System

Abstract

An object of the system according to the embodiment is to make it easier for retired individuals to find local jobs and to contribute to the problem of local labor shortages.SOLUTION: A system includes an investigation unit, a proposal unit, and a matching unit. The investigation unit investigates an organization seeking a human resource in a local area on the basis of information on a place and a job type input from the user. A proposal part proposes a local organization optimal to the user on the basis of a result investigated by the investigation part. The matching unit connects the local organization proposed by the proposal unit and the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult for retired individuals to find work in rural areas, making it difficult to address the problem of labor shortages in rural areas.

[0005] The system according to the embodiment aims to make it easier for retired individuals to find work in rural areas and contribute to solving the problem of labor shortages in rural areas. [Means for solving the problem]

[0006] The system according to the embodiment includes a research unit, a proposal unit, and a matching unit. The research unit investigates organizations seeking personnel in local areas based on location and job type information input by the user. The proposal unit proposes the most suitable local organization to the user based on the results of the research conducted by the research unit. The matching unit connects the user with the local organization proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can make it easier for retired individuals to find work in rural areas, thereby contributing to the problem of labor shortages in rural areas. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The service according to an embodiment of the present invention uses generative AI to research and propose organizations seeking human resources in rural areas, and connects these organizations with retired individuals. This allows retired individuals to enjoy their leisure time while eliminating financial worries. It also contributes to resolving the labor shortage problem in rural areas.

[0029] A service according to an embodiment includes a research unit, a proposal unit, and a matching unit. The research unit investigates organizations seeking personnel in local areas based on location and job type information input by a user. For example, the research unit analyzes information input by a user, such as "I'm looking for an agricultural-related job in Hokkaido," and lists relevant organizations. The research unit can also research local organizations using database searches and questionnaire surveys. The proposal unit suggests the most suitable local organization for the user based on the results of the research by the research unit. For example, the proposal unit makes a specific proposal, such as "XX Farm is recruiting personnel for an agricultural-related job in Hokkaido." The proposal unit can also make proposals taking into account the user's skill set and work history. The matching unit connects the user with the local organizations suggested by the proposal unit. For example, the matching unit arranges interview schedules between the user and the local organizations. The matching unit can also support contract procedures between the user and the local organizations. As a result, the service according to an embodiment allows users to enjoy their leisure time while eliminating financial concerns after retirement. It can also contribute to addressing the labor shortage issue in local areas.

[0030] The research department can analyze a user's past work history and skill set and, based on that, suggest the most suitable job type and work content. For example, the generation AI in the research department analyzes a user's past work history data and suggests the most suitable job type for a user with specific skills and experience. For example, for a user who previously worked in the IT industry, the generation AI suggests job openings at local IT-related companies. The research department also analyzes a user's skill set and suggests suitable work content based on the skills. For example, for a user with project management skills, the generation AI suggests job openings for project management positions in local areas. The research department also comprehensively analyzes a user's work history and skill set and lists the most suitable job types and work content for the user. For example, for a user with previous sales experience, the generation AI suggests job openings for sales positions in local areas. This makes it possible to suggest the most suitable job types and work content based on the user's past work history and skill set.

[0031] The research department can analyze the culture and working styles of local organizations and suggest organizations that suit the user's personality and working style. For example, the generation AI in the research department analyzes the cultural data of local organizations and suggests organizations that suit the user's personality. For example, for a user who prefers organizations with a homely atmosphere, the generation AI will suggest local family-run companies. The research department can also analyze the working style data of local organizations and suggest organizations that suit the user's working style. For example, for a user who wishes to work remotely, the generation AI will suggest local companies that allow remote work. The research department can also analyze the user's personality and working style and use that information to create a list of optimal local organizations. For example, for a user who values ​​teamwork, the generation AI will suggest local companies that require team cooperation. This makes it possible to suggest organizations that suit the user's personality and working style.

[0032] The suggestion unit can analyze the user's hobbies and interests and, based on that, suggest areas where leisure activities can also be enjoyed. For example, the suggestion unit uses a generation AI to analyze the user's hobby data and suggest areas where leisure activities can also be enjoyed based on the hobbies. For example, for a user whose hobby is fishing, the suggestion unit suggests local areas where fishing can be enjoyed. The suggestion unit also uses a generation AI to analyze the user's interest data and suggest areas where leisure activities can also be enjoyed based on the interests. For example, for a user who is interested in history, the suggestion unit suggests local areas with many historical tourist spots. The suggestion unit also uses a generation AI to comprehensively analyze the user's hobbies and interests and, based on that, lists areas where leisure activities can also be enjoyed. For example, for a user who likes outdoor activities, the suggestion unit suggests local areas rich in nature. In this way, it is possible to suggest areas where leisure activities can also be enjoyed based on the user's hobbies and interests.

