System

Abstract

An object of a system according to an embodiment is to automatically determine an optimal placement destination in consideration of the orientation and tendency of a new employee.SOLUTION: A system includes an information input part, an analysis part, a proposal part, a matching degree calculation part, and an assignment determination part. The information input unit inputs information on a new employee. The analysis part analyzes the information of the new employee input by the information input part. The proposal unit proposes an optimum placement destination and a carrier map on the basis of the information analyzed by the analysis unit. A matching degree calculation part calculates a matching degree between the intention and tendency of the new employee and the department information of the assignment candidate. The allocation determination unit automatically determines an allocation destination based on the matching degree calculated by the matching degree calculation 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] With conventional technology, it was difficult to fully consider individual inclinations and tendencies when deciding where new employees should be assigned, which placed a heavy burden on human resources personnel.

[0005] The system according to the embodiment aims to automatically determine the most suitable department to assign new employees to, taking into consideration their inclinations and tendencies. [Means for solving the problem]

[0006] The system according to the embodiment includes an information input unit, an analysis unit, a proposal unit, a match degree calculation unit, and an assignment determination unit. The information input unit inputs information about the new employee. The analysis unit analyzes the information about the new employee input by the information input unit. The proposal unit proposes an optimal assignment and career map based on the information analyzed by the analysis unit. The match degree calculation unit calculates the degree of match between the new employee's inclinations and tendencies and the department information of the assignment candidate. The assignment determination unit automatically determines the assignment based on the degree of match calculated by the match degree calculation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically determine the most suitable assignment for a new employee, taking into consideration their inclinations and tendencies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The career finder system according to an embodiment of the present invention is a system that proposes optimal assignments and career maps based on information about new employees, and calculates the degree of match between the new employee's inclinations and tendencies and the department information of potential assignments for human resources personnel. As a result, the career finder system can find assignments that match the new employee's skills and aspirations, and human resources personnel can efficiently decide on assignments.

[0029] A career finder system according to an embodiment includes an information input unit, an analysis unit, a proposal unit, a match degree calculation unit, and an assignment determination unit. The information input unit inputs information about new employees. For example, the new employees input information such as their skills, experience, and assignment preferences. The information input unit can also collect information about new employees via LINE. The analysis unit analyzes the information about new employees input by the information input unit. For example, the analysis unit analyzes the skills and experience of new employees using data mining technology. The analysis unit can also analyze the assignment preferences of new employees using statistical analysis methods. The proposal unit proposes an optimal assignment and career map based on the information analyzed by the analysis unit. For example, the proposal unit proposes an optimal assignment for the new employee using a generation AI. The proposal unit can also propose a career map for the new employee using the generation AI. The match degree calculation unit calculates the degree of match between the inclinations and tendencies of the new employee and department information of the assignment candidate. For example, the match degree calculation unit calculates the match degree based on information such as the new employee's skills, experience, and assignment preferences, as well as the work content and required skill set of each department. The match degree calculation unit can also calculate the match degree using a generation AI. The assignment determination unit automatically determines the assignment destination based on the match degree calculated by the match degree calculation unit. For example, the assignment determination unit automatically determines the assignment destination using a global optimization algorithm. The assignment determination unit can also automatically determine the assignment destination using a generation AI. As a result, the career finder system according to the embodiment proposes the optimal assignment destination and career map based on the new employee's information and automatically determines the assignment destination, thereby significantly reducing the workload of human resources personnel. For example, the career finder system proposes the optimal assignment destination based on the new employee's information and automatically determines the assignment destination, thereby reducing the burden on human resources personnel. Furthermore, the career finder system proposes a career map for the new employee, thereby clarifying the new employee's career path and creating a specific action plan toward their goal.

