system

Abstract

The system according to the embodiment aims to simultaneously solve the shortage of human resources in small and medium-sized enterprises and to revitalize senior employees in large enterprises. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a matching unit, and a provision unit. The collection unit collects employee data. The analysis unit analyzes the data collected by the collection unit. The matching unit performs matching based on the analysis results obtained by the analysis unit. The provision unit provides the matching results obtained by the matching unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of simultaneously solving the labor shortage in small and medium-sized enterprises and revitalizing senior employees in large companies.

[0005] The system according to the embodiment aims to simultaneously solve the shortage of human resources in small and medium-sized enterprises and to revitalize senior employees in large enterprises. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a provision unit. The collection unit collects employee data. The analysis unit analyzes the data collected by the collection unit. The matching unit performs matching based on the analysis results obtained by the analysis unit. The provision unit provides the matching results obtained by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can simultaneously solve the shortage of human resources in small and medium-sized enterprises and revitalize senior employees in large enterprises. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A matching system according to an embodiment of the present invention uses a generation AI to match employees of small and medium-sized enterprises with those of large enterprises. This matching system digitizes the large enterprise's company data, employee data, and employee requirements, and digitizes the SME's company data and desired talent data. These data are linked via a generation AI and an API. The generation AI analyzes the large enterprise's employee data and the SME's desired talent data to perform optimal matching. For example, it matches an SME seeking senior employees with specific skills with employees from the large enterprise who possess those skills. The matching results are provided to both companies via an API. This allows large enterprises to reduce labor costs and monetize their businesses. Meanwhile, SMEs can resolve their talent shortages by hiring talent from large enterprises. For example, the matching system can perform a detailed analysis of employees' skills, experience, and requirements to propose talent optimally suited to the SME's needs. Furthermore, by linking data in real time through an API, matching based on the latest information is always possible. This allows the matching system to simultaneously address the SME's talent shortage and revitalize senior employees at large enterprises. For example, it can perform a detailed analysis of employees' skills, experience, and requirements to propose talent optimally suited to the SME's needs. In addition, by sharing data in real time through APIs, matching can always be based on the latest information.

[0029] The matching system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a providing unit. The collection unit collects employee data. The employee data includes, for example, personal information, skill information, and work history, but is not limited to these examples. The collection unit collects, for example, employee skills, experience, and desired work content. The collection unit can also collect employee requests. For example, the employee requests can be collected through questionnaires or interviews. The collection unit can also collect the needs of small and medium-sized enterprises. For example, the collection unit can collect the company's project requirements and resource needs. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, an analysis algorithm to be used and a purpose of the analysis, but is not limited to these examples. For example, the analysis unit can perform an evaluation based on skill matching criteria and years of experience. The analysis unit can also apply different analysis algorithms depending on the category of employee data. For example, a dedicated analysis algorithm for technical skills can be applied to data related to technical skills. The matching unit performs matching based on the analysis results obtained by the analysis unit. The matching is performed based on, for example, a matching evaluation criterion and an algorithm to be used, but is not limited to these examples. For example, the matching unit can match employee requests with the needs of small and medium-sized enterprises. The matching unit can also improve the accuracy of matching by taking into account the interrelationships between employee data. For example, matching can be performed by taking into account the interrelationships between employee skills and the needs of small and medium-sized enterprises. The providing unit provides the matching results obtained by the matching unit. The provision is performed, for example, based on the provision means and the provision timing, but is not limited to such examples. For example, the providing unit can provide the matching results through an API. The providing unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, if the user is nervous, simple, highly visible information can be provided. This allows the matching system according to the embodiment to efficiently collect, analyze, match, and provide employee data.

[0030] The matching system includes a request collection unit that collects requests from employees. The request collection unit collects the requests from employees. The requests include, for example, desired work content and requests for skill improvement, but are not limited to these examples. The request collection unit can collect the requests from employees through, for example, questionnaires or interviews. The request collection unit can also estimate the emotions of employees and adjust the timing of request collection based on the estimated emotions. For example, if a user is feeling stressed, the timing of collection can be delayed so that requests can be collected in a relaxed state. In this way, by collecting employee requests, more appropriate matching can be achieved.

[0031] The matching system includes a needs collection unit that collects the needs of SMEs. The needs collection unit collects the needs of SMEs. The needs include, for example, a company's project requirements and resource needs, but are not limited to such examples. The needs collection unit can collect the needs of SMEs, for example, through questionnaires or interviews. The needs collection unit can also estimate the emotions of the SMEs and adjust the timing of collecting needs based on the estimated emotions. For example, if a user is feeling stressed, the timing of collection can be delayed so that needs can be collected in a relaxed state. This allows for more appropriate matching by collecting the needs of SMEs.

[0032] The matching system includes a linking unit that links data in real time. The linking unit links data in real time. Real-time data linking includes, but is not limited to, for example, the frequency of data updates and delay times. The linking unit can link data, for example, through an API. The linking unit can also estimate a user's emotions and adjust the timing of data linking based on the estimated emotions. For example, if the user is feeling stressed, the timing of data linking can be delayed so that linking can be performed in a relaxed state. By linking data in real time, matching can always be performed based on the latest information.

[0033] The collection unit can collect the skills, experience, and desired work content of employees. The collection unit collects the skills, experience, and desired work content of employees. Skills include, for example, technical skills and soft skills, but are not limited to these examples. For example, the collection unit can collect the technical skills of employees. The collection unit can also collect the soft skills of employees. The collection unit can also collect the work experience of employees. For example, it can collect projects that employees have participated in in the past and the work content. In this way, by collecting detailed information about employees, more accurate matching is possible.

[0034] The analysis unit can perform evaluation based on skill matching criteria and years of experience. The analysis unit performs evaluation based on skill matching criteria and years of experience. Skill matching criteria include, for example, skill level and relevance, but are not limited to these examples. The analysis unit can evaluate the employee's skills based on, for example, skill level. The analysis unit can also perform evaluation based on skill relevance. The analysis unit can also evaluate the employee's experience based on years of experience. For example, evaluation can be performed based on the employee's years of work experience. This enables more appropriate matching by performing evaluation based on skill matching criteria and years of experience.

[0035] The matching department can match the requests of employees with the needs of SMEs. The matching department matches the requests of employees with the needs of SMEs. Requests include, for example, desired work content and desire to improve skills, but are not limited to such examples. Needs include, for example, company project requirements and resource needs, but are not limited to such examples. The matching department can, for example, match the work content desired by employees with the project requirements of SMEs. The matching department can also match the requests of employees to improve skills with the resource needs of SMEs. This allows for more appropriate matching by matching the requests of employees with the needs of SMEs.

[0036] The providing unit can provide the matching results through an API. The providing unit provides the matching results through an API. Examples of APIs include, but are not limited to, a REST API and GraphQL. The providing unit can provide the matching results through, for example, a REST API. The providing unit can also provide the matching results through GraphQL. The providing unit can also estimate the user's emotions and adjust the way in which information is presented based on the estimated emotions. For example, if the user is nervous, simple, highly visible information can be provided. As a result, providing the matching results through an API enables quick and accurate information provision.

[0037] The collection unit can analyze the employee's past work history and select a collection method. The collection unit analyzes the employee's past work history and selects a collection method. Past work history includes, but is not limited to, project history and work results, for example. For example, if the employee has participated in many projects in the past, the collection unit can select a project-based data collection method. Furthermore, if the employee specializes in a specific skill, the collection unit can prioritize collecting data related to that skill. Furthermore, if the employee has demonstrated leadership in the past, the collection unit can focus on collecting data related to leadership. In this way, the optimal collection method can be selected by analyzing the employee's past work history.

[0038] When collecting employee data, the collection unit can filter the data based on the employee's current projects and areas of interest. When collecting employee data, the collection unit can filter the data based on the employee's current projects and areas of interest. Current projects include, but are not limited to, ongoing projects and areas of interest, for example. The collection unit can, for example, prioritize collecting data related to the employee's ongoing projects. The collection unit can also filter and collect data related to the employee's areas of interest. The collection unit can also collect data based on areas in which the employee has expressed interest in the past. In this way, by filtering the data based on the employee's current projects and areas of interest, more relevant data can be collected.

[0039] When collecting employee data, the collection unit can select a collection means according to the employee's input method. When collecting employee data, the collection unit selects a collection means according to the employee's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the employee prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the employee prefers text input, the collection unit can prioritize collecting text data. Furthermore, if the employee prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means according to the employee's input method.

[0040] When collecting employee data, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. When collecting employee data, the collection unit prioritizes collecting highly relevant data by taking into account the employee's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. For example, if an employee works in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if an employee is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, if an employee is working remotely, the collection unit can prioritize collecting data related to the remote work. In this way, by taking into account the employee's geographical location information, more relevant data can be collected.