[0033] The suggestion unit can analyze the user's health condition and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit uses a generation AI to analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that provide a stress-free work environment. The suggestion unit also uses a generation AI to analyze the user's health condition and list organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have introduced health management programs. The suggestion unit also uses a generation AI to comprehensively analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have relaxation spaces. This makes it possible to suggest organizations that provide a work environment that is considerate of the user's health condition.

[0034] The proposal unit can analyze past job vacancies of local organizations and predict future job vacancies. For example, the proposal unit uses a generation AI to analyze past job vacancies of local organizations and predict future job vacancies. For example, future job demand is predicted based on past job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes the job vacancy data of local organizations and predicts future job vacancies. For example, it performs a time series analysis of the job vacancy data and predicts future job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes past job vacancies of local organizations and predicts future job vacancies. For example, it predicts job demand for specific industries or occupations and makes suggestions to the user. This makes it possible to predict future job vacancies.

[0035] The proposal unit can analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit uses the generation AI to analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with high employee satisfaction. The proposal unit also uses the generation AI to list organizations that offer a comfortable working environment for the user based on the employee satisfaction data of local organizations. For example, the proposal unit proposes local companies that offer excellent employee benefits. The proposal unit also uses the generation AI to comprehensively analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with a good workplace atmosphere. This makes it possible to propose organizations that offer a comfortable working environment for the user.

[0036] The proposal unit can analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, the generation AI in the proposal unit analyzes the employee benefit data of local organizations and suggests organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that have comprehensive health insurance and pension systems. The proposal unit also uses the generation AI to create a list of organizations that offer employee benefits that are attractive to the user based on the employee benefit data of local organizations. For example, it suggests local companies that offer childcare support and refreshment leave. The proposal unit also uses the generation AI to comprehensively analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that offer extensive employee discounts and in-house events. This makes it possible to suggest organizations that offer employee benefits that are attractive to the user.

[0037] The suggestion unit can analyze the relationship between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit uses the generation AI to analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest local companies that actively participate in local events. The suggestion unit also uses the generation AI to list organizations that will allow the user to easily integrate into the local community based on the relationship data between the local organization and the local community. For example, the suggestion unit can suggest local companies that participate in local volunteer activities. The suggestion unit also uses the generation AI to comprehensively analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest organizations that will allow the user to easily integrate into the local community.

[0038] The proposal unit can analyze local labor market data and identify the fields with the greatest labor shortages. In the proposal unit, for example, the generation AI analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies fields such as agriculture and tourism and suggests them to the user. The proposal unit also builds a system in which the generation AI identifies the fields with the greatest labor shortages based on local labor market data. For example, it analyzes labor market data and identifies fields with labor shortages. In addition, the proposal unit comprehensively analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies labor shortages in specific industries or occupations and suggests them to the user. This makes it possible to identify the fields with the greatest labor shortages.

[0039] The proposal unit can analyze growth forecasts for local organizations and identify areas where labor will be needed in the future. For example, the proposal unit uses a generation AI to analyze growth forecast data for local organizations and identify areas where labor will be needed in the future. For example, it identifies industries and occupations that are expected to grow and suggests them to the user. The proposal unit also builds a system in which the generation AI uses growth forecast data for local organizations to identify areas where labor will be needed in the future. For example, it analyzes growth forecast data and identifies areas where labor shortages are predicted. The proposal unit also uses a generation AI to comprehensively analyze growth forecast data for local organizations and identify areas where labor will be needed in the future. For example, it identifies areas where labor shortages are predicted based on growth forecasts for specific industries and occupations and suggests them to the user. This makes it possible to identify areas where labor will be needed in the future.

[0040] The proposal unit can work with local educational institutions to propose career paths to local young people. For example, the generation AI works with local educational institutions to propose career paths to local young people. For example, it works with local high schools and universities to propose local career paths to young people. The proposal unit also builds a system in which the generation AI works with local educational institutions to propose career paths to local young people. For example, it analyzes data from educational institutions to propose optimal career paths to young people. The proposal unit also builds a system in which the generation AI works with local educational institutions to propose career paths to local young people. For example, it provides information on local companies and industries to propose local career paths to young people. This makes it possible to propose career paths to local young people.

[0041] The proposal department can work with local companies to make proposals to promote the introduction of remote work. For example, the generation AI can work with local companies to make proposals to promote the introduction of remote work. For example, the generation AI can propose the advantages of remote work and how to introduce it to companies, thereby resolving labor shortages. The proposal department can also build a system in which the generation AI can work with local companies to promote the introduction of remote work. For example, the generation AI can propose tasks and tools suitable for remote work and encourage companies to introduce it. The proposal department can also work with local companies to make proposals to promote the introduction of remote work. For example, the generation AI can introduce successful cases and best practices of remote work to companies and support their introduction. This makes it possible to make proposals to promote the introduction of remote work.

