system

Abstract

The system according to the embodiment aims to analyze information on job seekers, estimate the most suitable business type or industry, and propose the most suitable job destination based on expected annual salary. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, a calculation unit, and a proposal unit. The collection unit collects information on job seekers. The analysis unit analyzes the information collected by the collection unit. The estimation unit estimates the most suitable business type or industry based on the analysis results obtained by the analysis unit. The calculation unit calculates the expected annual income in the business type or industry estimated by the estimation unit. The proposal unit proposes the most suitable job destination based on the expected annual income calculated by the calculation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately estimate the most suitable business or industry based on the job seeker's information and calculate the expected annual salary, so there is room for improvement.

[0005] The system according to the embodiment aims to analyze information on job seekers, estimate the most suitable business type or industry, and propose the most suitable job destination based on expected annual salary. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an estimation unit, a calculation unit, and a proposal unit. The collection unit collects information on job seekers. The analysis unit analyzes the information collected by the collection unit. The estimation unit estimates the most suitable business type or industry based on the analysis results obtained by the analysis unit. The calculation unit calculates the expected annual income in the business type or industry estimated by the estimation unit. The proposal unit proposes the most suitable job destination based on the expected annual income calculated by the calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze information about job seekers, estimate the most suitable business type or industry, and suggest the most suitable job destination based on the expected annual salary. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A staffing system according to an embodiment of the present invention collects a wide range of information about job seekers, analyzes it using generative AI technology, and proposes optimal job opportunities. This system collects information about job seekers, such as their abilities, experience, qualifications, and personality, and uses generative AI to analyze it to predict the industry and job type that best suits them. Furthermore, the generative AI calculates the expected annual salary in the predicted industry and industry and proposes it to the job seeker. This system allows job seekers to consider completely different industries and jobs that they may not have known about. The generative AI also draws out new attractive features of job seekers and proposes optimal job opportunities. For example, detailed information about job seekers' abilities, experience, qualifications, and personality is collected. This information includes detailed data about the job seeker's past work, qualifications, and personality traits. For example, if the job seeker previously worked as an engineer, the system collects information about the job duties, qualifications, and personality traits. The generative AI then analyzes the collected information. Based on the collected information, the generative AI predicts the industry and job type that best suits the job seeker. For example, for a job seeker with engineering experience, the generative AI can predict not only the IT industry but also other industries such as manufacturing and services. Furthermore, the generative AI calculates the expected annual salary in the predicted industry and industry. Based on past data and market trends, the generative AI calculates the job seeker's expected annual salary if they change jobs. For example, it calculates the expected annual salary if they change jobs to the IT industry or the manufacturing industry. Finally, the generative AI suggests the job seeker's optimal job. Based on the predicted industry, industry, and expected annual salary, the generative AI suggests the job seeker's optimal job. For example, it suggests a job where the job seeker can utilize their engineering experience while trying a new industry. This system allows the job seeker to consider a completely different industry or field that they may not have known about. The generative AI also brings out the new appeal of the job seeker and suggests the optimal job. This enables the staffing system to efficiently collect, analyze, estimate, calculate, and suggest information about job seekers.

[0029] A staffing system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, a calculation unit, and a proposal unit. The collection unit collects information about job seekers. The information about job seekers includes, but is not limited to, their work history, skills, and desired conditions. For example, if the job seeker has previously worked as an engineer, the collection unit collects information about the job seeker's work, qualifications, and personality traits. If the job seeker has managerial experience, the collection unit can also collect information about leadership and management. Furthermore, if the job seeker has worked in various industries, the collection unit can collect information related to each industry in a balanced manner. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the job seeker's work history, skills, desired conditions, and other information to estimate the industry or business that is most suitable for the job seeker. For example, if the job seeker has experience as an engineer, the analysis unit can estimate not only the IT industry but also other industries such as manufacturing and services. The estimation unit estimates the industry or business that is most suitable for the job seeker based on the analysis results obtained by the analysis unit. The estimation unit makes the estimation, for example, by taking into account the correlation between the job seeker's skills and experience. The estimation unit can also make the estimation by taking into account the correlation between the job seeker's qualifications and personality traits. Furthermore, the estimation unit can also make the estimation by taking into account the correlation between the job seeker's past work history and current living situation. The calculation unit calculates the expected annual income in the business type or industry estimated by the estimation unit. The calculation unit, for example, calculates the expected annual income by referring to annual income data of past job seekers. The calculation unit can also calculate the expected annual income by referring to past market trend data. Furthermore, the calculation unit can calculate the expected annual income by referring to past annual income data by industry. The proposal unit proposes an optimal job change based on the expected annual income calculated by the calculation unit. For example, the proposal unit proposes a job change where the job seeker can take on a new industry while utilizing their experience as an engineer. The proposal unit can also bring out the job seeker's new appeal and propose an optimal job change. Furthermore, the proposal unit can propose an optimal job change based on the job seeker's desired conditions and living situation.As a result, the temporary staffing system according to the embodiment can efficiently collect, analyze, estimate, calculate, and propose information on job seekers.

[0030] Furthermore, the temporary staffing system is equipped with an attractiveness-drawing unit that draws out new attractiveness in job seekers. The attractiveness-drawing unit, for example, discovers new skills and potential abilities in job seekers and, based on that, suggests optimal job opportunities. For example, the attractiveness-drawing unit finds opportunities where job seekers can utilize their skills and abilities, even in industries or fields they have not previously worked in. The attractiveness-drawing unit can also draw out new attractiveness based on the job seeker's personality traits and interests. For example, it can identify situations where job seekers can demonstrate leadership or creativity, and suggest job opportunities based on those. Furthermore, the attractiveness-drawing unit can draw out new attractiveness based on the job seeker's past work experience and qualifications. For example, if a job seeker has experience as an engineer, that experience can be used to draw out the attractiveness of a new industry or field. This allows the job seeker to draw out new attractiveness and suggest optimal job opportunities.

[0031] The staffing system also includes an update unit that updates information about job seekers. The update unit, for example, periodically updates the information about job seekers. For example, the update unit periodically collects newly acquired qualifications, skills, experience, and the like from job seekers, and keeps the information up to date. The update unit can also update the information in response to changes in the job seeker's living situation or desired conditions. For example, if the job seeker starts a new family, the update unit collects that information and reflects it in job proposals. The update unit can also update the information in response to changes in the job seeker's emotions and interests. For example, the update unit collects information about new industries or fields in which the job seeker has an interest, and reflects it in job proposals. This allows the job seeker's information to be kept up to date.

[0032] The collection unit can analyze the job seeker's past work history and select the optimal information collection method. For example, if the job seeker has worked as an engineer in the past, the collection unit prioritizes collecting information about technical skills. For example, if the job seeker has experience as an engineer, the collection unit collects information about the job seeker's technical skills and reflects it in job proposals. In addition, if the job seeker has experience as a manager, the collection unit can also collect information about leadership and management. For example, if the job seeker has experience as a manager, the collection unit collects information about the job seeker's leadership and management and reflects it in job proposals. Furthermore, if the job seeker has worked in various industries, the collection unit can collect information related to each industry in a balanced manner. For example, if the job seeker has worked in various industries, the collection unit collects information related to each industry and reflects it in job proposals. This makes it possible to select the optimal information collection method based on the job seeker's past work history.

[0033] When collecting information, the collection unit can filter the information based on the job seeker's current living situation and areas of interest. For example, if the job seeker has a family, the collection unit collects information that takes into account the balance between family and work. For example, if the job seeker has a family, the collection unit collects information that takes into account the balance between family and work and reflects it in job suggestions. In addition, if the job seeker has a specific hobby or interest, the collection unit can prioritize collecting information related to that field. For example, if the job seeker has a specific hobby or interest, the collection unit collects information related to that field and reflects it in job suggestions. Furthermore, the collection unit can collect information regarding remote work or flexible working arrangements depending on the job seeker's current living situation. For example, if the job seeker desires remote work or flexible working arrangements, the collection unit collects that information and reflects it in job suggestions. This makes it possible to filter information based on the job seeker's living situation and areas of interest.

[0034] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into consideration the geographical location information of the job seeker. For example, if the job seeker lives in an urban area, the collection unit prioritizes collecting job information for the urban area. For example, if the job seeker lives in an urban area, the collection unit collects job information for the urban area and reflects it in job suggestions. Furthermore, if the job seeker lives in a rural area, the collection unit can prioritize collecting job information for the rural area. For example, if the job seeker lives in a rural area, the collection unit collects job information for the rural area and reflects it in job suggestions. Furthermore, if the job seeker wishes to change jobs overseas, the collection unit can prioritize collecting overseas job information. For example, if the job seeker wishes to change jobs overseas, the collection unit collects overseas job information and reflects it in job suggestions. In this way, highly relevant information can be collected based on the geographical location information of the job seeker.