[0041] The collection unit may analyze the employee's social media activity and collect related data when collecting employee data. The collection unit may analyze the employee's social media activity and collect related data when collecting employee data. Social media activity includes, but is not limited to, the content of posts and the number of followers. For example, the collection unit may collect data related to projects shared by employees on social media. The collection unit may also collect data related to skills mentioned by employees on social media. The collection unit may also collect data related to industries that employees follow on social media. This allows for more relevant data to be collected by analyzing the employee's social media activity.

[0042] The collection unit can customize the collection method by reflecting employees' past feedback when collecting employee data. The collection unit customizes the collection method by reflecting employees' past feedback when collecting employee data. Past feedback includes, but is not limited to, survey results and evaluation comments, for example. The collection unit can adjust the collection method based on feedback provided by employees in the past. The collection unit can also prioritize collection methods that employees have preferred in the past. The collection unit can also avoid collection methods that employees have expressed dissatisfaction with in the past. In this way, a more appropriate collection method can be selected by reflecting employees' past feedback.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the employee data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the employee data during analysis. Importance includes, but is not limited to, for example, the importance of the job or the importance of the project. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the employee data.

[0044] During analysis, the analysis unit can apply different analysis algorithms depending on the category of employee data. During analysis, the analysis unit applies different analysis algorithms depending on the category of employee data. Categories include, for example, technical categories and business categories, but are not limited to these examples. For example, the analysis unit can apply an analysis algorithm dedicated to technical skills to data related to technical skills. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to management skills to data related to management skills. Furthermore, the analysis unit can apply an analysis algorithm dedicated to soft skills to data related to soft skills. In this way, by applying different analysis algorithms depending on the category of employee data, more accurate analysis is possible.

[0045] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. Past analysis results include, for example, past project data and business results, but are not limited to these examples. For example, the analysis unit can correct the current analysis results based on the employee's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the employee's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the employee's past analysis results. In this way, the accuracy of the analysis is improved by referring to the employee's past analysis results.

[0046] During analysis, the analysis unit can determine the analysis priority based on the time of submission of employee data. During analysis, the analysis unit determines the analysis priority based on the time of submission of employee data. The submission time includes, for example, the submission date and the submission frequency, but is not limited to these examples. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also appropriately prioritize data that was submitted at a medium time. In this way, by determining the analysis priority based on the time of submission of employee data, efficient analysis is possible.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the employee data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the employee data during analysis. Relevance includes, for example, the degree of data relevance and the relevance of work, but is not limited to these examples. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of data with low relevance. The analysis unit can also appropriately prioritize data with medium relevance. In this way, by adjusting the order of analysis based on the relevance of the employee data, efficient analysis is possible.

[0048] The analysis unit can adjust the use of technical terminology during analysis according to the employee's level of expertise. The analysis unit can adjust the use of technical terminology during analysis according to the employee's level of expertise. Expertise level includes, but is not limited to, technical skill level and business knowledge level, for example. The analysis unit can provide, for example, analysis results that use a lot of technical terminology to employees with high expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with low expertise. The analysis unit can also provide analysis results that use a moderate amount of technical terminology to employees with intermediate expertise. In this way, by adjusting the use of technical terminology during analysis according to the employee's level of expertise, more appropriate analysis results can be provided.

[0049] The matching unit can improve the accuracy of matching by taking into account the interrelationships of employee data when matching. The matching unit improves the accuracy of matching by taking into account the interrelationships of employee data when matching. Interrelationships include, for example, data relevance and business relevance, but are not limited to these examples. The matching unit can, for example, perform matching by taking into account the interrelationships between employee skills and the needs of SMEs. The matching unit can also perform matching by taking into account the interrelationships between employee experience and SME projects. The matching unit can also perform matching by taking into account the interrelationships between employee requests and SME provision conditions. In this way, by taking into account the interrelationships of employee data, more accurate matching is possible.

[0050] The matching unit can perform matching taking into consideration employee attribute information when matching. The matching unit can perform matching taking into consideration employee attribute information when matching. Attribute information includes, for example, age, gender, and occupation, but is not limited to these examples. The matching unit can perform matching taking into consideration, for example, the employee's age and gender. The matching unit can also perform matching taking into consideration the employee's work location and work style. The matching unit can also perform matching taking into consideration the employee's work history and skill set. In this way, more appropriate matching is possible by taking into consideration employee attribute information.

[0051] The matching unit can weight the matching based on the employee's submission frequency during matching. The matching unit weights the matching based on the employee's submission frequency during matching. Submission frequency includes, but is not limited to, for example, the number of submissions and the submission interval. For example, the matching unit can prioritize matching for employees with a high submission frequency. The matching unit can also postpone employees with a low submission frequency. The matching unit can also moderately prioritize employees with a medium submission frequency. In this way, weighting the matching based on the employee's submission frequency enables more appropriate matching.

[0052] The matching unit can perform matching taking into account the geographic distribution of employee data when performing matching. The matching unit performs matching taking into account the geographic distribution of employee data when performing matching. Geographic distribution includes, for example, data by region or by country, but is not limited to these examples. For example, if an employee works in a specific region, the matching unit can prioritize matching with small and medium-sized enterprises related to that region. Furthermore, if an employee is able to travel on business, the matching unit can also match with small and medium-sized enterprises related to the business trip destination. Furthermore, if an employee desires remote work, the matching unit can also match with small and medium-sized enterprises that allow remote work. This enables more appropriate matching by taking into account the geographic distribution of employee data.

[0053] The matching unit can improve the accuracy of matching by referring to literature related to the employee data when matching. The matching unit can improve the accuracy of matching by referring to literature related to the employee data when matching. Related literature includes, for example, academic papers and technical reports, but is not limited to these examples. For example, the matching unit can perform matching by referring to literature related to the employee's skills. The matching unit can also perform matching by referring to literature related to the employee's experience. The matching unit can also perform matching by referring to literature related to the employee's requests. In this way, by referring to literature related to the employee data, more accurate matching is possible.

[0054] The matching unit can perform matching taking into account the market value of the employee data when matching. The matching unit performs matching taking into account the market value of the employee data when matching. Market value includes, for example, salary level and supply and demand balance, but is not limited to these examples. For example, if the market value of an employee is high, the matching unit can prioritize matching with small and medium-sized enterprises that match that value. Furthermore, if the market value of an employee is low, the matching unit can also match with small and medium-sized enterprises that match that value. Furthermore, if the market value of an employee is medium, the matching unit can appropriately match with small and medium-sized enterprises that match that value. In this way, more appropriate matching is possible by taking into account the market value of the employee data.

[0055] The providing unit can adjust the level of detail to be provided based on the importance of the employee data when providing the data. The providing unit adjusts the level of detail to be provided based on the importance of the employee data when providing the data. Importance includes, for example, the importance of the job or the importance of the project, but is not limited to these examples. For example, the providing unit can provide detailed information for data with high importance. The providing unit can also provide simplified information for data with low importance. The providing unit can also provide information with an appropriate level of detail for data with medium importance. In this way, by adjusting the level of detail to be provided based on the importance of the employee data, efficient information provision is possible.

[0056] The providing unit can apply different providing algorithms depending on the category of employee data when providing the data. The providing unit applies different providing algorithms depending on the category of employee data when providing the data. Categories include, for example, technical categories and business categories, but are not limited to these examples. For example, the providing unit can apply a providing algorithm dedicated to technical skills to data related to technical skills. Furthermore, the providing unit can also apply a providing algorithm dedicated to management skills to data related to management skills. Furthermore, the providing unit can also apply a providing algorithm dedicated to soft skills to data related to soft skills. In this way, by applying different providing algorithms depending on the category of employee data, more appropriate information can be provided.

[0057] The providing unit can improve the accuracy of the data provided by referring to the employee's past provided results when providing the data. The providing unit can improve the accuracy of the data provided by referring to the employee's past provided results when providing the data. Past provided results include, but are not limited to, past project data and business results. The providing unit can correct the current provided results, for example, based on the employee's past provided results. The providing unit can also adjust the providing algorithm by referring to the employee's past provided results. The providing unit can also improve the accuracy of the data provided by referring to the employee's past provided results. In this way, the accuracy of the data provided is improved by referring to the employee's past provided results.

[0058] The providing unit can determine the priority of provision based on the time of submission of employee data at the time of provision. The providing unit can determine the priority of provision based on the time of submission of employee data at the time of provision. The submission time includes, for example, the submission date and the submission frequency, but is not limited to these examples. For example, the providing unit can provide the most recent data preferentially. The providing unit can also postpone data that was submitted earlier. The providing unit can also appropriately prioritize data that was submitted at a medium time. In this way, by determining the priority of provision based on the time of submission of employee data, efficient information provision is possible.