[0042] The suggestion unit can analyze the user's past income and expenditure data and suggest the optimal income source. In the suggestion unit, for example, the generation AI analyzes the user's past income data and suggests the optimal income source. For example, it suggests an income source that suits the user based on past income patterns. In addition, the suggestion unit can analyze the user's expenditure data and suggest the optimal income source. For example, it suggests an income source that suits the user based on past expenditure patterns. In addition, the suggestion unit can analyze the user's income and expenditure data comprehensively and suggest the optimal income source. For example, it takes into account the balance between income and expenditure and suggests an income source that suits the user. In this way, it is possible to suggest the optimal income source based on the user's past income and expenditure data.

[0043] The proposal unit can analyze the user's asset management data and propose the optimal asset management method. In the proposal unit, for example, the generation AI analyzes the user's asset management data and proposes the optimal asset management method. For example, it proposes an asset management method that suits the user based on past investment patterns. The proposal unit also builds a system in which the generation AI proposes the optimal asset management method based on the user's asset management data. For example, it analyzes the risks and returns of asset management and proposes an investment method that suits the user. The proposal unit also comprehensively analyzes the user's asset management data and proposes the optimal asset management method. For example, it takes into account asset diversification and risk management to propose an investment method that suits the user. In this way, it is possible to propose the optimal asset management method based on the user's asset management data.

[0044] The suggestion unit can analyze the user's health condition and suggest health-conscious income sources. For example, the suggestion unit uses a generation AI to analyze the user's health data and suggest health-conscious income sources. For example, it can suggest jobs that do not require much physical strength or remote work. The suggestion unit also uses a generation AI to analyze the user's health condition and list health-conscious income sources. For example, it can suggest jobs that provide a work environment that makes it easy to manage health. The suggestion unit also uses a generation AI to comprehensively analyze the user's health data and suggest health-conscious income sources. For example, it can suggest low-stress jobs or flexible working styles. This makes it possible to suggest income sources that take the user's health condition into consideration.

[0045] The suggestion unit can analyze the user's lifestyle and suggest income sources that suit the lifestyle. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and suggest income sources that suit the lifestyle. For example, it can suggest jobs that allow for flexible working styles. The suggestion unit also uses a generation AI to analyze the user's lifestyle and list income sources that suit the lifestyle. For example, it can suggest jobs that are related to the user's hobbies and interests. The suggestion unit also uses a generation AI to comprehensively analyze the user's lifestyle data and suggest income sources that suit the lifestyle. For example, it can suggest jobs that allow for working styles that suit the user's lifestyle. This makes it possible to suggest income sources that suit the user's lifestyle.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The suggestion unit can analyze the user's hobbies and interests and, based on that, suggest areas where leisure activities can also be enjoyed. For example, the suggestion unit uses a generation AI to analyze the user's hobby data and suggest areas where leisure activities can also be enjoyed based on the hobbies. For example, for a user whose hobby is fishing, the suggestion unit suggests local areas where fishing can be enjoyed. The suggestion unit also uses a generation AI to analyze the user's interest data and suggest areas where leisure activities can also be enjoyed based on the interests. For example, for a user who is interested in history, the suggestion unit suggests local areas with many historical tourist spots. The suggestion unit also uses a generation AI to comprehensively analyze the user's hobbies and interests and, based on that, lists areas where leisure activities can also be enjoyed. For example, for a user who likes outdoor activities, the suggestion unit suggests local areas rich in nature. In this way, it is possible to suggest areas where leisure activities can also be enjoyed based on the user's hobbies and interests.

[0048] The suggestion unit can analyze the user's health condition and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit uses a generation AI to analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that provide a stress-free work environment. The suggestion unit also uses a generation AI to analyze the user's health condition and list organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have introduced health management programs. The suggestion unit also uses a generation AI to comprehensively analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have relaxation spaces. This makes it possible to suggest organizations that provide a work environment that is considerate of the user's health condition.

[0049] The proposal unit can analyze past job vacancies of local organizations and predict future job vacancies. For example, the proposal unit uses a generation AI to analyze past job vacancies of local organizations and predict future job vacancies. For example, future job demand is predicted based on past job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes the job vacancy data of local organizations and predicts future job vacancies. For example, it performs a time series analysis of the job vacancy data and predicts future job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes past job vacancies of local organizations and predicts future job vacancies. For example, it predicts job demand for specific industries or occupations and makes suggestions to the user. This makes it possible to predict future job vacancies.

[0050] The proposal unit can analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit uses the generation AI to analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with high employee satisfaction. The proposal unit also uses the generation AI to list organizations that offer a comfortable working environment for the user based on the employee satisfaction data of local organizations. For example, the proposal unit proposes local companies that offer excellent employee benefits. The proposal unit also uses the generation AI to comprehensively analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with a good workplace atmosphere. This makes it possible to propose organizations that offer a comfortable working environment for the user.