[0035] When collecting information, the collection unit can analyze the job seeker's social media activities and collect related information. For example, if the job seeker is active on LinkedIn (registered trademark), the collection unit collects LinkedIn profile information. For example, if the job seeker is active on LinkedIn, the collection unit collects the profile information and reflects it in job suggestions. Furthermore, if the job seeker posts frequently on X (formerly Twitter (registered trademark)), the collection unit can analyze the content of X's (formerly Twitter) posts and collect related information. For example, if the job seeker posts frequently on X (formerly Twitter), the collection unit analyzes the content of the posts and reflects it in job suggestions. Furthermore, if the job seeker is active on Facebook (registered trademark), the collection unit can analyze the content of Facebook posts and collect related information. For example, if the job seeker is active on Facebook, the collection unit analyzes the content of the posts and reflects it in job suggestions. In this way, related information can be collected based on the job seeker's social media activities.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, performs a detailed analysis on information of high importance. For example, the analysis unit performs a detailed analysis on information of high importance and reflects the result in job suggestions. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit performs a simplified analysis on information of low importance and reflects the result in job suggestions. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on information of medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on information of medium importance and reflects the result in job suggestions. In this way, the level of detail of the analysis can be adjusted based on the importance of the collected information.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a technical skill analysis algorithm to information regarding technical skills. For example, the analysis unit applies the technical skill analysis algorithm to the information regarding technical skills and reflects it in job suggestions. The analysis unit can also apply a leadership analysis algorithm to information regarding managerial experience. For example, the analysis unit applies the leadership analysis algorithm to information regarding managerial experience and reflects it in job suggestions. Furthermore, the analysis unit can also apply a personality analysis algorithm to information regarding personality traits. For example, the analysis unit applies the personality analysis algorithm to information regarding personality traits and reflects it in job suggestions. This allows an appropriate analysis algorithm to be applied depending on the category of information.

[0038] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the information. The analysis unit, for example, prioritizes analysis of recently submitted information. For example, the analysis unit performs a prioritized analysis of recently submitted information and reflects the results in job placement proposals. The analysis unit can also postpone analysis of older information. For example, the analysis unit performs a postponed analysis of older information and reflects the results in job placement proposals. Furthermore, the analysis unit can also give moderate priority to analysis of information that was submitted at a medium time. For example, the analysis unit performs moderate priority analysis of information that was submitted at a medium time and reflects the results in job placement proposals. In this way, the priority of analysis can be determined based on the time of submission of the information.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of information with high relevance. For example, the analysis unit performs a prioritized analysis of information with high relevance and reflects the results in job placement suggestions. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit performs a postponed analysis of information with low relevance and reflects the results in job placement suggestions. Furthermore, the analysis unit can also give moderate priority to analysis of information with medium relevance. For example, the analysis unit performs moderate priority analysis of information with medium relevance and reflects the results in job placement suggestions. This allows the order of analysis to be adjusted based on the relevance of the information.

[0040] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationship between information during estimation. The estimation unit makes the estimation by taking into account, for example, the interrelationship between the skills and experience of the job seeker. For example, the estimation unit makes the estimation by taking into account the interrelationship between the skills and experience of the job seeker and reflects it in the job change proposal. The estimation unit can also make the estimation by taking into account the interrelationship between the qualifications and personality traits of the job seeker. For example, the estimation unit makes the estimation by taking into account the interrelationship between the qualifications and personality traits of the job seeker and reflects it in the job change proposal. Furthermore, the estimation unit can also make the estimation by taking into account the interrelationship between the job seeker's past work history and current living situation. For example, the estimation unit makes the estimation by taking into account the interrelationship between the job seeker's past work history and current living situation and reflects it in the job change proposal. In this way, the accuracy of the estimation can be improved by taking into account the interrelationship between information.

[0041] When making an estimation, the estimation unit can make the estimation taking into account attribute information of the job change candidate. The estimation unit makes the estimation taking into account, for example, the age of the job change candidate. For example, the estimation unit makes the estimation taking into account the age of the job change candidate and reflects this in the proposed job change candidate. The estimation unit can also make the estimation taking into account the gender of the job change candidate. For example, the estimation unit makes the estimation taking into account the gender of the job change candidate and reflects this in the proposed job change candidate. Furthermore, the estimation unit can also make the estimation taking into account the educational background of the job change candidate. For example, the estimation unit makes the estimation taking into account the educational background of the job change candidate and reflects this in the proposed job change candidate. In this way, estimation can be made taking into account attribute information of the job change candidate.

[0042] The estimation unit can make the estimation taking into account the geographical distribution of information. For example, if the job seeker lives in an urban area, the estimation unit prioritizes job information in the urban area. For example, if the job seeker lives in an urban area, the estimation unit prioritizes job information in the urban area and reflects this in the job change proposal. Furthermore, if the job seeker lives in a rural area, the estimation unit can also prioritize job information in the rural area. For example, if the job seeker lives in a rural area, the estimation unit prioritizes job information in the rural area and reflects this in the job change proposal. Furthermore, if the job seeker wishes to change jobs overseas, the estimation unit can also prioritize job information overseas. For example, if the job seeker wishes to change jobs overseas, the estimation unit prioritizes job information overseas and reflects this in the job change proposal. This makes it possible to make the estimation taking into account the geographical distribution of information.

[0043] The estimation unit can improve the accuracy of the estimation by referring to related literature when making the estimation. The estimation unit, for example, makes the estimation by referring to the latest research papers related to the skills of the job seeker. For example, the estimation unit makes the estimation by referring to the latest research papers related to the skills of the job seeker, and reflects the estimation in the proposed job change. The estimation unit can also make the estimation by referring to market reports related to the job seeker's industry. For example, the estimation unit makes the estimation by referring to market reports related to the job seeker's industry, and reflects the estimation in the proposed job change. Furthermore, the estimation unit can also make the estimation by referring to specialized books related to the qualifications of the job seeker. For example, the estimation unit makes the estimation by referring to specialized books related to the qualifications of the job seeker, and reflects the estimation in the proposed job change. In this way, the accuracy of the estimation can be improved by referring to related literature.

[0044] The calculation unit can improve the accuracy of the expected annual income by referring to past data when calculating. The calculation unit, for example, calculates the expected annual income by referring to annual income data of past job changers. For example, the calculation unit calculates the expected annual income by referring to annual income data of past job changers, and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income by referring to past market trend data. For example, the calculation unit calculates the expected annual income by referring to past market trend data, and reflects it in the proposed job change. Furthermore, the calculation unit can also calculate the expected annual income by referring to past industry-specific annual income data. For example, the calculation unit calculates the expected annual income by referring to past industry-specific annual income data, and reflects it in the proposed job change. In this way, the accuracy of the expected annual income can be improved by referring to past data.

[0045] The calculation unit can calculate the expected annual income taking into account market trends in the industry when calculating. The calculation unit, for example, calculates the expected annual income taking into account current market trends. For example, the calculation unit calculates the expected annual income taking into account current market trends and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income based on a future market forecast. For example, the calculation unit calculates the expected annual income based on a future market forecast and reflects it in the proposed job change. Furthermore, the calculation unit can also calculate the expected annual income taking into account the growth rate of the industry. For example, the calculation unit calculates the expected annual income taking into account the growth rate of the industry and reflects it in the proposed job change. In this way, the expected annual income can be calculated taking into account market trends in the industry.

[0046] During calculation, the calculation unit can determine the priority of expected annual income based on the time of submission of information. The calculation unit, for example, calculates the expected annual income based on the most recently submitted information. For example, the calculation unit calculates the expected annual income based on the most recently submitted information and reflects it in the job change proposal. The calculation unit can also calculate the expected annual income by putting older information on hold. For example, the calculation unit calculates the expected annual income by putting older information on hold and reflects it in the job change proposal. Furthermore, the calculation unit can calculate the expected annual income by giving appropriate priority to information submitted at a medium time. For example, the calculation unit calculates the expected annual income by giving appropriate priority to information submitted at a medium time and reflects it in the job change proposal. In this way, the priority of expected annual income can be determined based on the time of submission of information.

[0047] The calculation unit can improve the accuracy of the expected annual income by referring to related market data during calculation. The calculation unit, for example, calculates the expected annual income by referring to annual income data of the related market. For example, the calculation unit calculates the expected annual income by referring to the annual income data of the related market, and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income by referring to growth rate data of the related market. For example, the calculation unit calculates the expected annual income by referring to the growth rate data of the related market, and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income by referring to supply and demand data of the related market. For example, the calculation unit calculates the expected annual income by referring to the demand and supply data of the related market, and reflects it in the proposed job change. In this way, the accuracy of the expected annual income can be improved by referring to the related market data.