[0059] The providing unit can adjust the order of providing the employee data based on the relevance of the employee data when providing the data. The providing unit can adjust the order of providing the employee data based on the relevance of the employee data when providing the data. Relevance includes, for example, the degree of relevance of the data and the relevance of the work, but is not limited to such examples. For example, the providing unit can prioritize providing highly relevant data. The providing unit can also postpone data with low relevance. The providing unit can also appropriately prioritize data with medium relevance. In this way, by adjusting the order of providing the employee data based on the relevance of the employee data, efficient information provision is possible.

[0060] The provision unit can adjust the use of technical terminology when providing the information according to the employee's level of expertise. The provision unit can adjust the use of technical terminology when providing the information according to the employee's level of expertise. Expertise level includes, but is not limited to, technical skill level and business knowledge level, for example. The provision unit can provide information that uses a lot of technical terminology to employees with high expertise. The provision unit can also provide information that avoids technical terminology to employees with low expertise. The provision unit can also provide information that uses a moderate amount of technical terminology to employees with intermediate expertise. In this way, by adjusting the use of technical terminology when providing the information according to the employee's level of expertise, more appropriate information can be provided.

[0061] When collecting requests, the request collection unit can analyze the employee's past request history and select the optimal collection method. When collecting requests, the request collection unit analyzes the employee's past request history and selects the optimal collection method. The past request history includes, for example, but is not limited to, the type of request and the frequency of requests. For example, if the employee has submitted many requests in the past, the request collection unit can select a request-based collection method. Furthermore, if the employee specializes in a specific skill, the request collection unit can prioritize collecting requests related to that skill. Furthermore, if the employee has demonstrated leadership in the past, the request collection unit can focus on collecting requests related to leadership. In this way, the optimal collection method can be selected by analyzing the employee's past request history.

[0062] The request collection unit can filter requests based on the employee's current projects and areas of interest when collecting requests. The request collection unit can filter requests based on the employee's current projects and areas of interest when collecting requests. Current projects include, but are not limited to, ongoing projects and areas of interest, for example. The request collection unit can, for example, preferentially collect requests related to the employee's ongoing projects. The request collection unit can also filter and collect requests related to areas of interest to the employee. The request collection unit can also collect requests based on areas in which the employee has shown interest in the past. In this way, by filtering requests based on the employee's current projects and areas of interest, more relevant requests can be collected.

[0063] When collecting requests, the request collection unit can prioritize collecting highly relevant requests by taking into account the geographical location information of employees. When collecting requests, the request collection unit prioritizes collecting highly relevant requests by taking into account the geographical location information of employees. Geographical location information includes, but is not limited to, GPS data and address information, for example. For example, if an employee works in a specific area, the request collection unit can prioritize collecting requests related to that area. Furthermore, if an employee is on a business trip, the request collection unit can prioritize collecting requests related to the business trip destination. Furthermore, if an employee is working remotely, the request collection unit can prioritize collecting requests related to the remote work. In this way, by taking into account the geographical location information of employees, more relevant requests can be collected.

[0064] The request collection unit can analyze the employee's social media activity during request collection to collect related requests. The request collection unit can analyze the employee's social media activity during request collection to collect related requests. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. For example, the request collection unit can collect requests related to projects shared by employees on social media. The request collection unit can also collect requests related to skills mentioned by employees on social media. The request collection unit can also collect requests related to industries that employees follow on social media. This makes it possible to collect more relevant requests by analyzing the employee's social media activity.

[0065] When collecting needs, the needs gathering unit can analyze the SME's past needs history and select the optimal collection method. When collecting needs, the needs gathering unit analyzes the SME's past needs history and select the optimal collection method. Past needs history includes, for example, but is not limited to, the type of needs and the frequency of needs. For example, if the SME has submitted many needs in the past, the needs gathering unit can select a needs-based collection method. Furthermore, if the SME specializes in a specific skill, the needs gathering unit can prioritize collecting needs related to that skill. Furthermore, if the SME has demonstrated leadership in the past, the needs gathering unit can focus on collecting needs related to leadership. In this way, the optimal collection method can be selected by analyzing the SME's past needs history.

[0066] The needs gathering unit can filter needs based on the current projects and areas of interest of the SME when collecting needs. The needs gathering unit can filter needs based on the current projects and areas of interest of the SME when collecting needs. Current projects include, but are not limited to, ongoing projects and areas of interest, for example. The needs gathering unit can, for example, prioritize collecting needs related to projects currently underway by the SME. The needs gathering unit can also filter and collect needs related to areas of interest of the SME. The needs gathering unit can also collect needs based on areas in which the SME has shown interest in the past. In this way, by filtering needs based on the current projects and areas of interest of the SME, more relevant needs can be collected.

[0067] When collecting needs, the needs gathering unit can prioritize collecting highly relevant needs by taking into account the geographical location information of the SME. When collecting needs, the needs gathering unit prioritize collecting highly relevant needs by taking into account the geographical location information of the SME. Geographical location information includes, but is not limited to, GPS data and address information. For example, if the SME is active in a specific area, the needs gathering unit can prioritize collecting needs related to that area. Furthermore, when the SME is on a business trip, the needs gathering unit can prioritize collecting needs related to the business trip destination. Furthermore, when the SME is working remotely, the needs gathering unit can prioritize collecting needs related to remote work. In this way, by taking into account the geographical location information of the SME, more relevant needs can be collected.

[0068] The needs gathering unit can analyze the social media activity of the SME when gathering needs and collect related needs. The needs gathering unit can analyze the social media activity of the SME when gathering needs and collect related needs. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. For example, the needs gathering unit can collect needs related to projects shared by the SME on social media. The needs gathering unit can also collect needs related to skills mentioned by the SME on social media. The needs gathering unit can also collect needs related to industries followed by the SME on social media. This makes it possible to gather more relevant needs by analyzing the social media activity of the SME.

[0069] When linking data, the linking unit can improve the accuracy of the linking by taking into account the interrelationships between employee data and small and medium-sized enterprise data. When linking data, the linking unit improves the accuracy of the linking by taking into account the interrelationships between employee data and small and medium-sized enterprise data. Interrelationships include, for example, data relevance and business relevance, but are not limited to these examples. For example, the linking unit can link data by taking into account the interrelationships between employee skills and the needs of small and medium-sized enterprises. The linking unit can also link data by taking into account the interrelationships between employee experience and small and medium-sized enterprise projects. The linking unit can also link data by taking into account the interrelationships between employee requests and the terms of service provided by small and medium-sized enterprises. In this way, by taking into account the interrelationships between employee data and small and medium-sized enterprise data, more accurate data linking is possible.

[0070] When linking data, the linking unit can apply different linking algorithms depending on the categories of employee data and small and medium-sized business data. When linking data, the linking unit applies different linking algorithms depending on the categories of employee data and small and medium-sized business data. Categories include, for example, technical categories and business categories, but are not limited to these examples. For example, the linking unit can apply a linking algorithm dedicated to technical skills to data related to technical skills. The linking unit can also apply a linking algorithm dedicated to management skills to data related to management skills. The linking unit can also apply a linking algorithm dedicated to soft skills to data related to soft skills. This enables more appropriate data linking by applying different linking algorithms depending on the categories of employee data and small and medium-sized business data.

[0071] When linking data, the linking unit can take into account the geographical distribution of employee data and small and medium-sized enterprise data. When linking data, the linking unit can take into account the geographical distribution of employee data and small and medium-sized enterprise data. Geographical distribution includes, but is not limited to, data by region or country. For example, if an employee works in a specific region, the linking unit can prioritize linking small and medium-sized enterprise data related to that region. Furthermore, if an employee is able to travel on business, the linking unit can also link small and medium-sized enterprise data related to the business trip destination. Furthermore, if an employee wishes to work remotely, the linking unit can also link data on small and medium-sized enterprises that allow remote work. This enables more appropriate data linking by taking into account the geographical distribution of employee data and small and medium-sized enterprise data.

