System

Abstract

An object of a system according to an embodiment is to appropriately grasp desires and needs of employees and propose an optimal career plan and an appropriate arrangement of materials.SOLUTION: A system includes a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The collection unit collects requests and needs of employees. The analysis unit analyzes the information collected by the collection unit and proposes a career plan. The evaluation part analyzes the resume of the applicant and evaluates the aptitude and skill. The proposal unit proposes an appropriate material arrangement based on the information obtained by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to properly understand employees' desires and needs and propose optimal career plans and personnel placements.

[0005] The system according to the embodiment aims to properly understand the wishes and needs of employees and propose optimal career plans and suitable personnel placements. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The collection unit collects employee wishes and needs. The analysis unit analyzes the information collected by the collection unit and proposes a career plan. The evaluation unit analyzes applicants' resumes and evaluates their aptitude and skills. The proposal unit proposes optimal personnel placement based on the information obtained by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can properly understand the wishes and needs of employees and propose optimal career plans and suitable personnel placements. [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 career support system according to an embodiment of the present invention utilizes generative AI to understand the hopes and needs of individuals and realizes a new, proposal-based career support system that connects people and companies. The career support system collects employees' hopes and needs, analyzes them using generative AI, and proposes optimal career plans. The career support system also uses generative AI to diagnose and screen resumes for new graduates and mid-career hires, organizes information for optimal talent placement, and proposes it to the human resources department. Furthermore, the career support system uses generative AI to help provide opportunities where individuals can thrive by utilizing their experience and knowledge. For example, the career support system collects employee hopes and needs. For example, it collects information such as the skills employees want to acquire in the future and the type of position they want to hold, and creates a career plan based on that information. Next, the career support system uses generative AI to diagnose and screen resumes for new graduates and mid-career hires. The generative AI analyzes applicants' resumes and evaluates their aptitude and skills. For example, it analyzes the applicants' past experience and skill sets to determine whether they match the company's needs. This allows it to organize information for optimal talent placement and propose it to the human resources department. Furthermore, the career support system uses generative AI to help utilize each employee's experience and knowledge and provide a workplace where they can thrive. Generative AI analyzes each employee's experience and knowledge and, based on that, suggests optimal projects and roles. For example, for an employee who is proficient in a particular technology, it suggests projects that can utilize that technology. In this way, it is possible to provide an environment where employees can thrive to the fullest. This enables the career support system to understand each individual's wishes and needs and provide optimal career support. For example, employees can clarify their own career plans and acquire the necessary skills to achieve them. Companies can also improve the efficiency of the entire organization by placing the right people in the right positions. Furthermore, by providing a workplace where each employee can thrive, generative AI can increase employee motivation and promote corporate growth by utilizing each employee's experience and knowledge.

[0029] A career support system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The collection unit collects employee hopes and needs. The employee hopes and needs include, but are not limited to, career advancement, work location, and working hours. The collection unit collects information such as the skills employees want to acquire in the future and the position they want to hold. The collection unit can also estimate the employee's emotions and adjust the timing of collecting the hopes and needs based on the estimated employee emotions. For example, if an employee is feeling stressed, the hopes and needs can be collected during breaks or in a relaxing environment so that the employee is in a relaxed state. The analysis unit analyzes the information collected by the collection unit and proposes an optimal career plan. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit proposes the skills employees should acquire in the future and the position they should hold based on the collected information. The evaluation unit analyzes the applicant's resume and evaluates their aptitude and skills. Assessment of aptitude and skills includes, but is not limited to, technical skills, communication abilities, leadership, etc. For example, the evaluation unit analyzes the applicant's past experience and skill set to determine whether the applicant matches the company's needs. The proposal unit proposes optimal personnel placement based on the information obtained by the evaluation unit. Optimal personnel placement is performed based on, for example, the job content, team composition, project requirements, etc., but is not limited to, these examples. For example, the proposal unit proposes a project that can utilize an applicant who is familiar with a specific technology. In this way, the career support system according to the embodiment can understand the wishes and needs of employees, propose optimal career plans, and perform optimal personnel placement.

[0030] The collection unit can analyze the employee's past career history and select a collection method. For example, the collection unit can analyze the projects and roles in which the employee has participated in the past and collect the employee's hopes and needs using related questions. The collection unit can also ask specific questions about the employee's future career plan by referring to the employee's past career path. The collection unit can also select an appropriate collection method based on the employee's past evaluations and feedback to effectively collect the employee's hopes and needs. This makes it possible to effectively collect the employee's hopes and needs based on the employee's past career history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the employee's past career history data into the generation AI and have the generation AI select the collection method.

[0031] When collecting the wants and needs, the collection unit can filter them based on the employee's current project or area of ​​interest. For example, the collection unit prioritizes collecting wants and needs related to the project the employee is currently working on. The collection unit can also collect wants and needs using related questions based on the employee's area of ​​interest. The collection unit can also collect information on the employee's areas of interest and filter the wants and needs based on that information. This makes it possible to collect wants and needs related to the employee's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the employee's current project data into the generation AI and have the generation AI perform filtering of the wants and needs.

[0032] When collecting wants and needs, the collection unit can select a collection means depending on the employee's input method. For example, if the employee prefers voice input, the collection unit provides a means for collecting wants and needs by voice. Furthermore, if the employee prefers text input, the collection unit can also provide a means for collecting wants and needs by text. Furthermore, if the employee expresses wants and needs using images or visuals, the collection unit can also provide a collection means using images. This allows wants and needs to be collected by the optimal means depending on the employee's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the employee's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0033] When collecting desires and needs, the collection unit can prioritize collecting highly relevant information by taking into account the employee's geographic location information. For example, if the employee lives in a specific area, the collection unit can prioritize collecting career options and opportunities related to that area. Furthermore, if the employee desires remote work, the collection unit can prioritize collecting information related to remote work. Furthermore, if the employee desires to work in a specific city, the collection unit can prioritize collecting career options related to that city. This makes it possible to collect highly relevant information based on the employee's geographic location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the employee's geographic location information into the generation AI and cause the generation AI to collect highly relevant information.