[0048] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the business type or industry. For example, the proposal unit makes a detailed proposal for a business type or industry with high importance. For example, the proposal unit makes a detailed proposal for a business type or industry with high importance and provides it to the job seeker. The proposal unit can also make a simplified proposal for a business type or industry with low importance. For example, the proposal unit makes a simplified proposal for a business type or industry with low importance and provides it to the job seeker. Furthermore, the proposal unit can also make a proposal with an appropriate level of detail for a business type or industry with medium importance. For example, the proposal unit makes a proposal with an appropriate level of detail for a business type or industry with medium importance and provides it to the job seeker. In this way, the level of detail of the proposal can be adjusted based on the importance of the business type or industry.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the business type or industry category. For example, the proposal unit applies a proposal algorithm based on technical skills to a technical business type. For example, the proposal unit applies a proposal algorithm based on the technical skills to a technical business type and provides it to the job seeker. The proposal unit can also apply a proposal algorithm based on leadership to a managerial business type. For example, the proposal unit applies a proposal algorithm based on the leadership to a managerial business type and provides it to the job seeker. Furthermore, the proposal unit can also apply a proposal algorithm based on customer service skills to a service business type. For example, the proposal unit applies a proposal algorithm based on the customer service skills to a service business and provides it to the job seeker. This makes it possible to apply an appropriate proposal algorithm depending on the business type or industry category.

[0050] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the industry or the industry. The proposal unit, for example, makes a proposal based on recently submitted information on the industry or the industry. For example, the proposal unit makes the proposal based on recently submitted information on the industry or the industry and provides it to the job seeker. The proposal unit can also postpone making a proposal for older information. For example, the proposal unit postpones making the proposal for older information and provides it to the job seeker. Furthermore, the proposal unit can also make a proposal with moderate priority for information that was submitted recently. For example, the proposal unit moderately prioritizes information that was submitted recently and provides it to the job seeker. This makes it possible to determine the priority of the proposal based on the time of submission of the industry or the industry.

[0051] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the business type or industry. For example, the proposal unit prioritizes proposals for business types or industries with high relevance. For example, the proposal unit makes prioritized proposals for business types or industries with high relevance and provides them to the job seeker. The proposal unit can also postpone proposals for business types or industries with low relevance. For example, the proposal unit makes postponed proposals for business types or industries with low relevance and provides them to the job seeker. Furthermore, the proposal unit can also give moderate priority to proposals for business types or industries with medium relevance. For example, the proposal unit makes moderate priority proposals for business types or industries with medium relevance and provides them to the job seeker. In this way, the order of proposals can be adjusted based on the relevance of the business types or industries.

[0052] When drawing out an appeal, the appeal drawing unit can select the optimal drawing method by referring to past data. The appeal drawing unit, for example, selects the optimal method for drawing out an appeal by referring to data on past job-changers. For example, the appeal drawing unit references data on past job-changers to select the optimal method for drawing out the appeal, thereby drawing out new appeal in job-changers. The appeal drawing unit can also select the optimal method for drawing out an appeal by referring to past market trend data. For example, the appeal drawing unit references past market trend data to select the optimal method for drawing out the appeal, thereby drawing out new appeal in job-changers. Furthermore, the appeal drawing unit can also select the optimal method for drawing out an appeal by referring to past industry-specific data. For example, the appeal drawing unit references past industry-specific data to select the optimal method for drawing out the appeal, thereby drawing out new appeal in job-changers. In this way, the optimal method for drawing out an appeal can be selected by referring to past data.

[0053] The attractiveness elicitation unit can select the optimal method of eliciting attractiveness by taking into consideration the geographical location information of the job seeker when eliciting the attractiveness. For example, if the job seeker lives in an urban area, the attractiveness elicitation unit selects a method of eliciting the attractiveness of the urban area. For example, if the job seeker lives in an urban area, the attractiveness elicitation unit selects a method of eliciting the attractiveness of the urban area to elicit new attractiveness from the job seeker. Furthermore, if the job seeker lives in a rural area, the attractiveness elicitation unit can select a method of eliciting the attractiveness of the rural area to elicit new attractiveness from the job seeker. Furthermore, if the job seeker wishes to change jobs overseas, the attractiveness elicitation unit can select a method of eliciting the attractiveness of overseas locations. For example, if the job seeker wishes to change jobs overseas, the attractiveness elicitation unit selects a method of eliciting the attractiveness of overseas locations to elicit new attractiveness from the job seeker. This allows the optimal method of eliciting attractiveness to be selected based on the geographical location information of the job seeker.

[0054] The updating unit can select the optimal update method by referring to past data when updating. The updating unit, for example, selects the optimal update method by referring to data on past job-changers. For example, the updating unit references data on past job-changers, selects the optimal update method, and provides it to the job seeker. The updating unit can also select the optimal update method by referring to past market trend data. For example, the updating unit references past market trend data, selects the optimal update method, and provides it to the job seeker. The updating unit can also select the optimal update method by referring to past industry-specific data. For example, the updating unit references past industry-specific data, selects the optimal update method, and provides it to the job seeker. In this way, the optimal update method can be selected by referring to past data.

[0055] When updating, the update unit can select the optimal update method by taking into consideration the geographical location information of the job seeker. For example, if the job seeker lives in an urban area, the update unit prioritizes updating information about the urban area. For example, if the job seeker lives in an urban area, the update unit prioritizes updating information about the urban area and provides it to the job seeker. The update unit can also prioritize updating information about the rural area if the job seeker lives in a rural area. For example, if the job seeker lives in a rural area, the update unit prioritizes updating information about the rural area and provides it to the job seeker. Furthermore, if the job seeker wishes to change jobs overseas, the update unit can also prioritize updating information about overseas areas. For example, if the job seeker wishes to change jobs overseas, the update unit prioritizes updating information about the overseas area and provides it to the job seeker. This makes it possible to select the optimal update method based on the geographical location information of the job seeker.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The proposal department can customize job suggestions based on the job seeker's past work history. For example, if the job seeker has experience as an engineer, the proposal department can suggest a job where the job seeker can utilize his or her technical skills. Also, if the job seeker has experience as a manager, the proposal department can suggest a job where the job seeker can utilize his or her leadership and management skills. Furthermore, if the job seeker has worked in a variety of industries, the proposal department can suggest a job where the job seeker can utilize that diverse experience. This makes it possible to propose more appropriate job suggestions based on the job seeker's past work history.

[0058] The analysis unit can make job suggestions that take into account the hobbies and interests of job seekers. For example, if a job seeker is interested in outdoor activities, it can suggest industries and fields that allow them to utilize that hobby. Also, if a job seeker is interested in art or design, it can suggest creative jobs that allow them to utilize that interest. Furthermore, if a job seeker has a strong interest in technology, it can suggest IT-related jobs that allow them to utilize that interest. This makes it possible to make suggestions that take into account the hobbies and interests of job seekers.

[0059] The collection unit can analyze the social media activities of job seekers and collect related information. For example, if a job seeker is very active on LinkedIn, the collection unit can collect LinkedIn profile information. Also, if a job seeker posts a lot on X (formerly Twitter), the collection unit can analyze the content of X's (formerly Twitter) posts and collect related information. Furthermore, if a job seeker is very active on Facebook, the collection unit can analyze the content of Facebook posts and collect related information. In this way, related information can be collected based on the job seeker's social media activities.

[0060] The estimation unit can suggest job opportunities by taking into consideration the geographical location information of the job seeker. For example, if the job seeker lives in an urban area, it can prioritize suggesting job information in urban areas. Also, if the job seeker lives in a rural area, it can prioritize suggesting job information in rural areas. Furthermore, if the job seeker wishes to change jobs overseas, it can prioritize suggesting job information overseas. This makes it possible to suggest more appropriate job opportunities based on the geographical location information of the job seeker.

[0061] The calculation unit can calculate the expected annual income by referring to the past annual income data of the job seeker. For example, the calculation unit calculates the expected annual income of the job seeker by referring to the annual income data of past job seekers. The calculation unit can also calculate the expected annual income by referring to past market trend data. Furthermore, the calculation unit can also calculate the expected annual income by referring to past industry-specific annual income data. In this way, by referring to past data, the accuracy of the expected annual income can be improved.

[0062] The processing flow of the first embodiment will be briefly explained below.

[0063] Step 1: The collection department collects information on job seekers. This information includes work history, skills, and desired conditions. For example, if a job seeker has previously worked as an engineer, information on the job duties, qualifications obtained, and personality traits will be collected. If the job seeker has experience as a manager, information on leadership and management will also be collected. Furthermore, if the job seeker has worked in a variety of industries, information related to each industry will be collected in a balanced manner. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the job history, skills, and desired conditions of the job seeker to estimate the most suitable industry or business for the job seeker. If the job seeker has experience as an engineer, it can estimate not only the IT industry but also other industries such as manufacturing and services. Step 3: The estimation unit estimates the most suitable business or industry based on the analysis results obtained by the analysis unit. For example, it makes the estimation by taking into account the correlation between the job seeker's skills and experience, the correlation between qualifications and personality traits, and the correlation between past work history and current living situation. Step 4: The calculation unit calculates the expected annual income in the business type and industry estimated by the estimation unit. For example, the calculation unit calculates the expected annual income by referring to the annual income data of past job-changers, past market trend data, and annual income data by industry. Step 5: The proposal unit proposes the optimal job change based on the expected annual salary calculated by the calculation unit. For example, it proposes a job change that allows the job seeker to take on a new industry while utilizing their experience as an engineer, a job change that brings out new strengths in the job seeker, or a job change that is optimal according to the job seeker's desired conditions and living situation.