[0072] When linking data, the linking unit can improve the accuracy of the linking by referring to literature related to employee data and small and medium-sized enterprise data. When linking data, the linking unit can improve the accuracy of the linking by referring to literature related to employee data and small and medium-sized enterprise data. Related literature includes, for example, academic papers and technical reports, but is not limited to these examples. For example, the linking unit can link data by referring to literature related to employee skills. The linking unit can also link data by referring to literature related to employee experience. The linking unit can also link data by referring to literature related to employee requests. In this way, by referring to literature related to employee data and small and medium-sized enterprise data, more accurate data linking is possible.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The matching system may further include a feedback collection unit. The feedback collection unit collects user feedback on the matching results. For example, it may collect the level of satisfaction with the matching and areas for improvement from both the matched employees and the SMEs. The feedback collection unit may also provide the collected feedback to the analysis unit, which may then improve the matching algorithm based on the feedback. This allows the matching system to continuously improve its accuracy by reflecting user feedback.

[0075] The matching system can further include a training department. The training department implements training programs to provide matched employees with the necessary skills and knowledge. For example, it can provide training on specific technical skills or business processes tailored to the needs of SMEs. The training department can also monitor the progress of the training and provide additional support as needed. This can help matched employees quickly adapt to working at SMEs.

[0076] The matching system can further include an evaluation unit, which evaluates the performance of matched employees. For example, the evaluation unit can evaluate the employee's work results and project progress and provide the results to the SME. The evaluation unit can also suggest skills development and career paths for employees based on the evaluation results. In this way, the matching system can support employee growth and improve the project success rate of SMEs.

[0077] The matching system can further include a compensation management unit. The compensation management unit manages the compensation and incentives of matched employees. For example, it can calculate compensation and bonuses based on employees' work performance and provide them to small and medium-sized enterprises. The compensation management unit can also collect feedback on compensation and use it to improve the compensation system. In this way, the matching system can increase employee motivation and help improve the performance of small and medium-sized enterprises.

[0078] The matching system may further include a communication support unit. The communication support unit supports communication between matched employees and SMEs. For example, the communication support unit may coordinate the schedule of online meetings and provide communication tools. The communication support unit may also provide training and workshops to facilitate communication. In this way, the matching system can promote effective communication between employees and SMEs and support the success of the project.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The collection department collects employee data. Employee data includes personal information, skill information, and work history. The collection department can collect employee skills, experience, desired work, and requests. For example, employee requests can be collected through questionnaires or interviews. It can also collect the needs of small and medium-sized enterprises, such as project requirements and resource needs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the analysis algorithm to be used and the purpose of the analysis. For example, skill matching criteria or evaluation based on years of experience can be used. Different analysis algorithms can also be applied depending on the category of employee data. For example, an analysis algorithm specifically for technical skills can be applied to data related to technical skills. Step 3: The matching unit performs matching based on the analysis results obtained by the analysis unit. Matching is performed based on the matching evaluation criteria and the algorithm used. For example, it can match employee requests with the needs of small and medium-sized enterprises. It can also improve the accuracy of matching by taking into account the correlations between employee data. For example, matching is performed by taking into account the correlations between employee skills and the needs of small and medium-sized enterprises. Step 4: The providing unit provides the matching results obtained by the matching unit. The results are provided based on the means and timing of provision. For example, the matching results can be provided through an API. The providing unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, if the user is nervous, simple, highly visible information is provided.

[0081] (Example 2) A matching system according to an embodiment of the present invention uses a generation AI to match employees of small and medium-sized enterprises with those of large enterprises. This matching system digitizes the large enterprise's company data, employee data, and employee requirements, and digitizes the SME's company data and desired talent data. These data are linked via a generation AI and an API. The generation AI analyzes the large enterprise's employee data and the SME's desired talent data to perform optimal matching. For example, it matches an SME seeking senior employees with specific skills with employees from the large enterprise who possess those skills. The matching results are provided to both companies via an API. This allows large enterprises to reduce labor costs and monetize their businesses. Meanwhile, SMEs can resolve their talent shortages by hiring talent from large enterprises. For example, the matching system can perform a detailed analysis of employees' skills, experience, and requirements to propose talent optimally suited to the SME's needs. Furthermore, by linking data in real time through an API, matching based on the latest information is always possible. This allows the matching system to simultaneously address the SME's talent shortage and revitalize senior employees at large enterprises. For example, it can perform a detailed analysis of employees' skills, experience, and requirements to propose talent optimally suited to the SME's needs. In addition, by sharing data in real time through APIs, matching can always be based on the latest information.

[0082] The matching system according to the embodiment includes a collection unit, an analysis unit, a matching unit, and a providing unit. The collection unit collects employee data. The employee data includes, for example, personal information, skill information, and work history, but is not limited to these examples. The collection unit collects, for example, employee skills, experience, and desired work content. The collection unit can also collect employee requests. For example, the employee requests can be collected through questionnaires or interviews. The collection unit can also collect the needs of small and medium-sized enterprises. For example, the collection unit can collect the company's project requirements and resource needs. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, an analysis algorithm to be used and a purpose of the analysis, but is not limited to these examples. For example, the analysis unit can perform an evaluation based on skill matching criteria and years of experience. The analysis unit can also apply different analysis algorithms depending on the category of employee data. For example, a dedicated analysis algorithm for technical skills can be applied to data related to technical skills. The matching unit performs matching based on the analysis results obtained by the analysis unit. The matching is performed based on, for example, a matching evaluation criterion and an algorithm to be used, but is not limited to these examples. For example, the matching unit can match employee requests with the needs of small and medium-sized enterprises. The matching unit can also improve the accuracy of matching by taking into account the interrelationships between employee data. For example, matching can be performed by taking into account the interrelationships between employee skills and the needs of small and medium-sized enterprises. The providing unit provides the matching results obtained by the matching unit. The provision is performed, for example, based on the provision means and the provision timing, but is not limited to such examples. For example, the providing unit can provide the matching results through an API. The providing unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, if the user is nervous, simple, highly visible information can be provided. This allows the matching system according to the embodiment to efficiently collect, analyze, match, and provide employee data.

[0083] The matching system includes a request collection unit that collects requests from employees. The request collection unit collects the requests from employees. The requests include, for example, desired work content and requests for skill improvement, but are not limited to these examples. The request collection unit can collect the requests from employees through, for example, questionnaires or interviews. The request collection unit can also estimate the emotions of employees and adjust the timing of request collection based on the estimated emotions. For example, if a user is feeling stressed, the timing of collection can be delayed so that requests can be collected in a relaxed state. In this way, by collecting employee requests, more appropriate matching can be achieved.

[0084] The matching system includes a needs collection unit that collects the needs of SMEs. The needs collection unit collects the needs of SMEs. The needs include, for example, a company's project requirements and resource needs, but are not limited to such examples. The needs collection unit can collect the needs of SMEs, for example, through questionnaires or interviews. The needs collection unit can also estimate the emotions of the SMEs and adjust the timing of collecting needs based on the estimated emotions. For example, if a user is feeling stressed, the timing of collection can be delayed so that needs can be collected in a relaxed state. This allows for more appropriate matching by collecting the needs of SMEs.

[0085] The matching system includes a linking unit that links data in real time. The linking unit links data in real time. Real-time data linking includes, but is not limited to, for example, the frequency of data updates and delay times. The linking unit can link data, for example, through an API. The linking unit can also estimate a user's emotions and adjust the timing of data linking based on the estimated emotions. For example, if the user is feeling stressed, the timing of data linking can be delayed so that linking can be performed in a relaxed state. By linking data in real time, matching can always be performed based on the latest information.

[0086] The collection unit can collect the skills, experience, and desired work content of employees. The collection unit collects the skills, experience, and desired work content of employees. Skills include, for example, technical skills and soft skills, but are not limited to these examples. For example, the collection unit can collect the technical skills of employees. The collection unit can also collect the soft skills of employees. The collection unit can also collect the work experience of employees. For example, it can collect projects that employees have participated in in the past and the work content. In this way, by collecting detailed information about employees, more accurate matching is possible.

[0087] The analysis unit can perform evaluation based on skill matching criteria and years of experience. The analysis unit performs evaluation based on skill matching criteria and years of experience. Skill matching criteria include, for example, skill level and relevance, but are not limited to these examples. The analysis unit can evaluate the employee's skills based on, for example, skill level. The analysis unit can also perform evaluation based on skill relevance. The analysis unit can also evaluate the employee's experience based on years of experience. For example, evaluation can be performed based on the employee's years of work experience. This enables more appropriate matching by performing evaluation based on skill matching criteria and years of experience.

[0088] The matching department can match the requests of employees with the needs of SMEs. The matching department matches the requests of employees with the needs of SMEs. Requests include, for example, desired work content and desire to improve skills, but are not limited to such examples. Needs include, for example, company project requirements and resource needs, but are not limited to such examples. The matching department can, for example, match the work content desired by employees with the project requirements of SMEs. The matching department can also match the requests of employees to improve skills with the resource needs of SMEs. This allows for more appropriate matching by matching the requests of employees with the needs of SMEs.