[0034] When collecting the hopes and needs, the collection unit can analyze the employee's social media activity and collect related information. For example, the collection unit can analyze career-related posts shared by the employee on social media to collect related hopes and needs. The collection unit can also identify areas of interest and skills from the employee's social media activity and collect the hopes and needs based on the identified areas of interest and skills. The collection unit can also analyze the employee's social media network and collect related career options. This makes it possible to collect related information based on the employee's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's social media data into the generation AI and cause the generation AI to collect related information.

[0035] When collecting wants and needs, the collection unit can customize the collection method by reflecting employees' past feedback. For example, the collection unit adjusts the collection method based on feedback provided by employees in the past to more effectively collect wants and needs. The collection unit can also identify, from employees' past feedback, that a particular collection method is effective and use it preferentially. The collection unit can also analyze employees' past feedback and customize the collection method to more accurately collect wants and needs. This allows the collection method to be customized based on employees' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input employees' past feedback data into the generation AI and have the generation AI customize the collection method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the employee. For example, the analysis unit performs a detailed analysis on employees in important positions and provides a specific career plan. The analysis unit can also perform a basic analysis on new employees and provide an initial career plan. The analysis unit can also perform an analysis at a medium level of detail on mid-level employees and provide a plan for the mid-career. This allows the level of detail of the analysis to be adjusted depending on the importance of the employee. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee position data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the employee category. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to technical employees. The analysis unit can also apply an analysis algorithm specialized in leadership skills to managerial employees. The analysis unit can also apply an analysis algorithm specialized in creativity or design skills to creative employees. This allows an appropriate analysis algorithm to be applied depending on the employee category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. For example, the analysis unit performs a current analysis based on the employee's past career plan analysis results to improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to the employee's past feedback to improve accuracy. The analysis unit can also adjust the analysis parameters by referring to the employee's past evaluation results to improve accuracy. In this way, the analysis accuracy can be improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the employee's submission time. For example, the analysis unit prioritizes analysis for employees who require urgent career plans. The analysis unit can also determine the priority of analysis for employees who require regular career plan updates based on the submission time. The analysis unit can also prioritize analysis of initial career plans for new employees. This makes it possible to determine the priority of analysis based on the employee's submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee submission time data into the generation AI and have the generation AI determine the analysis priority.

[0040] During analysis, the analysis unit can adjust the order of analysis based on employee relevance. For example, the analysis unit may prioritize analysis for employees involved in important projects. The analysis unit can also adjust the order of analysis for employees who require inter-departmental collaboration based on relevance. The analysis unit can also prioritize analysis related to specific skills for employees who require specific skills. This makes it possible to adjust the order of analysis based on employee relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit may input employee relevance data into a generation AI and have the generation AI adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to employees with extensive technical expertise. The analysis unit can also provide analysis results in easy-to-understand language to employees with little technical expertise. The analysis unit can also provide analysis results using appropriate technical terminology to employees with medium levels of technical expertise. This allows the use of technical terminology in the analysis to be adjusted according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input employee expertise level data into the generation AI and have the generation AI execute the use of technical terminology.

[0042] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between applicants during the evaluation. For example, the evaluation unit can improve the accuracy of the evaluation by referring to the evaluations of colleagues and superiors with whom the applicant has worked in the past. The evaluation unit can also improve the accuracy of the evaluation by considering feedback from team members in projects in which the applicant has participated. The evaluation unit can also improve the accuracy of the evaluation by referring to evaluations in organizations or communities to which the applicant has belonged. In this way, the accuracy of the evaluation can be improved by taking into account the interrelationships between applicants. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the interrelationships between applicants into a generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0043] The evaluation unit can make an evaluation taking into account the applicant's attribute information. The evaluation unit can make a fair evaluation, for example, by taking into account the applicant's age and gender. The evaluation unit can also apply appropriate evaluation criteria by taking into account the applicant's educational background and work history. The evaluation unit can also make an evaluation that respects diversity by taking into account the applicant's cultural background and language skills. This allows for a fair evaluation by taking into account the applicant's attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's attribute information data into a generation AI and have the generation AI perform the evaluation.

[0044] During evaluation, the evaluation unit can weight the evaluation based on the applicant's submission frequency. For example, if the applicant applies frequently, the evaluation unit can reflect that applicant's enthusiasm in the evaluation. Furthermore, if the applicant has only applied once, the evaluation unit can also evaluate the applicant by emphasizing the quality of the application. Furthermore, if the applicant has applied multiple times in the past, the evaluation unit can weight the evaluation by taking into account that history. In this way, by weighting the evaluation based on the applicant's submission frequency, it is possible to evaluate the applicant by taking into account the applicant's enthusiasm and history. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's submission frequency data into the generation AI and have the generation AI perform the weighting of the evaluation.

[0045] The evaluation unit can take into account the geographic distribution of applicants when conducting the evaluation. For example, if an applicant lives in a specific region, the evaluation unit can take into account the characteristics of that region when conducting the evaluation. Furthermore, if an applicant requests remote work, the evaluation unit can also take that preference into account when conducting the evaluation. Furthermore, if an applicant is applying from overseas, the evaluation unit can also take into account their cultural background when conducting the evaluation. In this way, by conducting the evaluation taking into account the geographic distribution of applicants, it is possible to conduct an evaluation that reflects regional characteristics. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input applicant geographic distribution data into the generation AI and have the generation AI perform the evaluation.

[0046] During the evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to the applicant's related literature. The evaluation unit can, for example, evaluate the applicant's expertise by referring to papers and articles written by the applicant. The evaluation unit can also evaluate the applicant's influence by referring to literature cited by the applicant. The evaluation unit can also evaluate the applicant's achievements by referring to the results of research projects in which the applicant participated. This allows for an accurate evaluation of the applicant's expertise and achievements by referring to the applicant's related literature. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the applicant's related literature data into the generation AI and have the generation AI improve the accuracy of the evaluation.