[0064] (Example 2) A staffing system according to an embodiment of the present invention collects a wide range of information about job seekers, analyzes it using generative AI technology, and proposes optimal job opportunities. This system collects information about job seekers, such as their abilities, experience, qualifications, and personality, and uses generative AI to analyze it to predict the industry and job type that best suits them. Furthermore, the generative AI calculates the expected annual salary in the predicted industry and industry and proposes it to the job seeker. This system allows job seekers to consider completely different industries and jobs that they may not have known about. The generative AI also draws out new attractive features of job seekers and proposes optimal job opportunities. For example, detailed information about job seekers' abilities, experience, qualifications, and personality is collected. This information includes detailed data about the job seeker's past work, qualifications, and personality traits. For example, if the job seeker previously worked as an engineer, the system collects information about the job duties, qualifications, and personality traits. The generative AI then analyzes the collected information. Based on the collected information, the generative AI predicts the industry and job type that best suits the job seeker. For example, for a job seeker with engineering experience, the generative AI can predict not only the IT industry but also other industries such as manufacturing and services. Furthermore, the generative AI calculates the expected annual salary in the predicted industry and industry. Based on past data and market trends, the generative AI calculates the job seeker's expected annual salary if they change jobs. For example, it calculates the expected annual salary if they change jobs to the IT industry or the manufacturing industry. Finally, the generative AI suggests the job seeker's optimal job. Based on the predicted industry, industry, and expected annual salary, the generative AI suggests the job seeker's optimal job. For example, it suggests a job where the job seeker can utilize their engineering experience while trying a new industry. This system allows the job seeker to consider a completely different industry or field that they may not have known about. The generative AI also brings out the new appeal of the job seeker and suggests the optimal job. This enables the staffing system to efficiently collect, analyze, estimate, calculate, and suggest information about job seekers.

[0065] A staffing system according to an embodiment includes a collection unit, an analysis unit, an estimation unit, a calculation unit, and a proposal unit. The collection unit collects information about job seekers. The information about job seekers includes, but is not limited to, their work history, skills, and desired conditions. For example, if the job seeker has previously worked as an engineer, the collection unit collects information about the job seeker's work, qualifications, and personality traits. If the job seeker has managerial experience, the collection unit can also collect information about leadership and management. Furthermore, if the job seeker has worked in various industries, the collection unit can collect information related to each industry in a balanced manner. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit analyzes the job seeker's work history, skills, desired conditions, and other information to estimate the industry or business that is most suitable for the job seeker. For example, if the job seeker has experience as an engineer, the analysis unit can estimate not only the IT industry but also other industries such as manufacturing and services. The estimation unit estimates the industry or business that is most suitable for the job seeker based on the analysis results obtained by the analysis unit. The estimation unit makes the estimation, for example, by taking into account the correlation between the job seeker's skills and experience. The estimation unit can also make the estimation by taking into account the correlation between the job seeker's qualifications and personality traits. Furthermore, the estimation unit can also make the estimation by taking into account the correlation between the job seeker's past work history and current living situation. The calculation unit calculates the expected annual income in the business type or industry estimated by the estimation unit. The calculation unit, for example, calculates the expected annual income by referring to annual income data of past job seekers. The calculation unit can also calculate the expected annual income by referring to past market trend data. Furthermore, the calculation unit can calculate the expected annual income by referring to past annual income data by industry. The proposal unit proposes an optimal job change based on the expected annual income calculated by the calculation unit. For example, the proposal unit proposes a job change where the job seeker can take on a new industry while utilizing their experience as an engineer. The proposal unit can also bring out the job seeker's new appeal and propose an optimal job change. Furthermore, the proposal unit can propose an optimal job change based on the job seeker's desired conditions and living situation.As a result, the temporary staffing system according to the embodiment can efficiently collect, analyze, estimate, calculate, and propose information on job seekers.

[0066] Furthermore, the temporary staffing system is equipped with an attractiveness-drawing unit that draws out new attractiveness in job seekers. The attractiveness-drawing unit, for example, discovers new skills and potential abilities in job seekers and, based on that, suggests optimal job opportunities. For example, the attractiveness-drawing unit finds opportunities where job seekers can utilize their skills and abilities, even in industries or fields they have not previously worked in. The attractiveness-drawing unit can also draw out new attractiveness based on the job seeker's personality traits and interests. For example, it can identify situations where job seekers can demonstrate leadership or creativity, and suggest job opportunities based on those. Furthermore, the attractiveness-drawing unit can draw out new attractiveness based on the job seeker's past work experience and qualifications. For example, if a job seeker has experience as an engineer, that experience can be used to draw out the attractiveness of a new industry or field. This allows the job seeker to draw out new attractiveness and suggest optimal job opportunities.

[0067] The staffing system also includes an update unit that updates information about job seekers. The update unit, for example, periodically updates the information about job seekers. For example, the update unit periodically collects newly acquired qualifications, skills, experience, and the like from job seekers, and keeps the information up to date. The update unit can also update the information in response to changes in the job seeker's living situation or desired conditions. For example, if the job seeker starts a new family, the update unit collects that information and reflects it in job proposals. The update unit can also update the information in response to changes in the job seeker's emotions and interests. For example, the update unit collects information about new industries or fields in which the job seeker has an interest, and reflects it in job proposals. This allows the job seeker's information to be kept up to date.

[0068] The collection unit can estimate the emotions of the job seeker and adjust the timing of information collection based on the estimated emotions. For example, if the job seeker is feeling stressed, the collection unit delays information collection until the job seeker is in a relaxed state. For example, the collection unit can collect more accurate information by providing information when the job seeker is in a relaxed state. Furthermore, if the job seeker is excited, the collection unit can immediately start information collection and quickly acquire data. For example, the collection unit can collect more information by providing information when the job seeker is excited. Furthermore, if the job seeker is tired, the collection unit can resume information collection after the job seeker has rested. For example, the collection unit can collect more accurate information by providing information when the job seeker is tired. This makes it possible to adjust the timing of information collection according to the emotions of the job seeker.

[0069] The collection unit can analyze the job seeker's past work history and select the optimal information collection method. For example, if the job seeker has worked as an engineer in the past, the collection unit prioritizes collecting information about technical skills. For example, if the job seeker has experience as an engineer, the collection unit collects information about the job seeker's technical skills and reflects it in job proposals. In addition, if the job seeker has experience as a manager, the collection unit can also collect information about leadership and management. For example, if the job seeker has experience as a manager, the collection unit collects information about the job seeker's leadership and management and reflects it in job proposals. Furthermore, if the job seeker has worked in various industries, the collection unit can collect information related to each industry in a balanced manner. For example, if the job seeker has worked in various industries, the collection unit collects information related to each industry and reflects it in job proposals. This makes it possible to select the optimal information collection method based on the job seeker's past work history.

[0070] When collecting information, the collection unit can filter the information based on the job seeker's current living situation and areas of interest. For example, if the job seeker has a family, the collection unit collects information that takes into account the balance between family and work. For example, if the job seeker has a family, the collection unit collects information that takes into account the balance between family and work and reflects it in job suggestions. In addition, if the job seeker has a specific hobby or interest, the collection unit can prioritize collecting information related to that field. For example, if the job seeker has a specific hobby or interest, the collection unit collects information related to that field and reflects it in job suggestions. Furthermore, the collection unit can collect information regarding remote work or flexible working arrangements depending on the job seeker's current living situation. For example, if the job seeker desires remote work or flexible working arrangements, the collection unit collects that information and reflects it in job suggestions. This makes it possible to filter information based on the job seeker's living situation and areas of interest.

[0071] The collection unit can estimate the emotions of the job seeker and determine the priority of information to be collected based on the estimated emotions. For example, if the job seeker is feeling anxious, the collection unit prioritizes collecting information that gives the job seeker a sense of security. For example, if the job seeker is feeling anxious, the collection unit collects information that gives the job seeker a sense of security and reflects it in job suggestions. Furthermore, if the job seeker is excited, the collection unit can prioritize collecting information about new challenges. For example, if the job seeker is excited, the collection unit collects information about the new challenge and reflects it in job suggestions. Furthermore, if the job seeker is relaxed, the collection unit can collect detailed information and provide a wide range of options. For example, if the job seeker is relaxed, the collection unit collects detailed information and reflects it in job suggestions. In this way, the priority of information can be determined according to the emotions of the job seeker.