[0030] The suggestion unit uses the generation AI to refer to past success and failure cases based on the new employee's input information and suggest the optimal assignment. For example, based on the skills, experience, and assignment preferences entered by the new employee, the suggestion unit refers to past success and failure cases and suggests the optimal assignment. For example, it may prioritize departments where employees with a similar skill set have been successful in the past. The suggestion unit also analyzes past assignment data and suggests assignments based on which departments new employees with a similar background have been successful in. For example, for a new employee with marketing experience, it may refer to past success cases in the marketing department to determine the assignment. The suggestion unit also considers past failure cases based on the new employee's input information and suggests assignments that will avoid the same mistakes. For example, it may avoid assigning the new employee to a department that lacks a specific skill set. This allows for more accurate assignment suggestions by referring to past success and failure cases.

[0031] The proposal department can collect information on new employees' values ​​and lifestyles in addition to their skills and experience, and suggest assignments based on that information. For example, the proposal department can collect information on new employees' personal values ​​and lifestyles in addition to their skills and experience, and suggest assignments based on that information. For example, for a new employee who values ​​work-life balance, the proposal department can suggest a department that offers flexible working hours. The proposal department also has the generation AI conduct a survey on the new employee's values ​​and lifestyles, and suggest assignments based on the results. For example, for a new employee who values ​​teamwork, the proposal department can suggest a department with a cooperative culture. The proposal department also has the generation AI suggest the most suitable assignment based on information about the new employee's lifestyle. For example, for a new employee who wishes to work remotely, the proposal department can suggest a department that allows remote work. This makes it possible to suggest more suitable assignments based on the new employee's values ​​and lifestyle.

[0032] In the proposal department, the generative AI can suggest different industries and job types in response to new employee assignment preferences, thereby promoting career diversity. For example, in the proposal department, the generative AI can suggest different industries and job types in response to new employee assignment preferences. For example, a new employee who wants to work in marketing can be presented with the possibility of sales or product management. The proposal department also uses the generative AI to analyze new employees' skill sets and evaluate their suitability for different industries and job types. For example, a new employee with data analysis skills can be presented with the possibility of working in the IT or research and development department. In addition to the new employee's assignment preferences, the generative AI can provide information on different industries and job types to promote career diversity. For example, a new employee who wants to work in a creative field can be presented with the possibility of working in the design or advertising department. This allows the generative AI to suggest different industries and job types in order to promote career diversity for new employees.

[0033] The proposal department can also have the generative AI suggest appropriate training programs and skill-up opportunities based on the skills and experience of new employees. For example, the proposal department has the generative AI suggest appropriate training programs based on the skills and experience of new employees. For example, for a new employee with data analysis skills, it would suggest a data science training program. The proposal department also has the generative AI analyze the skill set of a new employee and suggest skill-up opportunities. For example, for a new employee with programming skills, it would suggest training in the latest programming languages. The proposal department also has the generative AI identify the skills necessary for career advancement based on the experience of the new employee and suggest training programs based on those. For example, it would suggest training to improve leadership skills. This makes it possible to suggest appropriate training programs and skill-up opportunities based on the skills and experience of new employees.

[0034] The match degree calculation unit can calculate a match degree that takes into account team dynamics and cultural compatibility in addition to the match degree of the new employee. For example, the generation AI calculates a match degree that takes into account team dynamics and cultural compatibility in addition to the match degree of the new employee. For example, it proposes new employees who are compatible with the team's communication style and culture. Furthermore, the match degree calculation unit calculates a match degree by having the generation AI consider not only the new employee's skills and experience, but also the team dynamics and cultural compatibility. For example, it proposes new employees who are compatible with the team's values ​​and work style. Furthermore, when calculating the match degree of the new employee, the generation AI considers team dynamics and cultural compatibility. For example, it proposes new employees who are compatible with the team's atmosphere and work style. This makes it possible to calculate a match degree that takes into account team dynamics and cultural compatibility.