[0047] The evaluation unit can take into account the market value of the applicant when evaluating them. For example, if the applicant's skill set is highly valued in the market, the evaluation unit can take that market value into account when evaluating them. Furthermore, if the applicant's experience is in demand in the market, the evaluation unit can also emphasize that experience when evaluating them. Furthermore, if the applicant's qualifications or certifications are highly valued in the market, the evaluation unit can also consider those qualifications or certifications when evaluating them. In this way, by performing an evaluation taking into account the applicant's market value, it is possible to perform an evaluation that meets the needs of the company. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's market value data into the generation AI and have the generation AI perform the evaluation.

[0048] When making a proposal, the proposal unit can select a proposal method by analyzing the employee's past career behavior. For example, the proposal unit can make a similar proposal by referring to a career path that the employee has been successful in the past. The proposal unit can also make a different proposal to help the employee avoid a career path that has failed them in the past. The proposal unit can also analyze the employee's past career behavior and select an optimal proposal method. This makes it possible to select an optimal proposal method based on the employee's past career behavior. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input the employee's past career behavior data into the generation AI and have the generation AI select a proposal method.

[0049] When making a proposal, the suggestion unit can customize the proposed means based on the employee's current living situation. For example, if an employee wishes to work remotely due to family circumstances, the suggestion unit can make a proposal based on that preference. Furthermore, if an employee wishes to do light work for health reasons, the suggestion unit can make a proposal based on that preference. Furthermore, if an employee wishes to balance work with their studies, the suggestion unit can make a proposal based on that preference. This makes it possible to provide optimal proposals based on the employee's current living situation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the employee's living situation data into the generation AI and cause the generation AI to customize the proposed means.

[0050] The suggestion unit can improve the suggestion method by reflecting employee feedback when making a suggestion. For example, the suggestion unit can adjust the suggestion method based on feedback provided by employees in the past to make more effective suggestions. The suggestion unit can also identify, from employee feedback, that a particular suggestion method is effective and use it preferentially. The suggestion unit can also analyze employee feedback and customize the suggestion method to make more accurate suggestions. This allows the suggestion method to be improved based on employee feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input employee feedback data into a generation AI and cause the generation AI to improve the suggestion method.

[0051] When making a proposal, the suggestion unit can select a proposal method taking into account the employee's geographic location information. For example, if the employee lives in a specific area, the suggestion unit can prioritize proposals for career options and opportunities related to that area. Furthermore, if the employee desires remote work, the suggestion unit can also make proposals related to remote work. Furthermore, if the employee desires to work in a specific city, the suggestion unit can also suggest career options related to that city. This allows optimal proposals to be provided based on the employee's geographic location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the employee's geographic location information into the generation AI and cause the generation AI to select a proposal method.

[0052] When making a proposal, the suggestion unit can analyze the employee's social media activity to suggest a proposal method. For example, the suggestion unit can analyze career-related posts shared by the employee on social media and make relevant proposals. The suggestion unit can also identify areas of interest and skills from the employee's social media activity and make proposals based on the identified areas of interest. The suggestion unit can also analyze the employee's social media network and suggest related career options. This makes it possible to provide optimal proposals based on the employee's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the employee's social media data into a generation AI and have the generation AI select a proposal method.

[0053] When making a proposal, the suggestion unit can customize the suggestion method by reflecting the employee's past feedback. For example, the suggestion unit can adjust the suggestion method based on feedback provided by the employee in the past to make more effective suggestions. The suggestion unit can also identify, from the employee's feedback, that a particular suggestion method is effective and use it preferentially. The suggestion unit can also analyze the employee's feedback and customize the suggestion method to make more accurate suggestions. This allows the suggestion method to be customized based on the employee's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input employee feedback data into the generation AI and cause the generation AI to customize the suggestion method.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The analysis unit can collect employee health data and adjust career plans based on their health condition. For example, if an employee has a chronic health problem, the analysis unit can propose a career plan that takes that health condition into consideration. Also, if an employee has a specific health risk based on the results of a health check, the analysis unit can provide a career plan to mitigate that risk. Furthermore, if an employee is aiming to improve their health, the analysis unit can propose a career plan that supports a healthy lifestyle. This makes it possible to provide the optimal career plan based on the employee's health condition.

[0056] The collection department can collect information about employees' hobbies and interests and suggest career plans based on that information. For example, if an employee is interested in a particular sport, it can suggest projects or roles related to that sport. If an employee is interested in art or music, it can provide career options that utilize that interest. Furthermore, if an employee is active in volunteer activities, it can suggest career plans that support those activities. This makes it possible to provide career plans based on employees' hobbies and interests.

[0057] The evaluation department can analyze applicants' social media activities and reflect their personality and values ​​in their evaluation. For example, they can evaluate their teamwork and leadership skills based on the content they share on social media. They can also determine whether an applicant's posts fit the company culture. They can also analyze the reactions of applicants' networks and followers to evaluate their social influence. This allows for a comprehensive evaluation based on applicants' social media activities.

[0058] The proposal department can propose career plans taking into account the employee's family structure. For example, if an employee is raising a child, it can propose career options that offer comprehensive childcare support. Also, if an employee has a family member who requires care, it can provide a career plan that allows for care support. Furthermore, if an employee wishes to work away from home, it can propose a career plan that meets that preference. This makes it possible to provide the optimal career plan that suits the employee's family structure.