[0072] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into consideration the geographical location information of the job seeker. For example, if the job seeker lives in an urban area, the collection unit prioritizes collecting job information for the urban area. For example, if the job seeker lives in an urban area, the collection unit collects job information for the urban area and reflects it in job suggestions. Furthermore, if the job seeker lives in a rural area, the collection unit can prioritize collecting job information for the rural area. For example, if the job seeker lives in a rural area, the collection unit collects job information for the rural area and reflects it in job suggestions. Furthermore, if the job seeker wishes to change jobs overseas, the collection unit can prioritize collecting overseas job information. For example, if the job seeker wishes to change jobs overseas, the collection unit collects overseas job information and reflects it in job suggestions. In this way, highly relevant information can be collected based on the geographical location information of the job seeker.

[0073] When collecting information, the collection unit can analyze the social media activities of job seekers and collect related information. For example, if the job seeker is active on LinkedIn, the collection unit collects LinkedIn profile information. For example, if the job seeker is active on LinkedIn, the collection unit collects the profile information and reflects it in job suggestions. Furthermore, if the job seeker posts frequently on X (formerly Twitter), the collection unit can analyze the content of posts on X (formerly Twitter) and collect related information. For example, if the job seeker posts frequently on X (formerly Twitter), the collection unit can analyze the content of posts and reflect it in job suggestions. Furthermore, if the job seeker is active on Facebook, the collection unit can analyze the content of posts on Facebook and collect related information. For example, if the job seeker is active on Facebook, the collection unit can analyze the content of posts and reflect it in job suggestions. In this way, related information can be collected based on the job seeker's social media activities.

[0074] The analysis unit can estimate the emotions of the job seeker and adjust the way the analysis is presented based on the estimated emotions. For example, if the job seeker is nervous, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the job seeker is nervous, the analysis unit provides the simple and easy-to-understand analysis result and reflects it in the job proposal. Furthermore, the analysis unit can also provide a detailed analysis result if the job seeker is relaxed. For example, if the job seeker is relaxed, the analysis unit provides the detailed analysis result and reflects it in the job proposal. Furthermore, the analysis unit can also provide a visually appealing analysis result if the job seeker is excited. For example, if the job seeker is excited, the analysis unit provides the visually appealing analysis result and reflects it in the job proposal. In this way, the way the analysis is presented can be adjusted according to the emotions of the job seeker.

[0075] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the collected information. The analysis unit, for example, performs a detailed analysis on information of high importance. For example, the analysis unit performs a detailed analysis on information of high importance and reflects the result in job suggestions. The analysis unit can also perform a simplified analysis on information of low importance. For example, the analysis unit performs a simplified analysis on information of low importance and reflects the result in job suggestions. Furthermore, the analysis unit can also perform an analysis with a moderate level of detail on information of medium importance. For example, the analysis unit performs an analysis with a moderate level of detail on information of medium importance and reflects the result in job suggestions. In this way, the level of detail of the analysis can be adjusted based on the importance of the collected information.

[0076] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit applies a technical skill analysis algorithm to information regarding technical skills. For example, the analysis unit applies the technical skill analysis algorithm to the information regarding technical skills and reflects it in job suggestions. The analysis unit can also apply a leadership analysis algorithm to information regarding managerial experience. For example, the analysis unit applies the leadership analysis algorithm to information regarding managerial experience and reflects it in job suggestions. Furthermore, the analysis unit can also apply a personality analysis algorithm to information regarding personality traits. For example, the analysis unit applies the personality analysis algorithm to information regarding personality traits and reflects it in job suggestions. This allows an appropriate analysis algorithm to be applied depending on the category of information.

[0077] The analysis unit can estimate the emotions of the job seeker and adjust the length of the analysis based on the estimated emotions. For example, if the job seeker is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, if the job seeker is in a hurry, the analysis unit provides the short and to-the-point analysis result and reflects it in the job proposal. The analysis unit can also provide a detailed analysis result if the job seeker is relaxed. For example, if the job seeker is relaxed, the analysis unit provides the detailed analysis result and reflects it in the job proposal. Furthermore, the analysis unit can also provide a visually appealing analysis result if the job seeker is excited. For example, if the job seeker is excited, the analysis unit provides the visually appealing analysis result and reflects it in the job proposal. In this way, the length of the analysis can be adjusted according to the emotions of the job seeker.

[0078] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of the information. The analysis unit, for example, prioritizes analysis of recently submitted information. For example, the analysis unit performs a prioritized analysis of recently submitted information and reflects the results in job placement proposals. The analysis unit can also postpone analysis of older information. For example, the analysis unit performs a postponed analysis of older information and reflects the results in job placement proposals. Furthermore, the analysis unit can also give moderate priority to analysis of information that was submitted at a medium time. For example, the analysis unit performs moderate priority analysis of information that was submitted at a medium time and reflects the results in job placement proposals. In this way, the priority of analysis can be determined based on the time of submission of the information.

[0079] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit, for example, prioritizes analysis of information with high relevance. For example, the analysis unit performs a prioritized analysis of information with high relevance and reflects the results in job placement suggestions. The analysis unit can also postpone analysis of information with low relevance. For example, the analysis unit performs a postponed analysis of information with low relevance and reflects the results in job placement suggestions. Furthermore, the analysis unit can also give moderate priority to analysis of information with medium relevance. For example, the analysis unit performs moderate priority analysis of information with medium relevance and reflects the results in job placement suggestions. This allows the order of analysis to be adjusted based on the relevance of the information.

[0080] The estimation unit can estimate the emotions of the job seeker and adjust the estimation criteria based on the estimated emotions. For example, if the job seeker is feeling anxious, the estimation unit applies a conservative estimation criterion. For example, if the job seeker is feeling anxious, the estimation unit applies the conservative estimation criterion and reflects it in the job proposal. The estimation unit can also apply a standard estimation criterion if the job seeker is relaxed. For example, if the job seeker is relaxed, the estimation unit applies the standard estimation criterion and reflects it in the job proposal. Furthermore, the estimation unit can also apply a challenging estimation criterion if the job seeker is excited. For example, if the job seeker is excited, the estimation unit applies the challenging estimation criterion and reflects it in the job proposal. In this way, the estimation criteria can be adjusted according to the emotions of the job seeker.

[0081] The estimation unit can improve the accuracy of the estimation by taking into account the interrelationship between information during estimation. The estimation unit makes the estimation by taking into account, for example, the interrelationship between the skills and experience of the job seeker. For example, the estimation unit makes the estimation by taking into account the interrelationship between the skills and experience of the job seeker and reflects it in the job change proposal. The estimation unit can also make the estimation by taking into account the interrelationship between the qualifications and personality traits of the job seeker. For example, the estimation unit makes the estimation by taking into account the interrelationship between the qualifications and personality traits of the job seeker and reflects it in the job change proposal. Furthermore, the estimation unit can also make the estimation by taking into account the interrelationship between the job seeker's past work history and current living situation. For example, the estimation unit makes the estimation by taking into account the interrelationship between the job seeker's past work history and current living situation and reflects it in the job change proposal. In this way, the accuracy of the estimation can be improved by taking into account the interrelationship between information.

[0082] When making an estimation, the estimation unit can make the estimation taking into account attribute information of the job change candidate. The estimation unit makes the estimation taking into account, for example, the age of the job change candidate. For example, the estimation unit makes the estimation taking into account the age of the job change candidate and reflects this in the proposed job change candidate. The estimation unit can also make the estimation taking into account the gender of the job change candidate. For example, the estimation unit makes the estimation taking into account the gender of the job change candidate and reflects this in the proposed job change candidate. Furthermore, the estimation unit can also make the estimation taking into account the educational background of the job change candidate. For example, the estimation unit makes the estimation taking into account the educational background of the job change candidate and reflects this in the proposed job change candidate. In this way, estimation can be made taking into account attribute information of the job change candidate.

[0083] The estimation unit can estimate the emotions of the job seeker and adjust the order in which the estimation results are displayed based on the estimated emotions. For example, if the job seeker is feeling anxious, the estimation unit first displays results that give a sense of security. For example, if the job seeker is feeling anxious, the estimation unit first displays results that give a sense of security and reflects this in the job placement proposals. The estimation unit can also display detailed results in an orderly manner if the job seeker is relaxed. For example, if the job seeker is feeling relaxed, the estimation unit displays detailed results in an orderly manner and reflects this in the job placement proposals. Furthermore, if the job seeker is excited, the estimation unit can also display visually appealing results first. For example, if the job seeker is excited, the estimation unit displays visually appealing results first and reflects this in the job placement proposals. This makes it possible to adjust the order in which the estimation results are displayed according to the emotions of the job seeker.

