information of the company's work request. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned recommendation department, It estimates the user's emotions and adjusts the display order of recommendation results based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned recommendation department, When making recommendations, the geographical distribution of users should be taken into consideration. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned recommendation department, When making recommendations, we improve the accuracy of recommendations by referring to the history of related work requests. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk where users register their expertise and career, A reception desk for registering business requests from companies, The analysis unit analyzes the information registered by the aforementioned reception unit and matches the most suitable personnel with the tasks, It includes an automation unit that automates the process from receiving job requests to matching and payment of compensation. A system characterized by the following features.

2. It has an analytics department that analyzes users' past work performance and skills. The system according to feature 1.

3. We have a recommendation department to recommend the most suitable candidates. The system according to feature 1.

4. The aforementioned analysis unit, Based on the user's skills, experience, and past performance evaluations, we recommend the person best suited to the company's requirements. The system according to feature 1.

5. The aforementioned automation unit, Automate the entire process from receiving job requests to matching them with clients and paying out compensation. The system according to feature 1.

6. The aforementioned reception unit is The system estimates user sentiment and adjusts the registration method for expertise and careers based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned reception unit is We analyze the user's past work history and provide the optimal registration format. The system according to feature 1.

8. The aforementioned reception unit is When registering expertise and careers, filtering is performed based on the user's current job status and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the information to be registered based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When registering expertise and career information, the system prioritizes registering highly relevant information by considering the user's geographical location. The system according to feature 1.

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