[0059] The analysis unit can analyze an employee's learning history and propose a learning plan for improving their skills. For example, it can suggest the next skills and knowledge an employee should learn based on the training and courses they have taken in the past. Also, if an employee wants to deepen their expertise in a specific field, it can provide a learning plan related to that field. It can also suggest a learning plan for employees to keep up with new technologies and trends. This makes it possible to provide the optimal skill improvement plan based on the employee's learning history.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The collection department collects employee hopes and needs. Employee hopes and needs include career advancement, work location, and working hours. The collection department collects information such as what skills employees would like to acquire in the future and what position they would like to hold. The collection department can also estimate employees' emotions and adjust the timing of collecting hopes and needs based on the estimated emotions of employees. For example, if an employee is feeling stressed, the collection department will collect hopes and needs during break time or in a relaxing environment so that the employee is in a relaxed state. Step 2: The analysis department analyzes the information collected by the collection department and proposes optimal career plans. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, based on the collected information, the analysis department suggests what skills employees should acquire in the future and what positions they should hold. Step 3: The evaluation department analyzes the applicant's resume and evaluates their aptitude and skills. The evaluation of aptitude and skills includes technical skills, communication ability, leadership, etc. For example, the evaluation department analyzes the applicant's past experience and skill set to determine whether they match the company's needs. Step 4: The proposal department proposes the right personnel placement based on the information obtained by the evaluation department. The right personnel placement is based on the job content, team composition, project requirements, etc. For example, if an applicant is familiar with a specific technology, the proposal department will propose a project that can utilize that technology.

[0062] (Example 2) A career support system according to an embodiment of the present invention utilizes generative AI to understand the hopes and needs of individuals and realizes a new, proposal-based career support system that connects people and companies. The career support system collects employees' hopes and needs, analyzes them using generative AI, and proposes optimal career plans. The career support system also uses generative AI to diagnose and screen resumes for new graduates and mid-career hires, organizes information for optimal talent placement, and proposes it to the human resources department. Furthermore, the career support system uses generative AI to help provide opportunities where individuals can thrive by utilizing their experience and knowledge. For example, the career support system collects employee hopes and needs. For example, it collects information such as the skills employees want to acquire in the future and the type of position they want to hold, and creates a career plan based on that information. Next, the career support system uses generative AI to diagnose and screen resumes for new graduates and mid-career hires. The generative AI analyzes applicants' resumes and evaluates their aptitude and skills. For example, it analyzes the applicants' past experience and skill sets to determine whether they match the company's needs. This allows it to organize information for optimal talent placement and propose it to the human resources department. Furthermore, the career support system uses generative AI to help utilize each employee's experience and knowledge and provide a workplace where they can thrive. Generative AI analyzes each employee's experience and knowledge and, based on that, suggests optimal projects and roles. For example, for an employee who is proficient in a particular technology, it suggests projects that can utilize that technology. In this way, it is possible to provide an environment where employees can thrive to the fullest. This enables the career support system to understand each individual's wishes and needs and provide optimal career support. For example, employees can clarify their own career plans and acquire the necessary skills to achieve them. Companies can also improve the efficiency of the entire organization by placing the right people in the right positions. Furthermore, by providing a workplace where each employee can thrive, generative AI can increase employee motivation and promote corporate growth by utilizing each employee's experience and knowledge.

[0063] A career support system according to an embodiment includes a collection unit, an analysis unit, an evaluation unit, and a proposal unit. The collection unit collects employee hopes and needs. The employee hopes and needs include, but are not limited to, career advancement, work location, and working hours. The collection unit collects information such as the skills employees want to acquire in the future and the position they want to hold. The collection unit can also estimate the employee's emotions and adjust the timing of collecting the hopes and needs based on the estimated employee emotions. For example, if an employee is feeling stressed, the hopes and needs can be collected during breaks or in a relaxing environment so that the employee is in a relaxed state. The analysis unit analyzes the information collected by the collection unit and proposes an optimal career plan. The analysis can be performed using, for example, data mining, statistical analysis, machine learning algorithms, or other methods. For example, the analysis unit proposes the skills employees should acquire in the future and the position they should hold based on the collected information. The evaluation unit analyzes the applicant's resume and evaluates their aptitude and skills. Assessment of aptitude and skills includes, but is not limited to, technical skills, communication abilities, leadership, etc. For example, the evaluation unit analyzes the applicant's past experience and skill set to determine whether the applicant matches the company's needs. The proposal unit proposes optimal personnel placement based on the information obtained by the evaluation unit. Optimal personnel placement is performed based on, for example, the job content, team composition, project requirements, etc., but is not limited to, these examples. For example, the proposal unit proposes a project that can utilize an applicant who is familiar with a specific technology. In this way, the career support system according to the embodiment can understand the wishes and needs of employees, propose optimal career plans, and perform optimal personnel placement.

[0064] The collection unit estimates the employee's emotions and adjusts the timing of collecting the employee's wants and needs based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit may collect the employee's wants and needs during a break or in a relaxing environment to collect the employee's wants and needs in a relaxed state. Furthermore, if the employee is highly motivated, the collection unit may collect the employee's wants and needs at that time to reflect their positive opinions. Furthermore, if the employee is tired, the collection unit may adjust the collection timing to a later date to collect the employee's wants and needs when they are refreshed. This allows the collection of the employee's wants and needs at the optimal timing depending on the employee's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit may input the employee's facial expression data into the generation AI and have the generation AI estimate the employee's emotions.

[0065] The collection unit can analyze the employee's past career history and select a collection method. For example, the collection unit can analyze the projects and roles in which the employee has participated in the past and collect the employee's hopes and needs using related questions. The collection unit can also ask specific questions about the employee's future career plan by referring to the employee's past career path. The collection unit can also select an appropriate collection method based on the employee's past evaluations and feedback to effectively collect the employee's hopes and needs. This makes it possible to effectively collect the employee's hopes and needs based on the employee's past career history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the employee's past career history data into the generation AI and have the generation AI select the collection method.

[0066] When collecting the wants and needs, the collection unit can filter them based on the employee's current project or area of ​​interest. For example, the collection unit prioritizes collecting wants and needs related to the project the employee is currently working on. The collection unit can also collect wants and needs using related questions based on the employee's area of ​​interest. The collection unit can also collect information on the employee's areas of interest and filter the wants and needs based on that information. This makes it possible to collect wants and needs related to the employee's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the employee's current project data into the generation AI and have the generation AI perform filtering of the wants and needs.