[0084] The estimation unit can make the estimation taking into account the geographical distribution of information. For example, if the job seeker lives in an urban area, the estimation unit prioritizes job information in the urban area. For example, if the job seeker lives in an urban area, the estimation unit prioritizes job information in the urban area and reflects this in the job change proposal. Furthermore, if the job seeker lives in a rural area, the estimation unit can also prioritize job information in the rural area. For example, if the job seeker lives in a rural area, the estimation unit prioritizes job information in the rural area and reflects this in the job change proposal. Furthermore, if the job seeker wishes to change jobs overseas, the estimation unit can also prioritize job information overseas. For example, if the job seeker wishes to change jobs overseas, the estimation unit prioritizes job information overseas and reflects this in the job change proposal. This makes it possible to make the estimation taking into account the geographical distribution of information.

[0085] The estimation unit can improve the accuracy of the estimation by referring to related literature when making the estimation. The estimation unit, for example, makes the estimation by referring to the latest research papers related to the skills of the job seeker. For example, the estimation unit makes the estimation by referring to the latest research papers related to the skills of the job seeker, and reflects the estimation in the proposed job change. The estimation unit can also make the estimation by referring to market reports related to the job seeker's industry. For example, the estimation unit makes the estimation by referring to market reports related to the job seeker's industry, and reflects the estimation in the proposed job change. Furthermore, the estimation unit can also make the estimation by referring to specialized books related to the qualifications of the job seeker. For example, the estimation unit makes the estimation by referring to specialized books related to the qualifications of the job seeker, and reflects the estimation in the proposed job change. In this way, the accuracy of the estimation can be improved by referring to related literature.

[0086] The calculation unit can estimate the emotions of the job change candidate and adjust the calculation method of the expected annual income based on the estimated emotions. For example, if the job change candidate is feeling anxious, the calculation unit calculates a conservative expected annual income. For example, if the job change candidate is feeling anxious, the calculation unit calculates the conservative expected annual income and reflects it in the proposed job change. The calculation unit can also calculate a standard expected annual income if the job change candidate is relaxed. For example, if the job change candidate is relaxed, the calculation unit calculates the standard expected annual income and reflects it in the proposed job change. Furthermore, the calculation unit can also calculate a challenging expected annual income if the job change candidate is excited. For example, if the job change candidate is excited, the calculation unit calculates the challenging expected annual income and reflects it in the proposed job change. This makes it possible to adjust the calculation method of the expected annual income according to the emotions of the job change candidate.

[0087] The calculation unit can improve the accuracy of the expected annual income by referring to past data when calculating. The calculation unit, for example, calculates the expected annual income by referring to annual income data of past job changers. For example, the calculation unit calculates the expected annual income by referring to annual income data of past job changers, and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income by referring to past market trend data. For example, the calculation unit calculates the expected annual income by referring to past market trend data, and reflects it in the proposed job change. Furthermore, the calculation unit can also calculate the expected annual income by referring to past industry-specific annual income data. For example, the calculation unit calculates the expected annual income by referring to past industry-specific annual income data, and reflects it in the proposed job change. In this way, the accuracy of the expected annual income can be improved by referring to past data.

[0088] The calculation unit can calculate the expected annual income taking into account market trends in the industry when calculating. The calculation unit, for example, calculates the expected annual income taking into account current market trends. For example, the calculation unit calculates the expected annual income taking into account current market trends and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income based on a future market forecast. For example, the calculation unit calculates the expected annual income based on a future market forecast and reflects it in the proposed job change. Furthermore, the calculation unit can also calculate the expected annual income taking into account the growth rate of the industry. For example, the calculation unit calculates the expected annual income taking into account the growth rate of the industry and reflects it in the proposed job change. In this way, the expected annual income can be calculated taking into account market trends in the industry.

[0089] The calculation unit can estimate the emotion of the job seeker and adjust the display method of the expected annual income based on the estimated emotion. For example, if the job seeker is feeling anxious, the calculation unit provides a display method that gives a sense of security. For example, if the job seeker is feeling anxious, the calculation unit provides a display method that gives a sense of security and reflects this in the proposed job change. Furthermore, the calculation unit can also provide a detailed display method if the job seeker is relaxed. For example, if the job seeker is feeling relaxed, the calculation unit provides a detailed display method and reflects this in the proposed job change. Furthermore, the calculation unit can also provide a visually appealing display method if the job seeker is excited. For example, if the job seeker is excited, the calculation unit provides a visually appealing display method and reflects this in the proposed job change. In this way, the display method of the expected annual income can be adjusted according to the emotion of the job seeker.

[0090] During calculation, the calculation unit can determine the priority of expected annual income based on the time of submission of information. The calculation unit, for example, calculates the expected annual income based on the most recently submitted information. For example, the calculation unit calculates the expected annual income based on the most recently submitted information and reflects it in the job change proposal. The calculation unit can also calculate the expected annual income by putting older information on hold. For example, the calculation unit calculates the expected annual income by putting older information on hold and reflects it in the job change proposal. Furthermore, the calculation unit can calculate the expected annual income by giving appropriate priority to information submitted at a medium time. For example, the calculation unit calculates the expected annual income by giving appropriate priority to information submitted at a medium time and reflects it in the job change proposal. In this way, the priority of expected annual income can be determined based on the time of submission of information.

[0091] The calculation unit can improve the accuracy of the expected annual income by referring to related market data during calculation. The calculation unit, for example, calculates the expected annual income by referring to annual income data of the related market. For example, the calculation unit calculates the expected annual income by referring to the annual income data of the related market, and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income by referring to growth rate data of the related market. For example, the calculation unit calculates the expected annual income by referring to the growth rate data of the related market, and reflects it in the proposed job change. The calculation unit can also calculate the expected annual income by referring to supply and demand data of the related market. For example, the calculation unit calculates the expected annual income by referring to the demand and supply data of the related market, and reflects it in the proposed job change. In this way, the accuracy of the expected annual income can be improved by referring to the related market data.

[0092] The suggestion unit can estimate the emotion of the job seeker and adjust the way the proposal is expressed based on the estimated emotion. For example, if the job seeker is feeling anxious, the suggestion unit provides an expression that gives a sense of security. For example, if the job seeker is feeling anxious, the suggestion unit provides an expression that gives a sense of security and reflects this in the proposal for new jobs. Furthermore, the suggestion unit can also provide a detailed expression if the job seeker is relaxed. For example, if the job seeker is feeling relaxed, the suggestion unit provides a detailed expression if the job seeker is relaxed and reflects this in the proposal for new jobs. Furthermore, the suggestion unit can also provide a visually appealing expression if the job seeker is excited. For example, if the job seeker is excited, the suggestion unit provides a visually appealing expression if the job seeker is excited and reflects this in the proposal for new jobs. In this way, the way the proposal is expressed can be adjusted according to the emotion of the job seeker.

[0093] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the business type or industry. For example, the proposal unit makes a detailed proposal for a business type or industry with high importance. For example, the proposal unit makes a detailed proposal for a business type or industry with high importance and provides it to the job seeker. The proposal unit can also make a simplified proposal for a business type or industry with low importance. For example, the proposal unit makes a simplified proposal for a business type or industry with low importance and provides it to the job seeker. Furthermore, the proposal unit can also make a proposal with an appropriate level of detail for a business type or industry with medium importance. For example, the proposal unit makes a proposal with an appropriate level of detail for a business type or industry with medium importance and provides it to the job seeker. In this way, the level of detail of the proposal can be adjusted based on the importance of the business type or industry.

[0094] When making a proposal, the proposal unit can apply different proposal algorithms depending on the business type or industry category. For example, the proposal unit applies a proposal algorithm based on technical skills to a technical business type. For example, the proposal unit applies a proposal algorithm based on the technical skills to a technical business type and provides it to the job seeker. The proposal unit can also apply a proposal algorithm based on leadership to a managerial business type. For example, the proposal unit applies a proposal algorithm based on the leadership to a managerial business type and provides it to the job seeker. Furthermore, the proposal unit can also apply a proposal algorithm based on customer service skills to a service business type. For example, the proposal unit applies a proposal algorithm based on the customer service skills to a service business and provides it to the job seeker. This makes it possible to apply an appropriate proposal algorithm depending on the business type or industry category.

[0095] The suggestion unit can estimate the emotion of the job seeker and adjust the length of the proposal based on the estimated emotion. For example, if the job seeker is in a hurry, the suggestion unit makes a short and to-the-point proposal. For example, if the job seeker is in a hurry, the suggestion unit makes the short and to-the-point proposal and provides it to the job seeker. Furthermore, the suggestion unit can make a detailed proposal if the job seeker is relaxed. For example, if the job seeker is relaxed, the suggestion unit makes the detailed proposal and provides it to the job seeker. Furthermore, the suggestion unit can make a visually appealing proposal if the job seeker is excited. For example, if the job seeker is excited, the suggestion unit makes the visually appealing proposal and provides it to the job seeker. In this way, the length of the proposal can be adjusted according to the emotion of the job seeker.