[0089] The providing unit can provide the matching results through an API. The providing unit provides the matching results through an API. Examples of APIs include, but are not limited to, a REST API and GraphQL. The providing unit can provide the matching results through, for example, a REST API. The providing unit can also provide the matching results through GraphQL. The providing unit can also estimate the user's emotions and adjust the way in which information is presented based on the estimated emotions. For example, if the user is nervous, simple, highly visible information can be provided. As a result, providing the matching results through an API enables quick and accurate information provision.

[0090] The collection unit can estimate the user's emotions and adjust the timing of collecting employee data based on the estimated user emotions. The collection unit can estimate the user's emotions and adjust the timing of collecting employee data based on the estimated user emotions. Emotions include, but are not limited to, stress and relaxation. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect data in a relaxed state. Furthermore, if the user is relaxed, the collection unit can immediately collect data and efficiently acquire information. Furthermore, if the user is in a hurry, the collection unit can quickly collect data and immediately acquire necessary information. This allows more appropriate data collection by adjusting the collection timing according to the user's emotions.

[0091] The collection unit can analyze the employee's past work history and select a collection method. The collection unit analyzes the employee's past work history and selects a collection method. Past work history includes, but is not limited to, project history and work results, for example. For example, if the employee has participated in many projects in the past, the collection unit can select a project-based data collection method. Furthermore, if the employee specializes in a specific skill, the collection unit can prioritize collecting data related to that skill. Furthermore, if the employee has demonstrated leadership in the past, the collection unit can focus on collecting data related to leadership. In this way, the optimal collection method can be selected by analyzing the employee's past work history.

[0092] When collecting employee data, the collection unit can filter the data based on the employee's current projects and areas of interest. When collecting employee data, the collection unit can filter the data based on the employee's current projects and areas of interest. Current projects include, but are not limited to, ongoing projects and areas of interest, for example. The collection unit can, for example, prioritize collecting data related to the employee's ongoing projects. The collection unit can also filter and collect data related to the employee's areas of interest. The collection unit can also collect data based on areas in which the employee has expressed interest in the past. In this way, by filtering the data based on the employee's current projects and areas of interest, more relevant data can be collected.

[0093] When collecting employee data, the collection unit can select a collection means according to the employee's input method. When collecting employee data, the collection unit selects a collection means according to the employee's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the employee prefers voice input, the collection unit can prioritize collecting voice data. Furthermore, if the employee prefers text input, the collection unit can prioritize collecting text data. Furthermore, if the employee prefers image input, the collection unit can prioritize collecting image data. This enables efficient data collection by selecting the optimal collection means according to the employee's input method.

[0094] The collection unit can estimate the user's emotions and determine the priority of employee data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions and determine the priority of employee data to be collected based on the estimated user emotions. Emotions include, but are not limited to, stress and relaxation. For example, if the user is feeling stressed, the collection unit can postpone collecting less important data. Furthermore, if the user is relaxed, the collection unit can prioritize collecting more important data. Furthermore, if the user is in a hurry, the collection unit can quickly collect the most important data. In this way, by determining the priority of data according to the user's emotions, more important data can be collected preferentially.

[0095] When collecting employee data, the collection unit can prioritize collecting highly relevant data by taking into account the employee's geographical location information. When collecting employee data, the collection unit prioritizes collecting highly relevant data by taking into account the employee's geographical location information. Geographical location information includes, but is not limited to, GPS data and address information. For example, if an employee works in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, if an employee is on a business trip, the collection unit can prioritize collecting data related to the business trip destination. Furthermore, if an employee is working remotely, the collection unit can prioritize collecting data related to the remote work. In this way, by taking into account the employee's geographical location information, more relevant data can be collected.

[0096] The collection unit may analyze the employee's social media activity and collect related data when collecting employee data. The collection unit may analyze the employee's social media activity and collect related data when collecting employee data. Social media activity includes, but is not limited to, the content of posts and the number of followers. For example, the collection unit may collect data related to projects shared by employees on social media. The collection unit may also collect data related to skills mentioned by employees on social media. The collection unit may also collect data related to industries that employees follow on social media. This allows for more relevant data to be collected by analyzing the employee's social media activity.

[0097] The collection unit can customize the collection method by reflecting employees' past feedback when collecting employee data. The collection unit customizes the collection method by reflecting employees' past feedback when collecting employee data. Past feedback includes, but is not limited to, survey results and evaluation comments, for example. The collection unit can adjust the collection method based on feedback provided by employees in the past. The collection unit can also prioritize collection methods that employees have preferred in the past. The collection unit can also avoid collection methods that employees have expressed dissatisfaction with in the past. In this way, a more appropriate collection method can be selected by reflecting employees' past feedback.

[0098] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. Emotions include, but are not limited to, tension and relaxation. For example, if the user is tensioned, the analysis unit can provide a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary analysis result. In this way, by adjusting the way the analysis is presented based on the user's emotions, more appropriate analysis results can be provided.

[0099] The analysis unit can adjust the level of detail of the analysis based on the importance of the employee data during analysis. The analysis unit adjusts the level of detail of the analysis based on the importance of the employee data during analysis. Importance includes, but is not limited to, for example, the importance of the job or the importance of the project. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also perform an analysis with an appropriate level of detail on data with medium importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the employee data.

[0100] During analysis, the analysis unit can apply different analysis algorithms depending on the category of employee data. During analysis, the analysis unit applies different analysis algorithms depending on the category of employee data. Categories include, for example, technical categories and business categories, but are not limited to these examples. For example, the analysis unit can apply an analysis algorithm dedicated to technical skills to data related to technical skills. Furthermore, the analysis unit can also apply an analysis algorithm dedicated to management skills to data related to management skills. Furthermore, the analysis unit can apply an analysis algorithm dedicated to soft skills to data related to soft skills. In this way, by applying different analysis algorithms depending on the category of employee data, more accurate analysis is possible.

[0101] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. Past analysis results include, for example, past project data and business results, but are not limited to these examples. For example, the analysis unit can correct the current analysis results based on the employee's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the employee's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the employee's past analysis results. In this way, the accuracy of the analysis is improved by referring to the employee's past analysis results.

[0102] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. Emotions include, but are not limited to, tension and relaxation. For example, if the user is tension, the analysis unit can provide a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a shortened analysis result that can be quickly understood. In this way, by adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided.

[0103] During analysis, the analysis unit can determine the analysis priority based on the time of submission of employee data. During analysis, the analysis unit determines the analysis priority based on the time of submission of employee data. The submission time includes, for example, the submission date and the submission frequency, but is not limited to these examples. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also postpone data that was submitted earlier. The analysis unit can also appropriately prioritize data that was submitted at a medium time. In this way, by determining the analysis priority based on the time of submission of employee data, efficient analysis is possible.

[0104] The analysis unit can adjust the order of analysis based on the relevance of the employee data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the employee data during analysis. Relevance includes, for example, the degree of data relevance and the relevance of work, but is not limited to these examples. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can also postpone the analysis of data with low relevance. The analysis unit can also appropriately prioritize data with medium relevance. In this way, by adjusting the order of analysis based on the relevance of the employee data, efficient analysis is possible.

[0105] The analysis unit can adjust the use of technical terminology during analysis according to the employee's level of expertise. The analysis unit can adjust the use of technical terminology during analysis according to the employee's level of expertise. Expertise level includes, but is not limited to, technical skill level and business knowledge level, for example. The analysis unit can provide, for example, analysis results that use a lot of technical terminology to employees with high expertise. The analysis unit can also provide analysis results that avoid technical terminology to employees with low expertise. The analysis unit can also provide analysis results that use a moderate amount of technical terminology to employees with intermediate expertise. In this way, by adjusting the use of technical terminology during analysis according to the employee's level of expertise, more appropriate analysis results can be provided.

[0106] The matching unit can estimate the user's emotion and adjust the matching criteria based on the estimated user's emotion. The matching unit can estimate the user's emotion and adjust the matching criteria based on the estimated user's emotion. Emotions include, but are not limited to, tension and relaxation. For example, when the user is tensioned, the matching unit can use simple and easy-to-understand matching criteria. Furthermore, when the user is relaxed, the matching unit can use detailed matching criteria. Furthermore, when the user is in a hurry, the matching unit can use criteria for quick matching. In this way, by adjusting the matching criteria according to the user's emotion, more appropriate matching is possible.