[0067] When collecting wants and needs, the collection unit can select a collection means depending on the employee's input method. For example, if the employee prefers voice input, the collection unit provides a means for collecting wants and needs by voice. Furthermore, if the employee prefers text input, the collection unit can also provide a means for collecting wants and needs by text. Furthermore, if the employee expresses wants and needs using images or visuals, the collection unit can also provide a collection means using images. This allows wants and needs to be collected by the optimal means depending on the employee's input method. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the employee's voice data into a generation AI and have the generation AI convert the voice data into text data.

[0068] The collection unit can estimate the employee's emotions and determine the priority of information to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit can prioritize collecting information about their desires and needs regarding stress reduction. Furthermore, if the employee is highly motivated, the collection unit can prioritize collecting information about their desires and needs regarding career advancement. Furthermore, if the employee is tired, the collection unit can prioritize collecting information about their desires and needs regarding work-life balance. This allows the priority of information to be determined based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the employee's facial expression data into the generation AI and have the generation AI estimate the employee's emotions.

[0069] When collecting desires and needs, the collection unit can prioritize collecting highly relevant information by taking into account the employee's geographic location information. For example, if the employee lives in a specific area, the collection unit can prioritize collecting career options and opportunities related to that area. Furthermore, if the employee desires remote work, the collection unit can prioritize collecting information related to remote work. Furthermore, if the employee desires to work in a specific city, the collection unit can prioritize collecting career options related to that city. This makes it possible to collect highly relevant information based on the employee's geographic location information. Some or all of the above-described processing by the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the employee's geographic location information into the generation AI and cause the generation AI to collect highly relevant information.

[0070] When collecting the hopes and needs, the collection unit can analyze the employee's social media activity and collect related information. For example, the collection unit can analyze career-related posts shared by the employee on social media to collect related hopes and needs. The collection unit can also identify areas of interest and skills from the employee's social media activity and collect the hopes and needs based on the identified areas of interest and skills. The collection unit can also analyze the employee's social media network and collect related career options. This makes it possible to collect related information based on the employee's social media activity. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the employee's social media data into the generation AI and cause the generation AI to collect related information.

[0071] When collecting wants and needs, the collection unit can customize the collection method by reflecting employees' past feedback. For example, the collection unit adjusts the collection method based on feedback provided by employees in the past to more effectively collect wants and needs. The collection unit can also identify, from employees' past feedback, that a particular collection method is effective and use it preferentially. The collection unit can also analyze employees' past feedback and customize the collection method to more accurately collect wants and needs. This allows the collection method to be customized based on employees' past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input employees' past feedback data into the generation AI and have the generation AI customize the collection method.

[0072] The analysis unit can estimate the employee's emotions and adjust the way the career plan is presented based on the estimated employee's emotions. For example, if the employee is relaxed, the analysis unit can provide a detailed career plan to clarify the employee's future prospects. Furthermore, if the employee is feeling stressed, the analysis unit can provide a simple and easy-to-understand career plan to reduce the employee's burden. Furthermore, if the employee is highly motivated, the analysis unit can provide a career plan including challenging goals to increase the employee's motivation. This allows the way the career plan is presented to be adjusted according to the employee's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the employee's facial expression data into the generation AI and have the generation AI adjust the way the career plan is presented.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the employee. For example, the analysis unit performs a detailed analysis on employees in important positions and provides a specific career plan. The analysis unit can also perform a basic analysis on new employees and provide an initial career plan. The analysis unit can also perform an analysis at a medium level of detail on mid-level employees and provide a plan for the mid-career. This allows the level of detail of the analysis to be adjusted depending on the importance of the employee. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee position data into the generation AI and have the generation AI adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the employee category. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to technical employees. The analysis unit can also apply an analysis algorithm specialized in leadership skills to managerial employees. The analysis unit can also apply an analysis algorithm specialized in creativity or design skills to creative employees. This allows an appropriate analysis algorithm to be applied depending on the employee category. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the employee's past analysis results. For example, the analysis unit performs a current analysis based on the employee's past career plan analysis results to improve accuracy. The analysis unit can also adjust the analysis algorithm by referring to the employee's past feedback to improve accuracy. The analysis unit can also adjust the analysis parameters by referring to the employee's past evaluation results to improve accuracy. In this way, the analysis accuracy can be improved by referring to the employee's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the employee's past analysis result data into the generation AI and have the generation AI improve the analysis accuracy.

[0076] The analysis unit can estimate the employee's emotions and adjust the length of the career plan based on the estimated employee emotions. For example, if the employee is relaxed, the analysis unit can provide a long-term career plan to clarify the employee's future prospects. Furthermore, if the employee is stressed, the analysis unit can provide a short-term career plan to reduce the employee's burden. Furthermore, if the employee is highly motivated, the analysis unit can provide a medium- to long-term career plan to increase the employee's motivation. This allows the length of the career plan to be adjusted according to the employee's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the employee's facial expression data into the generation AI and have the generation AI adjust the length of the career plan.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the employee's submission time. For example, the analysis unit prioritizes analysis for employees who require urgent career plans. The analysis unit can also determine the priority of analysis for employees who require regular career plan updates based on the submission time. The analysis unit can also prioritize analysis of initial career plans for new employees. This makes it possible to determine the priority of analysis based on the employee's submission time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input employee submission time data into the generation AI and have the generation AI determine the analysis priority.

[0078] During analysis, the analysis unit can adjust the order of analysis based on employee relevance. For example, the analysis unit may prioritize analysis for employees involved in important projects. The analysis unit can also adjust the order of analysis for employees who require inter-departmental collaboration based on relevance. The analysis unit can also prioritize analysis related to specific skills for employees who require specific skills. This makes it possible to adjust the order of analysis based on employee relevance. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI, for example. For example, the analysis unit may input employee relevance data into a generation AI and have the generation AI adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the employee's level of expertise. For example, the analysis unit can provide analysis results using detailed technical terminology to employees with extensive technical expertise. The analysis unit can also provide analysis results in easy-to-understand language to employees with little technical expertise. The analysis unit can also provide analysis results using appropriate technical terminology to employees with medium levels of technical expertise. This allows the use of technical terminology in the analysis to be adjusted according to the employee's level of expertise. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input employee expertise level data into the generation AI and have the generation AI execute the use of technical terminology.