[0051] The proposal unit can analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, the generation AI in the proposal unit analyzes the employee benefit data of local organizations and suggests organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that have comprehensive health insurance and pension systems. The proposal unit also uses the generation AI to create a list of organizations that offer employee benefits that are attractive to the user based on the employee benefit data of local organizations. For example, it suggests local companies that offer childcare support and refreshment leave. The proposal unit also uses the generation AI to comprehensively analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that offer extensive employee discounts and in-house events. This makes it possible to suggest organizations that offer employee benefits that are attractive to the user.

[0052] The suggestion unit can analyze the relationship between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit uses the generation AI to analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest local companies that actively participate in local events. The suggestion unit also uses the generation AI to list organizations that will allow the user to easily integrate into the local community based on the relationship data between the local organization and the local community. For example, the suggestion unit can suggest local companies that participate in local volunteer activities. The suggestion unit also uses the generation AI to comprehensively analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest organizations that will allow the user to easily integrate into the local community.

[0053] The proposal unit can analyze local labor market data and identify the fields with the greatest labor shortages. In the proposal unit, for example, the generation AI analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies fields such as agriculture and tourism and suggests them to the user. The proposal unit also builds a system in which the generation AI identifies the fields with the greatest labor shortages based on local labor market data. For example, it analyzes labor market data and identifies fields with labor shortages. In addition, the proposal unit comprehensively analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies labor shortages in specific industries or occupations and suggests them to the user. This makes it possible to identify the fields with the greatest labor shortages.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The research department investigates organizations seeking personnel in regional areas based on the location and job type information entered by the user. For example, the research department analyzes information entered by the user, such as "looking for agricultural-related work in Hokkaido," and lists relevant organizations. The research department can also investigate regional organizations using database searches and questionnaire surveys. Step 2: The proposal department proposes the most suitable local organization to the user based on the results of the research conducted by the research department. For example, the proposal department may make a specific proposal such as, "X farm is recruiting personnel for an agricultural-related job in Hokkaido." The proposal department may also consider the user's skill set and work history when making proposals. Step 3: The matching department connects the user with the local organization proposed by the proposal department. For example, the matching department coordinates an interview schedule between the user and the local organization. The matching department can also support the contract procedure between the user and the local organization.

[0056] (Example 2) The service according to an embodiment of the present invention uses generative AI to research and propose organizations seeking human resources in rural areas, and connects these organizations with retired individuals. This allows retired individuals to enjoy their leisure time while eliminating financial worries. It also contributes to resolving the labor shortage problem in rural areas.

[0057] A service according to an embodiment includes a research unit, a proposal unit, and a matching unit. The research unit investigates organizations seeking personnel in local areas based on location and job type information input by a user. For example, the research unit analyzes information input by a user, such as "I'm looking for an agricultural-related job in Hokkaido," and lists relevant organizations. The research unit can also research local organizations using database searches and questionnaire surveys. The proposal unit suggests the most suitable local organization for the user based on the results of the research by the research unit. For example, the proposal unit makes a specific proposal, such as "XX Farm is recruiting personnel for an agricultural-related job in Hokkaido." The proposal unit can also make proposals taking into account the user's skill set and work history. The matching unit connects the user with the local organizations suggested by the proposal unit. For example, the matching unit arranges interview schedules between the user and the local organizations. The matching unit can also support contract procedures between the user and the local organizations. As a result, the service according to an embodiment allows users to enjoy their leisure time while eliminating financial concerns after retirement. It can also contribute to addressing the labor shortage issue in local areas.

[0058] The research department can analyze a user's past work history and skill set and, based on that, suggest the most suitable job type and work content. For example, the generation AI in the research department analyzes a user's past work history data and suggests the most suitable job type for a user with specific skills and experience. For example, for a user who previously worked in the IT industry, the generation AI suggests job openings at local IT-related companies. The research department also analyzes a user's skill set and suggests suitable work content based on the skills. For example, for a user with project management skills, the generation AI suggests job openings for project management positions in local areas. The research department also comprehensively analyzes a user's work history and skill set and lists the most suitable job types and work content for the user. For example, for a user with previous sales experience, the generation AI suggests job openings for sales positions in local areas. This makes it possible to suggest the most suitable job types and work content based on the user's past work history and skill set.

[0059] The research department can analyze the culture and working styles of local organizations and suggest organizations that suit the user's personality and working style. For example, the generation AI in the research department analyzes the cultural data of local organizations and suggests organizations that suit the user's personality. For example, for a user who prefers organizations with a homely atmosphere, the generation AI will suggest local family-run companies. The research department can also analyze the working style data of local organizations and suggest organizations that suit the user's working style. For example, for a user who wishes to work remotely, the generation AI will suggest local companies that allow remote work. The research department can also analyze the user's personality and working style and use that information to create a list of optimal local organizations. For example, for a user who values ​​teamwork, the generation AI will suggest local companies that require team cooperation. This makes it possible to suggest organizations that suit the user's personality and working style.