[0107] The matching unit can improve the accuracy of matching by taking into account the interrelationships of employee data when matching. The matching unit improves the accuracy of matching by taking into account the interrelationships of employee data when matching. Interrelationships include, for example, data relevance and business relevance, but are not limited to these examples. The matching unit can, for example, perform matching by taking into account the interrelationships between employee skills and the needs of SMEs. The matching unit can also perform matching by taking into account the interrelationships between employee experience and SME projects. The matching unit can also perform matching by taking into account the interrelationships between employee requests and SME provision conditions. In this way, by taking into account the interrelationships of employee data, more accurate matching is possible.

[0108] The matching unit can perform matching taking into consideration employee attribute information when matching. The matching unit can perform matching taking into consideration employee attribute information when matching. Attribute information includes, for example, age, gender, and occupation, but is not limited to these examples. The matching unit can perform matching taking into consideration, for example, the employee's age and gender. The matching unit can also perform matching taking into consideration the employee's work location and work style. The matching unit can also perform matching taking into consideration the employee's work history and skill set. In this way, more appropriate matching is possible by taking into consideration employee attribute information.

[0109] The matching unit can weight the matching based on the employee's submission frequency during matching. The matching unit weights the matching based on the employee's submission frequency during matching. Submission frequency includes, but is not limited to, for example, the number of submissions and the submission interval. For example, the matching unit can prioritize matching for employees with a high submission frequency. The matching unit can also postpone employees with a low submission frequency. The matching unit can also moderately prioritize employees with a medium submission frequency. In this way, weighting the matching based on the employee's submission frequency enables more appropriate matching.

[0110] The matching unit can estimate the user's emotion and adjust the order in which the matching results are displayed based on the estimated user's emotion. The matching unit can estimate the user's emotion and adjust the order in which the matching results are displayed based on the estimated user's emotion. Emotions include, but are not limited to, tension and relaxation. For example, if the user is tensioned, the matching unit can display the most important results first. Also, if the user is relaxed, the matching unit can sequentially display detailed results. Also, if the user is in a hurry, the matching unit can first display results that are concise so that the user can understand quickly. In this way, by adjusting the order in which the matching results are displayed based on the user's emotion, more appropriate information can be provided.

[0111] The matching unit can perform matching taking into account the geographic distribution of employee data when performing matching. The matching unit performs matching taking into account the geographic distribution of employee data when performing matching. Geographic distribution includes, for example, data by region or by country, but is not limited to these examples. For example, if an employee works in a specific region, the matching unit can prioritize matching with small and medium-sized enterprises related to that region. Furthermore, if an employee is able to travel on business, the matching unit can also match with small and medium-sized enterprises related to the business trip destination. Furthermore, if an employee desires remote work, the matching unit can also match with small and medium-sized enterprises that allow remote work. This enables more appropriate matching by taking into account the geographic distribution of employee data.

[0112] The matching unit can improve the accuracy of matching by referring to literature related to the employee data when matching. The matching unit can improve the accuracy of matching by referring to literature related to the employee data when matching. Related literature includes, for example, academic papers and technical reports, but is not limited to these examples. For example, the matching unit can perform matching by referring to literature related to the employee's skills. The matching unit can also perform matching by referring to literature related to the employee's experience. The matching unit can also perform matching by referring to literature related to the employee's requests. In this way, by referring to literature related to the employee data, more accurate matching is possible.

[0113] The matching unit can perform matching taking into account the market value of the employee data when matching. The matching unit performs matching taking into account the market value of the employee data when matching. Market value includes, for example, salary level and supply and demand balance, but is not limited to these examples. For example, if the market value of an employee is high, the matching unit can prioritize matching with small and medium-sized enterprises that match that value. Furthermore, if the market value of an employee is low, the matching unit can also match with small and medium-sized enterprises that match that value. Furthermore, if the market value of an employee is medium, the matching unit can appropriately match with small and medium-sized enterprises that match that value. In this way, more appropriate matching is possible by taking into account the market value of the employee data.

[0114] The providing unit can estimate the user's emotion and adjust the manner in which information is presented based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the manner in which information is presented based on the estimated user's emotion. Emotions include, but are not limited to, tension and relaxation, for example. For example, when the user is tensioned, the providing unit can provide simple, highly visible information. Furthermore, when the user is relaxed, the providing unit can provide detailed information. Furthermore, when the user is in a hurry, the providing unit can provide information that focuses on the main points. This makes it possible to provide more appropriate information by adjusting the manner in which information is presented according to the user's emotion.

[0115] The providing unit can adjust the level of detail to be provided based on the importance of the employee data when providing the data. The providing unit adjusts the level of detail to be provided based on the importance of the employee data when providing the data. Importance includes, for example, the importance of the job or the importance of the project, but is not limited to these examples. For example, the providing unit can provide detailed information for data with high importance. The providing unit can also provide simplified information for data with low importance. The providing unit can also provide information with an appropriate level of detail for data with medium importance. In this way, by adjusting the level of detail to be provided based on the importance of the employee data, efficient information provision is possible.

[0116] The providing unit can apply different providing algorithms depending on the category of employee data when providing the data. The providing unit applies different providing algorithms depending on the category of employee data when providing the data. Categories include, for example, technical categories and business categories, but are not limited to these examples. For example, the providing unit can apply a providing algorithm dedicated to technical skills to data related to technical skills. Furthermore, the providing unit can also apply a providing algorithm dedicated to management skills to data related to management skills. Furthermore, the providing unit can also apply a providing algorithm dedicated to soft skills to data related to soft skills. In this way, by applying different providing algorithms depending on the category of employee data, more appropriate information can be provided.

[0117] The providing unit can improve the accuracy of the data provided by referring to the employee's past provided results when providing the data. The providing unit can improve the accuracy of the data provided by referring to the employee's past provided results when providing the data. Past provided results include, but are not limited to, past project data and business results. The providing unit can correct the current provided results, for example, based on the employee's past provided results. The providing unit can also adjust the providing algorithm by referring to the employee's past provided results. The providing unit can also improve the accuracy of the data provided by referring to the employee's past provided results. In this way, the accuracy of the data provided is improved by referring to the employee's past provided results.

[0118] The providing unit can estimate the user's emotion and adjust the length of information to be provided based on the estimated user's emotion. The providing unit can estimate the user's emotion and adjust the length of information to be provided based on the estimated user's emotion. Emotions include, but are not limited to, tension and relaxation, for example. For example, if the user is tensioned, the providing unit can provide short, to-the-point information. Furthermore, if the user is relaxed, the providing unit can provide detailed information. Furthermore, if the user is in a hurry, the providing unit can provide shortened information that can be quickly understood. In this way, by adjusting the length of information according to the user's emotion, more appropriate information can be provided.

[0119] The providing unit can determine the priority of provision based on the time of submission of employee data at the time of provision. The providing unit can determine the priority of provision based on the time of submission of employee data at the time of provision. The submission time includes, for example, the submission date and the submission frequency, but is not limited to these examples. For example, the providing unit can provide the most recent data preferentially. The providing unit can also postpone data that was submitted earlier. The providing unit can also appropriately prioritize data that was submitted at a medium time. In this way, by determining the priority of provision based on the time of submission of employee data, efficient information provision is possible.

[0120] The providing unit can adjust the order of providing the employee data based on the relevance of the employee data when providing the data. The providing unit can adjust the order of providing the employee data based on the relevance of the employee data when providing the data. Relevance includes, for example, the degree of relevance of the data and the relevance of the work, but is not limited to such examples. For example, the providing unit can prioritize providing highly relevant data. The providing unit can also postpone data with low relevance. The providing unit can also appropriately prioritize data with medium relevance. In this way, by adjusting the order of providing the employee data based on the relevance of the employee data, efficient information provision is possible.

[0121] The provision unit can adjust the use of technical terminology when providing the information according to the employee's level of expertise. The provision unit can adjust the use of technical terminology when providing the information according to the employee's level of expertise. Expertise level includes, but is not limited to, technical skill level and business knowledge level, for example. The provision unit can provide information that uses a lot of technical terminology to employees with high expertise. The provision unit can also provide information that avoids technical terminology to employees with low expertise. The provision unit can also provide information that uses a moderate amount of technical terminology to employees with intermediate expertise. In this way, by adjusting the use of technical terminology when providing the information according to the employee's level of expertise, more appropriate information can be provided.

[0122] The request collection unit can estimate the user's emotions and adjust the timing of request collection based on the estimated user emotions. The request collection unit can estimate the user's emotions and adjust the timing of request collection based on the estimated user emotions. Emotions include, but are not limited to, stress and relaxation. For example, when the user is feeling stressed, the request collection unit can delay the collection timing and collect requests in a relaxed state. Furthermore, when the user is relaxed, the request collection unit can collect requests immediately and acquire information efficiently. Furthermore, when the user is in a hurry, the request collection unit can quickly collect requests and acquire necessary information immediately. This enables more appropriate request collection by adjusting the timing of request collection according to the user's emotions.