[0080] The evaluation unit can estimate the applicant's emotions and adjust the resume evaluation criteria based on the estimated applicant's emotions. For example, if the applicant is nervous, the evaluation unit can adjust the evaluation criteria so that the applicant can be evaluated in a relaxed state. Furthermore, if the applicant is confident, the evaluation unit can apply challenging evaluation criteria to maximize the evaluation of the applicant's skills. Furthermore, if the applicant is anxious, the evaluation unit can apply gentle evaluation criteria to provide a sense of security. This allows the resume evaluation criteria to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit may be performed using AI, or may be performed without AI. For example, the evaluation unit can input the applicant's facial expression data into the generation AI and have the generation AI adjust the evaluation criteria.

[0081] The evaluation unit can improve the accuracy of the evaluation by taking into account the interrelationships between applicants during the evaluation. For example, the evaluation unit can improve the accuracy of the evaluation by referring to the evaluations of colleagues and superiors with whom the applicant has worked in the past. The evaluation unit can also improve the accuracy of the evaluation by considering feedback from team members in projects in which the applicant has participated. The evaluation unit can also improve the accuracy of the evaluation by referring to evaluations in organizations or communities to which the applicant has belonged. In this way, the accuracy of the evaluation can be improved by taking into account the interrelationships between applicants. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the interrelationships between applicants into a generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0082] The evaluation unit can make an evaluation taking into account the applicant's attribute information. The evaluation unit can make a fair evaluation, for example, by taking into account the applicant's age and gender. The evaluation unit can also apply appropriate evaluation criteria by taking into account the applicant's educational background and work history. The evaluation unit can also make an evaluation that respects diversity by taking into account the applicant's cultural background and language skills. This allows for a fair evaluation by taking into account the applicant's attribute information. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's attribute information data into a generation AI and have the generation AI perform the evaluation.

[0083] During evaluation, the evaluation unit can weight the evaluation based on the applicant's submission frequency. For example, if the applicant applies frequently, the evaluation unit can reflect that applicant's enthusiasm in the evaluation. Furthermore, if the applicant has only applied once, the evaluation unit can also evaluate the applicant by emphasizing the quality of the application. Furthermore, if the applicant has applied multiple times in the past, the evaluation unit can weight the evaluation by taking into account that history. In this way, by weighting the evaluation based on the applicant's submission frequency, it is possible to evaluate the applicant by taking into account the applicant's enthusiasm and history. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's submission frequency data into the generation AI and have the generation AI perform the weighting of the evaluation.

[0084] The evaluation unit can estimate the applicant's emotions and adjust the display order of the evaluation results based on the estimated emotions of the applicant. For example, if the applicant is nervous, the evaluation unit can display the evaluation results in stages to help the applicant relax. Furthermore, if the applicant is confident, the evaluation unit can display the evaluation results all at once to provide quick feedback. Furthermore, if the applicant is feeling anxious, the evaluation unit can display positive evaluation results first to provide a sense of security. This allows the applicant to receive optimal feedback by adjusting the display order of the evaluation results according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or without AI. For example, the evaluation unit can input the applicant's facial expression data into the generation AI and have the generation AI adjust the display order of the evaluation results.

[0085] The evaluation unit can take into account the geographic distribution of applicants when conducting the evaluation. For example, if an applicant lives in a specific region, the evaluation unit can take into account the characteristics of that region when conducting the evaluation. Furthermore, if an applicant requests remote work, the evaluation unit can also take that preference into account when conducting the evaluation. Furthermore, if an applicant is applying from overseas, the evaluation unit can also take into account their cultural background when conducting the evaluation. In this way, by conducting the evaluation taking into account the geographic distribution of applicants, it is possible to conduct an evaluation that reflects regional characteristics. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input applicant geographic distribution data into the generation AI and have the generation AI perform the evaluation.

[0086] During the evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to the applicant's related literature. The evaluation unit can, for example, evaluate the applicant's expertise by referring to papers and articles written by the applicant. The evaluation unit can also evaluate the applicant's influence by referring to literature cited by the applicant. The evaluation unit can also evaluate the applicant's achievements by referring to the results of research projects in which the applicant participated. This allows for an accurate evaluation of the applicant's expertise and achievements by referring to the applicant's related literature. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or without AI. For example, the evaluation unit can input the applicant's related literature data into the generation AI and have the generation AI improve the accuracy of the evaluation.

[0087] The evaluation unit can take into account the market value of the applicant when evaluating them. For example, if the applicant's skill set is highly valued in the market, the evaluation unit can take that market value into account when evaluating them. Furthermore, if the applicant's experience is in demand in the market, the evaluation unit can also emphasize that experience when evaluating them. Furthermore, if the applicant's qualifications or certifications are highly valued in the market, the evaluation unit can also consider those qualifications or certifications when evaluating them. In this way, by performing an evaluation taking into account the applicant's market value, it is possible to perform an evaluation that meets the needs of the company. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's market value data into the generation AI and have the generation AI perform the evaluation.

[0088] The suggestion unit can estimate the employee's emotions and adjust the suggestion method based on the estimated employee's emotions. For example, if the employee is relaxed, the suggestion unit can provide detailed suggestions and clarify future prospects. If the employee is feeling stressed, the suggestion unit can provide simple and easy-to-understand suggestions to reduce the employee's burden. If the employee is highly motivated, the suggestion unit can provide suggestions including challenging goals to increase motivation. By adjusting the suggestion method according to the employee's emotions, optimal suggestions can be provided to the employee. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input the employee's facial expression data into the generation AI and have the generation AI adjust the suggestion method.