[0096] When making a proposal, the proposal unit can determine the priority of the proposal based on the time of submission of the industry or the industry. The proposal unit, for example, makes a proposal based on recently submitted information on the industry or the industry. For example, the proposal unit makes the proposal based on recently submitted information on the industry or the industry and provides it to the job seeker. The proposal unit can also postpone making a proposal for older information. For example, the proposal unit postpones making the proposal for older information and provides it to the job seeker. Furthermore, the proposal unit can also make a proposal with moderate priority for information that was submitted recently. For example, the proposal unit moderately prioritizes information that was submitted recently and provides it to the job seeker. This makes it possible to determine the priority of the proposal based on the time of submission of the industry or the industry.

[0097] When making a proposal, the proposal unit can adjust the order of proposals based on the relevance of the business type or industry. For example, the proposal unit prioritizes proposals for business types or industries with high relevance. For example, the proposal unit makes prioritized proposals for business types or industries with high relevance and provides them to the job seeker. The proposal unit can also postpone proposals for business types or industries with low relevance. For example, the proposal unit makes postponed proposals for business types or industries with low relevance and provides them to the job seeker. Furthermore, the proposal unit can also give moderate priority to proposals for business types or industries with medium relevance. For example, the proposal unit makes moderate priority proposals for business types or industries with medium relevance and provides them to the job seeker. In this way, the order of proposals can be adjusted based on the relevance of the business types or industries.

[0098] The attractiveness elicitation unit can estimate the emotions of the job change candidate and adjust a new attractiveness elicitation method based on the estimated emotions. For example, if the job change candidate is feeling anxious, the attractiveness elicitation unit provides a method for eliciting an attractiveness that gives a sense of security. For example, if the job change candidate is feeling anxious, the attractiveness elicitation unit provides a method for eliciting an attractiveness that gives a sense of security, thereby eliciting a new attractiveness in the job change candidate. Furthermore, if the job change candidate is relaxed, the attractiveness elicitation unit can provide a detailed method for eliciting an attractiveness. For example, if the job change candidate is relaxed, the attractiveness elicitation unit provides a detailed method for eliciting an attractiveness, thereby eliciting a new attractiveness in the job change candidate. Furthermore, if the job change candidate is excited, the attractiveness elicitation unit can provide a visually attractive method for eliciting an attractiveness. For example, if the job change candidate is excited, the attractiveness elicitation unit provides a visually attractive method for eliciting an attractiveness, thereby eliciting a new attractiveness in the job change candidate. This makes it possible to adjust a new attractiveness elicitation method according to the emotions of the job change candidate.

[0099] When drawing out an appeal, the appeal drawing unit can select the optimal drawing method by referring to past data. The appeal drawing unit, for example, selects the optimal method for drawing out an appeal by referring to data on past job-changers. For example, the appeal drawing unit references data on past job-changers to select the optimal method for drawing out the appeal, thereby drawing out new appeal in job-changers. The appeal drawing unit can also select the optimal method for drawing out an appeal by referring to past market trend data. For example, the appeal drawing unit references past market trend data to select the optimal method for drawing out the appeal, thereby drawing out new appeal in job-changers. Furthermore, the appeal drawing unit can also select the optimal method for drawing out an appeal by referring to past industry-specific data. For example, the appeal drawing unit references past industry-specific data to select the optimal method for drawing out the appeal, thereby drawing out new appeal in job-changers. In this way, the optimal method for drawing out an appeal can be selected by referring to past data.

[0100] The attraction elicitation unit can estimate the emotions of the job change candidate and determine the priority of new attractions based on the estimated emotions. For example, if the job change candidate is feeling anxious, the attraction elicitation unit preferentially elicits attraction that gives a sense of security. For example, if the job change candidate is feeling anxious, the attraction elicitation unit preferentially elicits attraction that gives a sense of security, thereby eliciting a new attraction from the job change candidate. Furthermore, if the job change candidate is relaxed, the attraction elicitation unit can preferentially elicit detailed attraction. For example, if the job change candidate is relaxed, the attraction elicitation unit preferentially elicits detailed attraction, thereby eliciting a new attraction from the job change candidate. Furthermore, if the job change candidate is excited, the attraction elicitation unit can preferentially elicit visually attractive attraction. For example, if the job change candidate is excited, the attraction elicitation unit preferentially elicits visually attractive attraction, thereby eliciting a new attraction from the job change candidate. In this way, the priority of new attractions can be determined according to the emotions of the job change candidate.

[0101] The attractiveness elicitation unit can select the optimal method of eliciting attractiveness by taking into consideration the geographical location information of the job seeker when eliciting the attractiveness. For example, if the job seeker lives in an urban area, the attractiveness elicitation unit selects a method of eliciting the attractiveness of the urban area. For example, if the job seeker lives in an urban area, the attractiveness elicitation unit selects a method of eliciting the attractiveness of the urban area to elicit new attractiveness from the job seeker. Furthermore, if the job seeker lives in a rural area, the attractiveness elicitation unit can select a method of eliciting the attractiveness of the rural area to elicit new attractiveness from the job seeker. Furthermore, if the job seeker wishes to change jobs overseas, the attractiveness elicitation unit can select a method of eliciting the attractiveness of overseas locations. For example, if the job seeker wishes to change jobs overseas, the attractiveness elicitation unit selects a method of eliciting the attractiveness of overseas locations to elicit new attractiveness from the job seeker. This allows the optimal method of eliciting attractiveness to be selected based on the geographical location information of the job seeker.

[0102] The update unit can estimate the emotions of the job seeker and adjust the timing of information updates based on the estimated emotions. For example, if the job seeker is feeling anxious, the update unit updates the information at a timing that will give the job seeker a sense of security. For example, if the job seeker is feeling anxious, the update unit updates the information at a timing that will give the job seeker a sense of security and provides the information to the job seeker. The update unit can also timely update detailed information if the job seeker is relaxed. For example, if the job seeker is relaxed, the update unit timely updates the detailed information and provides the information to the job seeker. Furthermore, the update unit can quickly update visually appealing information if the job seeker is excited. For example, if the job seeker is excited, the update unit quickly updates the visually appealing information and provides it to the job seeker. This makes it possible to adjust the timing of information updates according to the emotions of the job seeker.

[0103] The updating unit can select the optimal update method by referring to past data when updating. The updating unit, for example, selects the optimal update method by referring to data on past job-changers. For example, the updating unit references data on past job-changers, selects the optimal update method, and provides it to the job seeker. The updating unit can also select the optimal update method by referring to past market trend data. For example, the updating unit references past market trend data, selects the optimal update method, and provides it to the job seeker. The updating unit can also select the optimal update method by referring to past industry-specific data. For example, the updating unit references past industry-specific data, selects the optimal update method, and provides it to the job seeker. In this way, the optimal update method can be selected by referring to past data.

[0104] The update unit can estimate the emotions of the job seeker and determine the priority of information updates based on the estimated emotions. For example, if the job seeker is feeling anxious, the update unit prioritizes updating information that gives a sense of security. For example, if the job seeker is feeling anxious, the update unit prioritizes updating information that gives a sense of security and provides it to the job seeker. The update unit can also prioritize updating detailed information if the job seeker is relaxed. For example, if the job seeker is relaxed, the update unit prioritizes updating the detailed information and provides it to the job seeker. Furthermore, if the job seeker is excited, the update unit can also prioritize updating visually attractive information. For example, if the job seeker is excited, the update unit prioritizes updating the visually attractive information and provides it to the job seeker. In this way, the priority of information updates can be determined according to the emotions of the job seeker.