[0060] The research department uses the emotion estimation function to infer emotions from the information entered by the user and can make suggestions that will most satisfy the user. For example, the generation AI in the research department analyzes the information entered by the user and uses the emotion estimation function to infer the user's emotions. For example, the research department prioritizes analyzing information that the user has positive emotions for and makes optimal suggestions. The research department also uses the emotion estimation function to infer emotions from the information entered by the user and makes suggestions that will most satisfy the user. For example, it suggests a work environment where the user can work while enjoying themselves. The research department also uses the emotion estimation function to analyze emotions from the information entered by the user and makes suggestions that will most satisfy the user. For example, it suggests a work environment where the user can relax. This makes it possible to make suggestions that will most satisfy the user.

[0061] The suggestion unit can analyze the user's hobbies and interests and, based on that, suggest areas where leisure activities can also be enjoyed. For example, the suggestion unit uses a generation AI to analyze the user's hobby data and suggest areas where leisure activities can also be enjoyed based on the hobbies. For example, for a user whose hobby is fishing, the suggestion unit suggests local areas where fishing can be enjoyed. The suggestion unit also uses a generation AI to analyze the user's interest data and suggest areas where leisure activities can also be enjoyed based on the interests. For example, for a user who is interested in history, the suggestion unit suggests local areas with many historical tourist spots. The suggestion unit also uses a generation AI to comprehensively analyze the user's hobbies and interests and, based on that, lists areas where leisure activities can also be enjoyed. For example, for a user who likes outdoor activities, the suggestion unit suggests local areas rich in nature. In this way, it is possible to suggest areas where leisure activities can also be enjoyed based on the user's hobbies and interests.

[0062] The suggestion unit can analyze the user's health condition and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit uses a generation AI to analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that provide a stress-free work environment. The suggestion unit also uses a generation AI to analyze the user's health condition and list organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have introduced health management programs. The suggestion unit also uses a generation AI to comprehensively analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have relaxation spaces. This makes it possible to suggest organizations that provide a work environment that is considerate of the user's health condition.

[0063] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest a work environment where the user can enjoy working. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest a work environment where the user can relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest a work environment that will give the user a sense of satisfaction. This makes it possible to make suggestions that will cause the user to have positive emotions toward the proposed workplace.

[0064] The proposal unit can analyze past job vacancies of local organizations and predict future job vacancies. For example, the proposal unit uses a generation AI to analyze past job vacancies of local organizations and predict future job vacancies. For example, future job demand is predicted based on past job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes the job vacancy data of local organizations and predicts future job vacancies. For example, it performs a time series analysis of the job vacancy data and predicts future job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes past job vacancies of local organizations and predicts future job vacancies. For example, it predicts job demand for specific industries or occupations and makes suggestions to the user. This makes it possible to predict future job vacancies.

[0065] The proposal unit can analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit uses the generation AI to analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with high employee satisfaction. The proposal unit also uses the generation AI to list organizations that offer a comfortable working environment for the user based on the employee satisfaction data of local organizations. For example, the proposal unit proposes local companies that offer excellent employee benefits. The proposal unit also uses the generation AI to comprehensively analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with a good workplace atmosphere. This makes it possible to propose organizations that offer a comfortable working environment for the user.

[0066] The suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, the suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, it can suggest an organization that will give the user a sense of satisfaction. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, it can suggest an organization where the user can relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, it can suggest an organization where the user can enjoy working. This makes it possible to make a proposal that will elicit positive emotion in the proposed organization.

[0067] The proposal unit can analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, the generation AI in the proposal unit analyzes the employee benefit data of local organizations and suggests organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that have comprehensive health insurance and pension systems. The proposal unit also uses the generation AI to create a list of organizations that offer employee benefits that are attractive to the user based on the employee benefit data of local organizations. For example, it suggests local companies that offer childcare support and refreshment leave. The proposal unit also uses the generation AI to comprehensively analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that offer extensive employee discounts and in-house events. This makes it possible to suggest organizations that offer employee benefits that are attractive to the user.

[0068] The suggestion unit can analyze the relationship between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit uses the generation AI to analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest local companies that actively participate in local events. The suggestion unit also uses the generation AI to list organizations that will allow the user to easily integrate into the local community based on the relationship data between the local organization and the local community. For example, the suggestion unit can suggest local companies that participate in local volunteer activities. The suggestion unit also uses the generation AI to comprehensively analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest organizations that will allow the user to easily integrate into the local community.