[0089] When making a proposal, the proposal unit can select a proposal method by analyzing the employee's past career behavior. For example, the proposal unit can make a similar proposal by referring to a career path that the employee has been successful in the past. The proposal unit can also make a different proposal to help the employee avoid a career path that has failed them in the past. The proposal unit can also analyze the employee's past career behavior and select an optimal proposal method. This makes it possible to select an optimal proposal method based on the employee's past career behavior. Some or all of the above-mentioned processing in the proposal unit can be performed using, for example, AI, or can be performed without using AI. For example, the proposal unit can input the employee's past career behavior data into the generation AI and have the generation AI select a proposal method.

[0090] When making a proposal, the suggestion unit can customize the proposed means based on the employee's current living situation. For example, if an employee wishes to work remotely due to family circumstances, the suggestion unit can make a proposal based on that preference. Furthermore, if an employee wishes to do light work for health reasons, the suggestion unit can make a proposal based on that preference. Furthermore, if an employee wishes to balance work with their studies, the suggestion unit can make a proposal based on that preference. This makes it possible to provide optimal proposals based on the employee's current living situation. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the employee's living situation data into the generation AI and cause the generation AI to customize the proposed means.

[0091] The suggestion unit can improve the suggestion method by reflecting employee feedback when making a suggestion. For example, the suggestion unit can adjust the suggestion method based on feedback provided by employees in the past to make more effective suggestions. The suggestion unit can also identify, from employee feedback, that a particular suggestion method is effective and use it preferentially. The suggestion unit can also analyze employee feedback and customize the suggestion method to make more accurate suggestions. This allows the suggestion method to be improved based on employee feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input employee feedback data into a generation AI and cause the generation AI to improve the suggestion method.

[0092] The suggestion unit can estimate the employee's emotions and prioritize suggestions based on the estimated employee emotions. For example, if the employee is feeling stressed, the suggestion unit can prioritize suggestions related to stress reduction. Furthermore, if the employee is highly motivated, the suggestion unit can prioritize suggestions related to career advancement. Furthermore, if the employee is tired, the suggestion unit can prioritize suggestions related to work-life balance. This allows the priority of suggestions to be determined based on the employee's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, AI, or without AI. For example, the suggestion unit can input the employee's facial expression data into the generation AI and have the generation AI determine the priority of suggestions.

[0093] When making a proposal, the suggestion unit can select a proposal method taking into account the employee's geographic location information. For example, if the employee lives in a specific area, the suggestion unit can prioritize proposals for career options and opportunities related to that area. Furthermore, if the employee desires remote work, the suggestion unit can also make proposals related to remote work. Furthermore, if the employee desires to work in a specific city, the suggestion unit can also suggest career options related to that city. This allows optimal proposals to be provided based on the employee's geographic location information. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the employee's geographic location information into the generation AI and cause the generation AI to select a proposal method.

[0094] When making a proposal, the suggestion unit can analyze the employee's social media activity to suggest a proposal method. For example, the suggestion unit can analyze career-related posts shared by the employee on social media and make relevant proposals. The suggestion unit can also identify areas of interest and skills from the employee's social media activity and make proposals based on the identified areas of interest. The suggestion unit can also analyze the employee's social media network and suggest related career options. This makes it possible to provide optimal proposals based on the employee's social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the employee's social media data into a generation AI and have the generation AI select a proposal method.

[0095] When making a proposal, the suggestion unit can customize the suggestion method by reflecting the employee's past feedback. For example, the suggestion unit can adjust the suggestion method based on feedback provided by the employee in the past to make more effective suggestions. The suggestion unit can also identify, from the employee's feedback, that a particular suggestion method is effective and use it preferentially. The suggestion unit can also analyze the employee's feedback and customize the suggestion method to make more accurate suggestions. This allows the suggestion method to be customized based on the employee's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input employee feedback data into the generation AI and cause the generation AI to customize the suggestion method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and proposal unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect facial expressions and voices of employees using the camera 42 and microphone 38B of the smart device 14, and estimate the employee's emotions using the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected information and proposes an optimal career plan. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, analyzes applicants' resumes and evaluates their aptitude and skills. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal personnel placement. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect facial expressions and voices of employees using the camera 42 and microphone 238 of the smart glasses 214 and estimate the employee's emotions using the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected information and proposes an optimal career plan. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, analyzes applicants' resumes and evaluates their aptitude and skills. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal personnel placement. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and proposal 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 can collect facial expressions and voices of employees using the camera 42 and microphone 238 of the headset-type terminal 314 and estimate the employee's emotions using the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected information and proposes an optimal career plan. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the resumes of applicants and evaluates their aptitude and skills. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal personnel placement. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, evaluation unit, and proposal 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 can collect facial expressions and voices of employees using the camera 42 and microphone 238 of the robot 414 and estimate the employee's emotions using the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the collected information and proposes an optimal career plan. The evaluation unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the resumes of applicants and evaluates their aptitude and skills. The proposal unit, realized by the specific processing unit 290 of the data processing device 12, proposes optimal personnel placement.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can collect employee health data and adjust career plans based on their health condition. For example, if an employee has a chronic health problem, the analysis unit can propose a career plan that takes that health condition into consideration. Also, if an employee has a specific health risk based on the results of a health check, the analysis unit can provide a career plan to mitigate that risk. Furthermore, if an employee is aiming to improve their health, the analysis unit can propose a career plan that supports a healthy lifestyle. This makes it possible to provide the optimal career plan based on the employee's health condition.

[0098] The collection department can collect information about employees' hobbies and interests and suggest career plans based on that information. For example, if an employee is interested in a particular sport, it can suggest projects or roles related to that sport. If an employee is interested in art or music, it can provide career options that utilize that interest. Furthermore, if an employee is active in volunteer activities, it can suggest career plans that support those activities. This makes it possible to provide career plans based on employees' hobbies and interests.