[0123] When collecting requests, the request collection unit can analyze the employee's past request history and select the optimal collection method. When collecting requests, the request collection unit analyzes the employee's past request history and selects the optimal collection method. The past request history includes, for example, but is not limited to, the type of request and the frequency of requests. For example, if the employee has submitted many requests in the past, the request collection unit can select a request-based collection method. Furthermore, if the employee specializes in a specific skill, the request collection unit can prioritize collecting requests related to that skill. Furthermore, if the employee has demonstrated leadership in the past, the request collection unit can focus on collecting requests related to leadership. In this way, the optimal collection method can be selected by analyzing the employee's past request history.

[0124] The request collection unit can filter requests based on the employee's current projects and areas of interest when collecting requests. The request collection unit can filter requests based on the employee's current projects and areas of interest when collecting requests. Current projects include, but are not limited to, ongoing projects and areas of interest, for example. The request collection unit can, for example, preferentially collect requests related to the employee's ongoing projects. The request collection unit can also filter and collect requests related to areas of interest to the employee. The request collection unit can also collect requests based on areas in which the employee has shown interest in the past. In this way, by filtering requests based on the employee's current projects and areas of interest, more relevant requests can be collected.

[0125] The request collection unit can estimate the user's emotions and determine the priority of requests to be collected based on the estimated user emotions. The request collection unit can estimate the user's emotions and determine the priority of requests to be collected based on the estimated user emotions. Emotions include, but are not limited to, stress and relaxation. For example, when the user is feeling stressed, the request collection unit can postpone requests of low importance. Furthermore, when the user is relaxed, the request collection unit can also prioritize collecting requests of high importance. Furthermore, when the user is in a hurry, the request collection unit can quickly collect the most important requests. In this way, by determining the priority of requests according to the user's emotions, more important requests can be collected preferentially.

[0126] When collecting requests, the request collection unit can prioritize collecting highly relevant requests by taking into account the geographical location information of employees. When collecting requests, the request collection unit prioritizes collecting highly relevant requests by taking into account the geographical location information of employees. Geographical location information includes, but is not limited to, GPS data and address information, for example. For example, if an employee works in a specific area, the request collection unit can prioritize collecting requests related to that area. Furthermore, if an employee is on a business trip, the request collection unit can prioritize collecting requests related to the business trip destination. Furthermore, if an employee is working remotely, the request collection unit can prioritize collecting requests related to the remote work. In this way, by taking into account the geographical location information of employees, more relevant requests can be collected.

[0127] The request collection unit can analyze the employee's social media activity during request collection to collect related requests. The request collection unit can analyze the employee's social media activity during request collection to collect related requests. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. For example, the request collection unit can collect requests related to projects shared by employees on social media. The request collection unit can also collect requests related to skills mentioned by employees on social media. The request collection unit can also collect requests related to industries that employees follow on social media. This makes it possible to collect more relevant requests by analyzing the employee's social media activity.

[0128] The needs collection unit can estimate the user's emotions and adjust the timing of collecting needs based on the estimated user emotions. The needs collection unit can estimate the user's emotions and adjust the timing of collecting needs based on the estimated user emotions. Emotions include, but are not limited to, stress and relaxation. For example, if the user is feeling stressed, the needs collection unit can delay the collection timing and collect needs in a relaxed state. Furthermore, if the user is relaxed, the needs collection unit can instantly collect needs and efficiently acquire information. Furthermore, if the user is in a hurry, the needs collection unit can quickly collect needs and instantly acquire necessary information. This makes it possible to collect needs more appropriately by adjusting the timing of collecting needs according to the user's emotions.

[0129] When collecting needs, the needs gathering unit can analyze the SME's past needs history and select the optimal collection method. When collecting needs, the needs gathering unit analyzes the SME's past needs history and select the optimal collection method. Past needs history includes, for example, but is not limited to, the type of needs and the frequency of needs. For example, if the SME has submitted many needs in the past, the needs gathering unit can select a needs-based collection method. Furthermore, if the SME specializes in a specific skill, the needs gathering unit can prioritize collecting needs related to that skill. Furthermore, if the SME has demonstrated leadership in the past, the needs gathering unit can focus on collecting needs related to leadership. In this way, the optimal collection method can be selected by analyzing the SME's past needs history.

[0130] The needs gathering unit can filter needs based on the current projects and areas of interest of the SME when collecting needs. The needs gathering unit can filter needs based on the current projects and areas of interest of the SME when collecting needs. Current projects include, but are not limited to, ongoing projects and areas of interest, for example. The needs gathering unit can, for example, prioritize collecting needs related to projects currently underway by the SME. The needs gathering unit can also filter and collect needs related to areas of interest of the SME. The needs gathering unit can also collect needs based on areas in which the SME has shown interest in the past. In this way, by filtering needs based on the current projects and areas of interest of the SME, more relevant needs can be collected.

[0131] The need collection unit can estimate the user's emotions and determine the priority of needs to be collected based on the estimated user emotions. The need collection unit can estimate the user's emotions and determine the priority of needs to be collected based on the estimated user emotions. Emotions include, but are not limited to, stress and relaxation, for example. For example, when the user is feeling stressed, the need collection unit can postpone less important needs. Furthermore, when the user is relaxed, the need collection unit can also prioritize collecting more important needs. Furthermore, when the user is in a hurry, the need collection unit can quickly collect the most important needs. In this way, by determining the priority of needs according to the user's emotions, more important needs can be collected preferentially.

[0132] When collecting needs, the needs gathering unit can prioritize collecting highly relevant needs by taking into account the geographical location information of the SME. When collecting needs, the needs gathering unit prioritize collecting highly relevant needs by taking into account the geographical location information of the SME. Geographical location information includes, but is not limited to, GPS data and address information. For example, if the SME is active in a specific area, the needs gathering unit can prioritize collecting needs related to that area. Furthermore, when the SME is on a business trip, the needs gathering unit can prioritize collecting needs related to the business trip destination. Furthermore, when the SME is working remotely, the needs gathering unit can prioritize collecting needs related to remote work. In this way, by taking into account the geographical location information of the SME, more relevant needs can be collected.

[0133] The needs gathering unit can analyze the social media activity of the SME when gathering needs and collect related needs. The needs gathering unit can analyze the social media activity of the SME when gathering needs and collect related needs. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. For example, the needs gathering unit can collect needs related to projects shared by the SME on social media. The needs gathering unit can also collect needs related to skills mentioned by the SME on social media. The needs gathering unit can also collect needs related to industries followed by the SME on social media. This makes it possible to gather more relevant needs by analyzing the social media activity of the SME.

[0134] The linking unit can estimate the user's emotion and adjust the timing of data linking based on the estimated user's emotion. The linking unit can estimate the user's emotion and adjust the timing of data linking based on the estimated user's emotion. Emotions include, but are not limited to, stress and relaxation. For example, if the user is feeling stressed, the linking unit can delay the timing of data linking and perform linking in a relaxed state. Furthermore, if the user is relaxed, the linking unit can immediately perform data linking and efficiently acquire information. Furthermore, if the user is in a hurry, the linking unit can quickly perform data linking and immediately acquire necessary information. This allows more appropriate data linking by adjusting the timing of data linking according to the user's emotion.

[0135] When linking data, the linking unit can improve the accuracy of the linking by taking into account the interrelationships between employee data and small and medium-sized enterprise data. When linking data, the linking unit improves the accuracy of the linking by taking into account the interrelationships between employee data and small and medium-sized enterprise data. Interrelationships include, for example, data relevance and business relevance, but are not limited to these examples. For example, the linking unit can link data by taking into account the interrelationships between employee skills and the needs of small and medium-sized enterprises. The linking unit can also link data by taking into account the interrelationships between employee experience and small and medium-sized enterprise projects. The linking unit can also link data by taking into account the interrelationships between employee requests and the terms of service provided by small and medium-sized enterprises. In this way, by taking into account the interrelationships between employee data and small and medium-sized enterprise data, more accurate data linking is possible.

[0136] When linking data, the linking unit can apply different linking algorithms depending on the categories of employee data and small and medium-sized business data. When linking data, the linking unit applies different linking algorithms depending on the categories of employee data and small and medium-sized business data. Categories include, for example, technical categories and business categories, but are not limited to these examples. For example, the linking unit can apply a linking algorithm dedicated to technical skills to data related to technical skills. The linking unit can also apply a linking algorithm dedicated to management skills to data related to management skills. The linking unit can also apply a linking algorithm dedicated to soft skills to data related to soft skills. This enables more appropriate data linking by applying different linking algorithms depending on the categories of employee data and small and medium-sized business data.