[0069] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can suggest organizations that provide employee benefits that will give the user a sense of satisfaction. Furthermore, the suggestion unit can use the generation AI to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can suggest organizations that provide employee benefits that allow the user to relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can suggest organizations that provide employee benefits that the user can enjoy using. This makes it possible to make suggestions that will elicit positive emotions toward the employee benefits offered by the proposed organization.

[0070] The proposal unit can analyze local labor market data and identify the fields with the greatest labor shortages. In the proposal unit, for example, the generation AI analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies fields such as agriculture and tourism and suggests them to the user. The proposal unit also builds a system in which the generation AI identifies the fields with the greatest labor shortages based on local labor market data. For example, it analyzes labor market data and identifies fields with labor shortages. In addition, the proposal unit comprehensively analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies labor shortages in specific industries or occupations and suggests them to the user. This makes it possible to identify the fields with the greatest labor shortages.

[0071] The proposal unit can analyze growth forecasts for local organizations and identify areas where labor will be needed in the future. For example, the proposal unit uses a generation AI to analyze growth forecast data for local organizations and identify areas where labor will be needed in the future. For example, it identifies industries and occupations that are expected to grow and suggests them to the user. The proposal unit also builds a system in which the generation AI uses growth forecast data for local organizations to identify areas where labor will be needed in the future. For example, it analyzes growth forecast data and identifies areas where labor shortages are predicted. The proposal unit also uses a generation AI to comprehensively analyze growth forecast data for local organizations and identify areas where labor will be needed in the future. For example, it identifies areas where labor shortages are predicted based on growth forecasts for specific industries and occupations and suggests them to the user. This makes it possible to identify areas where labor will be needed in the future.

[0072] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the local labor market and make suggestions that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the local labor market and make suggestions that will elicit positive emotions. For example, it can suggest a labor market that will give the user a sense of satisfaction. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the local labor market and make suggestions that will elicit positive emotions. For example, it can suggest a labor market where the user can relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the local labor market and make suggestions that will elicit positive emotions. For example, it can suggest a labor market where the user can enjoy working. This makes it possible to make suggestions that will give the user positive emotions toward the local labor market.

[0073] The proposal unit can work with local educational institutions to propose career paths to local young people. For example, the generation AI works with local educational institutions to propose career paths to local young people. For example, it works with local high schools and universities to propose local career paths to young people. The proposal unit also builds a system in which the generation AI works with local educational institutions to propose career paths to local young people. For example, it analyzes data from educational institutions to propose optimal career paths to young people. The proposal unit also builds a system in which the generation AI works with local educational institutions to propose career paths to local young people. For example, it provides information on local companies and industries to propose local career paths to young people. This makes it possible to propose career paths to local young people.

[0074] The proposal department can work with local companies to make proposals to promote the introduction of remote work. For example, the generation AI can work with local companies to make proposals to promote the introduction of remote work. For example, the generation AI can propose the advantages of remote work and how to introduce it to companies, thereby resolving labor shortages. The proposal department can also build a system in which the generation AI can work with local companies to promote the introduction of remote work. For example, the generation AI can propose tasks and tools suitable for remote work and encourage companies to introduce it. The proposal department can also work with local companies to make proposals to promote the introduction of remote work. For example, the generation AI can introduce successful cases and best practices of remote work to companies and support their introduction. This makes it possible to make proposals to promote the introduction of remote work.

[0075] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward local educational institutions and companies, and make suggestions that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward local educational institutions and companies, and make suggestions that will elicit positive emotions. For example, it can suggest educational institutions and companies that will give the user a sense of satisfaction. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward local educational institutions and companies, and make suggestions that will elicit positive emotions. For example, it can suggest educational institutions and companies where the user can relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward local educational institutions and companies, and make suggestions that will elicit positive emotions. For example, it can suggest educational institutions where the user can learn while having fun, and companies where the user can work. This makes it possible to make suggestions that will elicit positive emotions toward local educational institutions and companies.

[0076] The suggestion unit can analyze the user's past income and expenditure data and suggest the optimal income source. In the suggestion unit, for example, the generation AI analyzes the user's past income data and suggests the optimal income source. For example, it suggests an income source that suits the user based on past income patterns. In addition, the suggestion unit can analyze the user's expenditure data and suggest the optimal income source. For example, it suggests an income source that suits the user based on past expenditure patterns. In addition, the suggestion unit can analyze the user's income and expenditure data comprehensively and suggest the optimal income source. For example, it takes into account the balance between income and expenditure and suggests an income source that suits the user. In this way, it is possible to suggest the optimal income source based on the user's past income and expenditure data.