[0099] The evaluation department can analyze applicants' social media activities and reflect their personality and values ​​in their evaluation. For example, they can evaluate their teamwork and leadership skills based on the content they share on social media. They can also determine whether an applicant's posts fit the company culture. They can also analyze the reactions of applicants' networks and followers to evaluate their social influence. This allows for a comprehensive evaluation based on applicants' social media activities.

[0100] The proposal department can propose career plans taking into account the employee's family structure. For example, if an employee is raising a child, it can propose career options that offer comprehensive childcare support. Also, if an employee has a family member who requires care, it can provide a career plan that allows for care support. Furthermore, if an employee wishes to work away from home, it can propose a career plan that meets that preference. This makes it possible to provide the optimal career plan that suits the employee's family structure.

[0101] The analysis unit can analyze an employee's learning history and propose a learning plan for improving their skills. For example, it can suggest the next skills and knowledge an employee should learn based on the training and courses they have taken in the past. Also, if an employee wants to deepen their expertise in a specific field, it can provide a learning plan related to that field. It can also suggest a learning plan for employees to keep up with new technologies and trends. This makes it possible to provide the optimal skill improvement plan based on the employee's learning history.

[0102] The collection unit can estimate the employee's emotions and determine the priority of information to be collected based on the estimated employee emotions. For example, if the employee is feeling stressed, the collection unit can prioritize collecting information about their desires and needs regarding stress reduction. Also, if the employee is highly motivated, the collection unit can prioritize collecting information about their desires and needs regarding career advancement. Also, if the employee is tired, the collection unit can prioritize collecting information about their desires and needs regarding work-life balance. In this way, the priority of information to be collected can be determined according to the employee's emotions.

[0103] The analysis unit can estimate an employee's emotions and adjust the way the career plan is presented based on the estimated employee's emotions. For example, if an employee is relaxed, a detailed career plan can be provided to clarify future prospects. If an employee is feeling stressed, a simple and easy-to-understand career plan can be provided to reduce the employee's burden. If an employee is highly motivated, a career plan including challenging goals can be provided to increase motivation. This makes it possible to adjust the way the career plan is presented depending on the employee's emotions.

[0104] The evaluation unit can estimate the emotions of the applicant and adjust the evaluation criteria for the resume based on the estimated emotions of the applicant. For example, if the applicant is nervous, the evaluation criteria can be adjusted so that the applicant can be evaluated in a relaxed state. Also, if the applicant is confident, challenging evaluation criteria can be applied to maximize the evaluation of the applicant's skills. Also, if the applicant is anxious, gentle evaluation criteria can be applied to provide a sense of security. In this way, the evaluation criteria for the resume can be adjusted according to the emotions of the applicant.

[0105] The proposal unit can estimate an employee's emotions and adjust the proposal method based on the estimated employee's emotions. For example, if an employee is relaxed, detailed proposals can be made to clarify future prospects. If an employee is feeling stressed, simple and easy-to-understand proposals can be made to reduce the employee's burden. If an employee is highly motivated, proposals including challenging goals can be made to increase their motivation. In this way, by adjusting the proposal method according to the employee's emotions, it is possible to provide the optimal proposals for the employee.

[0106] The suggestion unit can estimate the employee's emotions and determine the priority of suggestions based on the estimated employee emotions. For example, if an employee is feeling stressed, suggestions related to stress reduction can be given priority. Also, if an employee is highly motivated, suggestions related to career advancement can be given priority. Also, if an employee is tired, suggestions related to work-life balance can be given priority. In this way, the priority of suggestions can be determined according to the employee's emotions.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection department collects employee hopes and needs. Employee hopes and needs include career advancement, work location, and working hours. The collection department collects information such as what skills employees would like to acquire in the future and what position they would like to hold. The collection department can also estimate employees' emotions and adjust the timing of collecting hopes and needs based on the estimated emotions of employees. For example, if an employee is feeling stressed, the collection department will collect hopes and needs during break time or in a relaxing environment so that the employee is in a relaxed state. Step 2: The analysis department analyzes the information collected by the collection department and proposes optimal career plans. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. For example, based on the collected information, the analysis department suggests what skills employees should acquire in the future and what positions they should hold. Step 3: The evaluation department analyzes the applicant's resume and evaluates their aptitude and skills. The evaluation of aptitude and skills includes technical skills, communication ability, leadership, etc. For example, the evaluation department analyzes the applicant's past experience and skill set to determine whether they match the company's needs. Step 4: The proposal department proposes the right personnel placement based on the information obtained by the evaluation department. The right personnel placement is based on the job content, team composition, project requirements, etc. For example, if an applicant is familiar with a specific technology, the proposal department will propose a project that can utilize that technology.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The 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.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 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.

[0121] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the 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.

[0124] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] The data processing system 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the 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.

[0140] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] The data processing system 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects employee wishes and needs; an analysis unit that analyzes the information collected by the collection unit and proposes a career plan; The evaluation department analyzes applicants' resumes and evaluates their aptitude and skills. a proposal unit that proposes an optimal personnel placement based on the information obtained by the evaluation unit; Equipped with A system characterized by:

2. The collecting unit Estimate employee emotions and adjust the timing of collecting their wants and needs based on the estimated emotions. The system of claim 1 .

3. The collecting unit Analyze employees' past career histories and select collection methods The system of claim 1 .

4. The collecting unit When collecting wants and needs, filter them based on employees' current projects and areas of interest The system of claim 1 .

5. The collecting unit When collecting wants and needs, select the collection method according to the employee's input method. The system of claim 1 .

6. The collecting unit Estimate employee sentiment and prioritize information collection based on the estimated sentiment The system of claim 1 .

7. The collecting unit When gathering wants and needs, prioritize the most relevant information by taking into account employees' geographic location. The system of claim 1 .

8. The collecting unit Analyze employees' social media activity and gather relevant information when gathering their wants and needs. The system of claim 1 .

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