[0105] When updating, the update unit can select the optimal update method by taking into consideration the geographical location information of the job seeker. For example, if the job seeker lives in an urban area, the update unit prioritizes updating information about the urban area. For example, if the job seeker lives in an urban area, the update unit prioritizes updating information about the urban area and provides it to the job seeker. The update unit can also prioritize updating information about the rural area if the job seeker lives in a rural area. For example, if the job seeker lives in a rural area, the update unit prioritizes updating information about the rural area and provides it to the job seeker. Furthermore, if the job seeker wishes to change jobs overseas, the update unit can also prioritize updating information about overseas areas. For example, if the job seeker wishes to change jobs overseas, the update unit prioritizes updating information about the overseas area and provides it to the job seeker. This makes it possible to select the optimal update method based on the geographical location information of the job seeker. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, calculation unit, suggestion unit, and attraction enhancement unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects information about the job seeker using the control unit 46A of the smart device 14. The analysis unit analyzes the collected information using, for example, the specific processing unit 290 of the data processing device 12. The estimation unit estimates the optimal business type or industry using, for example, the specific processing unit 290 of the data processing device 12. The calculation unit calculates the expected annual income using, for example, the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal job destination using, for example, the control unit 46A of the smart device 14. The attraction enhancement unit enhances new attractions of the job seeker using, for example, the specific processing unit 290 of the data processing device 12. The update unit periodically updates the information about the job seeker using, for example, the control unit 46A of the smart device 14. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, calculation unit, suggestion unit, and attractiveness elicitation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects information about the job seeker using the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected information using, for example, the specific processing unit 290 of the data processing device 12. The estimation unit estimates the optimal business type or industry using, for example, the specific processing unit 290 of the data processing device 12. The calculation unit calculates the expected annual income using, for example, the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal job destination using, for example, the control unit 46A of the smart glasses 214. The attractiveness elicitation unit elicits new attractiveness from the job seeker using, for example, the specific processing unit 290 of the data processing device 12. The update unit periodically updates the information about the job seeker using, for example, the control unit 46A of the smart glasses 214. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, calculation unit, suggestion unit, and attraction elicitation unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects information about the job seeker using the control unit 46A of the headset type terminal 314. The analysis unit analyzes the collected information using, for example, the specific processing unit 290 of the data processing device 12. The estimation unit estimates the optimal business type or industry using, for example, the specific processing unit 290 of the data processing device 12. The calculation unit calculates the expected annual income using, for example, the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal job destination using, for example, the control unit 46A of the headset type terminal 314. The attraction elicitation unit elicits new attractions from the job seeker using, for example, the specific processing unit 290 of the data processing device 12. The update unit periodically updates the information about the job seeker using, for example, the control unit 46A of the headset type terminal 314. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, estimation unit, calculation unit, suggestion unit, and attraction elicitation unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects information about the job seeker using the control unit 46A of the robot 414. The analysis unit analyzes the collected information using, for example, the specific processing unit 290 of the data processing device 12. The estimation unit estimates the optimal business type or industry using, for example, the specific processing unit 290 of the data processing device 12. The calculation unit calculates the expected annual income using, for example, the specific processing unit 290 of the data processing device 12. The suggestion unit suggests the optimal job destination using, for example, the control unit 46A of the robot 414. The attraction elicitation unit elicits new attractions from the job seeker using, for example, the specific processing unit 290 of the data processing device 12. The update unit periodically updates the information about the job seeker using, for example, the control unit 46A of the robot 414.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The proposal department can customize job suggestions based on the job seeker's past work history. For example, if the job seeker has experience as an engineer, the proposal department can suggest a job where the job seeker can utilize his or her technical skills. Also, if the job seeker has experience as a manager, the proposal department can suggest a job where the job seeker can utilize his or her leadership and management skills. Furthermore, if the job seeker has worked in a variety of industries, the proposal department can suggest a job where the job seeker can utilize that diverse experience. This makes it possible to propose more appropriate job suggestions based on the job seeker's past work history.

[0108] The analysis unit can make job suggestions that take into account the hobbies and interests of job seekers. For example, if a job seeker is interested in outdoor activities, it can suggest industries and fields that allow them to utilize that hobby. Also, if a job seeker is interested in art or design, it can suggest creative jobs that allow them to utilize that interest. Furthermore, if a job seeker has a strong interest in technology, it can suggest IT-related jobs that allow them to utilize that interest. This makes it possible to make suggestions that take into account the hobbies and interests of job seekers.

[0109] The collection unit can analyze the social media activities of job seekers and collect related information. For example, if a job seeker is very active on LinkedIn, the collection unit can collect LinkedIn profile information. Also, if a job seeker posts a lot on X (formerly Twitter), the collection unit can analyze the content of X's (formerly Twitter) posts and collect related information. Furthermore, if a job seeker is very active on Facebook, the collection unit can analyze the content of Facebook posts and collect related information. In this way, related information can be collected based on the job seeker's social media activities.

[0110] The estimation unit can suggest job opportunities by taking into consideration the geographical location information of the job seeker. For example, if the job seeker lives in an urban area, it can prioritize suggesting job information in urban areas. Also, if the job seeker lives in a rural area, it can prioritize suggesting job information in rural areas. Furthermore, if the job seeker wishes to change jobs overseas, it can prioritize suggesting job information overseas. This makes it possible to suggest more appropriate job opportunities based on the geographical location information of the job seeker.

[0111] The calculation unit can calculate the expected annual income by referring to the past annual income data of the job seeker. For example, the calculation unit calculates the expected annual income of the job seeker by referring to the annual income data of past job seekers. The calculation unit can also calculate the expected annual income by referring to past market trend data. Furthermore, the calculation unit can also calculate the expected annual income by referring to past industry-specific annual income data. In this way, by referring to past data, the accuracy of the expected annual income can be improved.

[0112] The collection unit can estimate the job seeker's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the job seeker is feeling stressed, the collection unit can delay information collection until the job seeker is relaxed. Also, if the job seeker is excited, the collection unit can start information collection immediately and acquire data quickly. Furthermore, if the job seeker is tired, the collection unit can resume information collection after the job seeker has rested. In this way, the timing of information collection can be adjusted according to the job seeker's emotions.

[0113] The analysis unit can estimate the job seeker's emotions and adjust the way the analysis is presented based on the estimated emotions. For example, if the job seeker is nervous, a simple and easy-to-understand analysis result can be provided. If the job seeker is relaxed, a detailed analysis result can be provided. Furthermore, if the job seeker is excited, a visually appealing analysis result can be provided. This allows the way the analysis is presented to be adjusted according to the job seeker's emotions.

[0114] The estimation unit can estimate the emotion of the job seeker and adjust the estimation standard based on the estimated emotion. For example, if the job seeker feels anxious, a conservative estimation standard can be applied. If the job seeker feels relaxed, a standard estimation standard can be applied. Furthermore, if the job seeker feels excited, an aggressive estimation standard can be applied. In this way, the estimation standard can be adjusted according to the emotion of the job seeker.

[0115] The calculation unit can estimate the emotion of the job seeker and adjust the calculation method of the expected annual income based on the estimated emotion. For example, if the job seeker feels anxious, a conservative expected annual income can be calculated. If the job seeker feels relaxed, a standard expected annual income can be calculated. Furthermore, if the job seeker feels excited, a challenging expected annual income can be calculated. In this way, the calculation method of the expected annual income can be adjusted according to the emotion of the job seeker.

[0116] The suggestion unit can estimate the emotion of the job seeker and adjust the way the suggestion is expressed based on the estimated emotion. For example, if the job seeker feels anxious, a way of expression that gives a sense of security can be provided. If the job seeker feels relaxed, a detailed way of expression can be provided. Furthermore, if the job seeker feels excited, a visually appealing way of expression can be provided. In this way, the way of expression of the suggestion can be adjusted according to the emotion of the job seeker.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects information on job seekers. This information includes work history, skills, and desired conditions. For example, if a job seeker has previously worked as an engineer, information on the job duties, qualifications obtained, and personality traits will be collected. If the job seeker has experience as a manager, information on leadership and management will also be collected. Furthermore, if the job seeker has worked in a variety of industries, information related to each industry will be collected in a balanced manner. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it analyzes the job history, skills, and desired conditions of the job seeker to estimate the most suitable industry or business for the job seeker. If the job seeker has experience as an engineer, it can estimate not only the IT industry but also other industries such as manufacturing and services. Step 3: The estimation unit estimates the most suitable business or industry based on the analysis results obtained by the analysis unit. For example, it makes the estimation by taking into account the correlation between the job seeker's skills and experience, the correlation between qualifications and personality traits, and the correlation between past work history and current living situation. Step 4: The calculation unit calculates the expected annual income in the business type and industry estimated by the estimation unit. For example, the calculation unit calculates the expected annual income by referring to the annual income data of past job-changers, past market trend data, and annual income data by industry. Step 5: The proposal unit proposes the optimal job change based on the expected annual salary calculated by the calculation unit. For example, it proposes a job change that allows the job seeker to take on a new industry while utilizing their experience as an engineer, a job change that brings out the job seeker's new strengths, or a job change that is optimal according to the job seeker's desired conditions and living situation.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information on job seekers, an analysis unit that analyzes the information collected by the collection unit; an estimation unit that estimates an optimal business type or industry based on the analysis results obtained by the analysis unit; a calculation unit that calculates an expected annual income in the business type or industry estimated by the estimation unit; a proposal unit that proposes an optimal job change based on the expected annual income calculated by the calculation unit; Equipped with A system characterized by:

2. Equipping a department that draws out the new charms of job seekers 2. The system of claim 1.

3. Equipped with an update department to update job seeker information 2. The system of claim 1.

4. The collecting unit Estimate the emotions of job seekers and adjust the timing of information gathering based on those emotions 2. The system of claim 1.

5. The collecting unit Analyze job applicants' past work histories and select the most appropriate method of information gathering 2. The system of claim 1.

6. The collecting unit When gathering information, filter it based on the job seeker's current living situation and areas of interest.

2. The system of claim 1.

7. The collecting unit Estimate the emotions of job seekers and prioritize the information to be collected based on those emotions.

2. The system of claim 1.

8. The collecting unit When gathering information, prioritize collecting relevant information by taking into account the job seeker's geographic location.

2. The system of claim 1.

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