[0077] The proposal unit can analyze the user's asset management data and propose the optimal asset management method. In the proposal unit, for example, the generation AI analyzes the user's asset management data and proposes the optimal asset management method. For example, it proposes an asset management method that suits the user based on past investment patterns. The proposal unit also builds a system in which the generation AI proposes the optimal asset management method based on the user's asset management data. For example, it analyzes the risks and returns of asset management and proposes an investment method that suits the user. The proposal unit also comprehensively analyzes the user's asset management data and proposes the optimal asset management method. For example, it takes into account asset diversification and risk management to propose an investment method that suits the user. In this way, it is possible to propose the optimal asset management method based on the user's asset management data.

[0078] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source or asset management method, and make suggestions that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source or asset management method, and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest income sources and asset management methods that will give the user a sense of security. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source or asset management method, and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest income sources and asset management methods that will allow the user to relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source or asset management method, and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest income sources and asset management methods that will give the user a sense of satisfaction. This makes it possible to make suggestions that will cause the user to have positive emotions toward the proposed income source or asset management method.

[0079] The suggestion unit can analyze the user's health condition and suggest health-conscious income sources. For example, the suggestion unit uses a generation AI to analyze the user's health data and suggest health-conscious income sources. For example, it can suggest jobs that do not require much physical strength or remote work. The suggestion unit also uses a generation AI to analyze the user's health condition and list health-conscious income sources. For example, it can suggest jobs that provide a work environment that makes it easy to manage health. The suggestion unit also uses a generation AI to comprehensively analyze the user's health data and suggest health-conscious income sources. For example, it can suggest low-stress jobs or flexible working styles. This makes it possible to suggest income sources that take the user's health condition into consideration.

[0080] The suggestion unit can analyze the user's lifestyle and suggest income sources that suit the lifestyle. For example, the suggestion unit uses a generation AI to analyze the user's lifestyle data and suggest income sources that suit the lifestyle. For example, it can suggest jobs that allow for flexible working styles. The suggestion unit also uses a generation AI to analyze the user's lifestyle and list income sources that suit the lifestyle. For example, it can suggest jobs that are related to the user's hobbies and interests. The suggestion unit also uses a generation AI to comprehensively analyze the user's lifestyle data and suggest income sources that suit the lifestyle. For example, it can suggest jobs that allow for working styles that suit the user's lifestyle. This makes it possible to suggest income sources that suit the user's lifestyle.

[0081] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source and lifestyle, and make suggestions that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source and lifestyle, and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest income sources and lifestyles that will give the user a sense of security. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source and lifestyle, and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest income sources and lifestyles that will allow the user to relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed income source and lifestyle, and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest income sources and lifestyles that will give the user a sense of satisfaction. This makes it possible to make suggestions that will cause the user to have positive emotions toward the proposed income source and lifestyle.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The suggestion unit can analyze the user's hobbies and interests and, based on that, suggest areas where leisure activities can also be enjoyed. For example, the suggestion unit uses a generation AI to analyze the user's hobby data and suggest areas where leisure activities can also be enjoyed based on the hobbies. For example, for a user whose hobby is fishing, the suggestion unit suggests local areas where fishing can be enjoyed. The suggestion unit also uses a generation AI to analyze the user's interest data and suggest areas where leisure activities can also be enjoyed based on the interests. For example, for a user who is interested in history, the suggestion unit suggests local areas with many historical tourist spots. The suggestion unit also uses a generation AI to comprehensively analyze the user's hobbies and interests and, based on that, lists areas where leisure activities can also be enjoyed. For example, for a user who likes outdoor activities, the suggestion unit suggests local areas rich in nature. In this way, it is possible to suggest areas where leisure activities can also be enjoyed based on the user's hobbies and interests.

[0084] The suggestion unit can analyze the user's health condition and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit uses a generation AI to analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that provide a stress-free work environment. The suggestion unit also uses a generation AI to analyze the user's health condition and list organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have introduced health management programs. The suggestion unit also uses a generation AI to comprehensively analyze the user's health data and suggest organizations that provide a health-conscious work environment. For example, the suggestion unit suggests local companies that have relaxation spaces. This makes it possible to suggest organizations that provide a work environment that is considerate of the user's health condition.

[0085] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest a work environment where the user can enjoy working. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest a work environment where the user can relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the proposed workplace and make suggestions that will elicit positive emotions. For example, the suggestion unit can suggest a work environment that will give the user a sense of satisfaction. This makes it possible to make suggestions that will cause the user to have positive emotions toward the proposed workplace.

[0086] The proposal unit can analyze past job vacancies of local organizations and predict future job vacancies. For example, the proposal unit uses a generation AI to analyze past job vacancies of local organizations and predict future job vacancies. For example, future job demand is predicted based on past job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes the job vacancy data of local organizations and predicts future job vacancies. For example, it performs a time series analysis of the job vacancy data and predicts future job vacancy trends. The proposal unit also builds a system in which the generation AI analyzes past job vacancies of local organizations and predicts future job vacancies. For example, it predicts job demand for specific industries or occupations and makes suggestions to the user. This makes it possible to predict future job vacancies.