[0137] The linking unit can estimate the user's emotions and determine the priority of data linking based on the estimated user's emotions. The linking unit can estimate the user's emotions and determine the priority of data linking based on the estimated user's emotions. Emotions include, but are not limited to, stress and relaxation. For example, when the user is feeling stressed, the linking unit can postpone data linking of less importance. Furthermore, when the user is relaxed, the linking unit can prioritize data linking of more importance. Furthermore, when the user is in a hurry, the linking unit can quickly link the most important data. In this way, by determining the priority of data linking according to the user's emotions, more important data can be linked preferentially.

[0138] When linking data, the linking unit can take into account the geographical distribution of employee data and small and medium-sized enterprise data. When linking data, the linking unit can take into account the geographical distribution of employee data and small and medium-sized enterprise data. Geographical distribution includes, but is not limited to, data by region or country. For example, if an employee works in a specific region, the linking unit can prioritize linking small and medium-sized enterprise data related to that region. Furthermore, if an employee is able to travel on business, the linking unit can also link small and medium-sized enterprise data related to the business trip destination. Furthermore, if an employee wishes to work remotely, the linking unit can also link data on small and medium-sized enterprises that allow remote work. This enables more appropriate data linking by taking into account the geographical distribution of employee data and small and medium-sized enterprise data.

[0139] When linking data, the linking unit can improve the accuracy of the linking by referring to literature related to employee data and small and medium-sized enterprise data. When linking data, the linking unit can improve the accuracy of the linking by referring to literature related to employee data and small and medium-sized enterprise data. Related literature includes, for example, academic papers and technical reports, but is not limited to these examples. For example, the linking unit can link data by referring to literature related to employee skills. The linking unit can also link data by referring to literature related to employee experience. The linking unit can also link data by referring to literature related to employee requests. In this way, by referring to literature related to employee data and small and medium-sized enterprise data, more accurate data linking is possible. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and providing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects employee data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs optimal matching based on the analysis results. The providing unit is realized, for example, by the control unit 46A of the smart device 14 and provides the matching results via an API. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, matching unit, and providing unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects employee data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs optimal matching based on the analysis results. The providing unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the matching results via an API. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and provision unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects employee data using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs optimal matching based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the matching results via an API. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, matching unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects employee data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and performs optimal matching based on the analysis results. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the matching results via an API.

[0140] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0141] The matching system may further include a feedback collection unit. The feedback collection unit collects user feedback on the matching results. For example, it may collect the level of satisfaction with the matching and areas for improvement from both the matched employees and the SMEs. The feedback collection unit may also provide the collected feedback to the analysis unit, which may then improve the matching algorithm based on the feedback. This allows the matching system to continuously improve its accuracy by reflecting user feedback.

[0142] The matching system can further include a training department. The training department implements training programs to provide matched employees with the necessary skills and knowledge. For example, it can provide training on specific technical skills or business processes tailored to the needs of SMEs. The training department can also monitor the progress of the training and provide additional support as needed. This can help matched employees quickly adapt to working at SMEs.

[0143] The matching system can further include an evaluation unit, which evaluates the performance of matched employees. For example, the evaluation unit can evaluate the employee's work results and project progress and provide the results to the SME. The evaluation unit can also suggest skills development and career paths for employees based on the evaluation results. In this way, the matching system can support employee growth and improve the project success rate of SMEs.

[0144] The matching system can further include a compensation management unit. The compensation management unit manages the compensation and incentives of matched employees. For example, it can calculate compensation and bonuses based on employees' work performance and provide them to small and medium-sized enterprises. The compensation management unit can also collect feedback on compensation and use it to improve the compensation system. In this way, the matching system can increase employee motivation and help improve the performance of small and medium-sized enterprises.

[0145] The matching system may further include a communication support unit. The communication support unit supports communication between matched employees and SMEs. For example, the communication support unit may coordinate the schedule of online meetings and provide communication tools. The communication support unit may also provide training and workshops to facilitate communication. In this way, the matching system can promote effective communication between employees and SMEs and support the success of the project.

[0146] The matching system can further include a sentiment analysis unit. The sentiment analysis unit analyzes the emotions of employees and SMEs in real time and provides the results to the matching unit. For example, if an employee is feeling stressed, the sentiment analysis unit provides that information to the matching unit, which can then perform matching that reduces stress. The sentiment analysis unit can also make suggestions to improve employee motivation based on the emotional data. This allows the matching system to achieve matching that takes into account the emotions of employees and SMEs, and provide better matching results.

[0147] The matching system can further include a stress management unit. The stress management unit monitors employees' stress levels and provides support to reduce stress. For example, it can provide relaxation programs and counseling to employees with high stress levels. The stress management unit can also provide feedback to the matching unit based on stress data to perform matching that reduces stress. This allows the matching system to reduce employee stress and provide better matching results.

[0148] The matching system can further include a motivation improvement unit. The motivation improvement unit implements measures to improve employee motivation. For example, it can provide incentive programs based on employee goal setting and achievement. The motivation improvement unit can also make customized suggestions for improving motivation based on employee emotional data. This allows the matching system to increase employee motivation and improve the project success rate of small and medium-sized enterprises.

[0149] The matching system may further include an emotion feedback unit. The emotion feedback unit collects the emotions of employees and SMEs regarding the matching results and provides the results to the analysis unit. For example, the emotion feedback unit may collect whether the matched employees are satisfied as emotion data, and the analysis unit may use the data to improve the matching algorithm. The emotion feedback unit may also make suggestions to reduce employee stress and dissatisfaction based on the emotion data. This allows the matching system to continuously improve its accuracy by utilizing emotion data.

[0150] The matching system can further include an emotional support unit. The emotional support unit monitors employees' emotions in real time and provides support as needed. For example, if an employee is feeling stressed, the emotional support unit can provide a relaxation program or counseling. The emotional support unit can also suggest measures to improve employee motivation based on the emotional data. This allows the matching system to provide support that takes employees' emotions into consideration and achieve better matching results.

[0151] The processing flow of the second embodiment will be briefly explained below.

[0152] Step 1: The collection department collects employee data. Employee data includes personal information, skill information, and work history. The collection department can collect employee skills, experience, desired work, and requests. For example, employee requests can be collected through questionnaires or interviews. It can also collect the needs of small and medium-sized enterprises, such as project requirements and resource needs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the analysis algorithm to be used and the purpose of the analysis. For example, skill matching criteria or evaluation based on years of experience can be used. Different analysis algorithms can also be applied depending on the category of employee data. For example, an analysis algorithm specifically for technical skills can be applied to data related to technical skills. Step 3: The matching unit performs matching based on the analysis results obtained by the analysis unit. Matching is performed based on the matching evaluation criteria and the algorithm used. For example, it can match employee requests with the needs of small and medium-sized enterprises. It can also improve the accuracy of matching by taking into account the correlations between employee data. For example, matching is performed by taking into account the correlations between employee skills and the needs of small and medium-sized enterprises. Step 4: The providing unit provides the matching results obtained by the matching unit. The results are provided based on the means and timing of provision. For example, the matching results can be provided through an API. The providing unit can also estimate the user's emotions and adjust the way the information is presented based on the estimated user emotions. For example, if the user is nervous, simple, highly visible information is provided.

[0153] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0154] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0174] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0175] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0176] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0177] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0178] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0179] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0180] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0181] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0184] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0185] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0186] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0187] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0190] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0191] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0192] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0193] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0194] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0195] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0196] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0197] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0198] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0199] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0200] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0201] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0202] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0203] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0204] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0205] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0206] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0207] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0208] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0209] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0210] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0211] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0212] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0213] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0214] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0215] 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.

[0216] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0217] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0218] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0219] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0220] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0221] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0222] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0223] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0224] [Explanation of symbols]

[0225] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects employee data; an analysis unit that analyzes the data collected by the collection unit; a matching unit that performs matching based on the analysis results obtained by the analysis unit; a providing unit that provides the matching result obtained by the matching unit. A system characterized by:

2. Have a request collection department to collect employee requests 2. The system of claim 1.

3. Establish a needs gathering department to gather information on the needs of small and medium-sized enterprises 2. The system of claim 1.

4. Equipped with a linking section that links data in real time 2. The system of claim 1.

5. The collecting unit Collect employee skills, experience, and desired work 2. The system of claim 1.

6. The analysis unit Evaluate based on skill matching criteria and years of experience 2. The system of claim 1.

7. The matching unit Aligning employee demands with the needs of small businesses 2. The system of claim 1.

8. The providing unit Providing matching results through API 2. The system of claim 1.

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