[0087] The proposal unit can analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit uses the generation AI to analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with high employee satisfaction. The proposal unit also uses the generation AI to list organizations that offer a comfortable working environment for the user based on the employee satisfaction data of local organizations. For example, the proposal unit proposes local companies that offer excellent employee benefits. The proposal unit also uses the generation AI to comprehensively analyze employee satisfaction data of local organizations and propose organizations that offer a comfortable working environment for the user. For example, the proposal unit proposes local companies with a good workplace atmosphere. This makes it possible to propose organizations that offer a comfortable working environment for the user.

[0088] The suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, the suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, it can suggest an organization that will give the user a sense of satisfaction. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, it can suggest an organization where the user can relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotion the user will have toward the proposed organization and make a proposal that will elicit the most positive emotion. For example, it can suggest an organization where the user can enjoy working. This makes it possible to make a proposal that will elicit positive emotion in the proposed organization.

[0089] The proposal unit can analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, the generation AI in the proposal unit analyzes the employee benefit data of local organizations and suggests organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that have comprehensive health insurance and pension systems. The proposal unit also uses the generation AI to create a list of organizations that offer employee benefits that are attractive to the user based on the employee benefit data of local organizations. For example, it suggests local companies that offer childcare support and refreshment leave. The proposal unit also uses the generation AI to comprehensively analyze the employee benefit data of local organizations and suggest organizations that offer employee benefits that are attractive to the user. For example, it suggests local companies that offer extensive employee discounts and in-house events. This makes it possible to suggest organizations that offer employee benefits that are attractive to the user.

[0090] The suggestion unit can analyze the relationship between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit uses the generation AI to analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest local companies that actively participate in local events. The suggestion unit also uses the generation AI to list organizations that will allow the user to easily integrate into the local community based on the relationship data between the local organization and the local community. For example, the suggestion unit can suggest local companies that participate in local volunteer activities. The suggestion unit also uses the generation AI to comprehensively analyze the relationship data between the local organization and the local community and suggest organizations that will allow the user to easily integrate into the local community. For example, the suggestion unit can suggest organizations that will allow the user to easily integrate into the local community.

[0091] The suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can suggest organizations that provide employee benefits that will give the user a sense of satisfaction. Furthermore, the suggestion unit can use the generation AI to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can suggest organizations that provide employee benefits that allow the user to relax. Furthermore, the suggestion unit can use the emotion estimation function to predict the emotions the user will have toward the employee benefits offered by the proposed organization and make suggestions that elicit positive emotions. For example, the suggestion unit can suggest organizations that provide employee benefits that the user can enjoy using. This makes it possible to make suggestions that will elicit positive emotions toward the employee benefits offered by the proposed organization.

[0092] The proposal unit can analyze local labor market data and identify the fields with the greatest labor shortages. In the proposal unit, for example, the generation AI analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies fields such as agriculture and tourism and suggests them to the user. The proposal unit also builds a system in which the generation AI identifies the fields with the greatest labor shortages based on local labor market data. For example, it analyzes labor market data and identifies fields with labor shortages. In addition, the proposal unit comprehensively analyzes local labor market data and identifies the fields with the greatest labor shortages. For example, it identifies labor shortages in specific industries or occupations and suggests them to the user. This makes it possible to identify the fields with the greatest labor shortages.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The research department investigates organizations seeking personnel in regional areas based on the location and job type information entered by the user. For example, the research department analyzes information entered by the user, such as "looking for agricultural-related work in Hokkaido," and lists relevant organizations. The research department can also investigate regional organizations using database searches and questionnaire surveys. Step 2: The proposal department proposes the most suitable local organization to the user based on the results of the research conducted by the research department. For example, the proposal department may make a specific proposal such as, "X farm is recruiting personnel for an agricultural-related job in Hokkaido." The proposal department may also consider the user's skill set and work history when making proposals. Step 3: The matching department connects the user with the local organization proposed by the proposal department. For example, the matching department coordinates an interview schedule between the user and the local organization. The matching department can also support the contract procedure between the user and the local organization.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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 research department that investigates organizations seeking personnel in local areas based on location and job type information entered by users; a proposal unit that proposes optimal local organizations to users based on the results of the survey conducted by the survey unit; a matching unit that connects the local organization proposed by the proposal unit with the user. A system characterized by:

2. The research department Analyze the user's past work history and skill set, and based on that, suggest the most suitable job type and work content.

2. The system of claim 1.

3. The research department Analyze the culture and working style of the local organization and propose an organization that suits the user's personality and working style.

2. The system of claim 1.

4. The research department Estimate the user's feelings from the information entered by the user and make a suggestion that will most satisfy the user.

2. The system of claim 1.

5. The proposal unit Analyzing the user's hobbies and interests and based on that, suggesting areas where they can also enjoy leisure activities 2. The system of claim 1.

Images