[0035] The assignment determination unit uses a global optimization algorithm to determine assignments by taking into account not only the skills and experience of new employees, but also their career goals and ambitions. For example, the global optimization algorithm determines assignments by taking into account not only the skills and experience of new employees, but also their career goals and ambitions. For example, for a new employee aiming for a management position in the future, the assignment determination unit suggests a department where they can develop leadership skills. Furthermore, the assignment determination unit uses a generative AI to analyze the new employee's career goals and ambitions, and the global optimization algorithm determines assignments based on that analysis. For example, for a new employee who wants to improve their technical skills, the system suggests a department that offers comprehensive technical training. Furthermore, the assignment determination unit uses a global optimization algorithm to determine assignments by taking into account not only the skills and experience of new employees, but also their career goals and ambitions. For example, for a new employee aiming for an international career, the system suggests a department where they can participate in overseas projects. This allows the optimal assignment to be determined by taking into account the new employee's career goals and ambitions.

[0036] The assignment determination unit uses a global optimization algorithm to learn past assignment results and subsequent performance data, allowing it to propose more accurate assignments. For example, the assignment determination unit uses a global optimization algorithm to learn past assignment results and subsequent performance data to propose more accurate assignments. For example, it determines the assignment of a new employee based on past successful assignment patterns. In addition, the assignment determination unit uses a generation AI to analyze past assignment results and performance data, and the global optimization algorithm learns that data to propose assignments. For example, it proposes departments in which new employees with similar skill sets have been successful. In addition, the assignment determination unit uses a global optimization algorithm to learn past assignment results and performance data to propose more accurate assignments. For example, it prioritizes suggesting departments where a specific skill set has received high evaluations. In this way, by learning past assignment results and performance data, it is possible to propose more accurate assignments.

[0037] The assignment determination unit can simulate different scenarios using a global optimization algorithm and propose multiple optimal assignments. For example, the assignment determination unit simulates different scenarios using a global optimization algorithm and proposes multiple optimal assignments. For example, it compares multiple assignment scenarios and selects the most suitable assignment. The assignment determination unit also simulates scenarios using different generation AIs and proposes multiple optimal assignments using a global optimization algorithm. For example, it presents multiple assignments based on skill sets and career goals. The assignment determination unit also simulates different scenarios using a global optimization algorithm and proposes multiple optimal assignments. For example, it proposes assignments based on short-term goals and long-term goals. In this way, it is possible to simulate different scenarios and propose multiple optimal assignments.

[0038] The assignment determination unit uses a global optimization algorithm to monitor the performance of new employees after assignment and propose reassignment as necessary. For example, the assignment determination unit uses a global optimization algorithm to monitor the performance of new employees after assignment and propose reassignment as necessary. For example, if performance declines, it proposes reassignment to an appropriate department. In addition, the assignment determination unit uses a generative AI to analyze the performance data of new employees after assignment and a global optimization algorithm to propose reassignment. For example, it proposes reassignment to a department that suits their skill set. In addition, the assignment determination unit uses a global optimization algorithm to monitor the performance of new employees after assignment and propose reassignment as necessary. For example, it proposes reassignment to a department that suits their career goals. In this way, by monitoring the performance of new employees after assignment and proposing reassignment as necessary, it is possible to maintain appropriate assignments.

[0039] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0040] The career finder system can further include a health management unit that monitors the health of new employees. The health management unit collects and analyzes the health data of new employees. For example, it may use a wearable device to monitor heart rate and sleep patterns and evaluate their health status. The health management unit can also suggest appropriate assignments based on the health data. For example, it may suggest departments with low stress or departments that offer a healthy environment. This allows the system to suggest assignments that take into account the health status of new employees.

[0041] The career finder system can further include a hobby analysis unit that suggests assignments based on the hobbies and interests of new employees. The hobby analysis unit collects and analyzes information about the hobbies and interests of new employees. For example, for a new employee who likes sports, it would suggest a department in charge of sports-related projects. The hobby analysis unit can also suggest assignments that new employees can enjoy based on their hobbies and interests. For example, for a new employee who is interested in art, it would suggest a department that offers creative work. This makes it possible to suggest assignments that take into account the hobbies and interests of new employees.

[0042] The career finder system can further include a learning style analysis unit that suggests assignments based on the learning style of new employees. The learning style analysis unit collects and analyzes information about the learning style of new employees. For example, for a new employee who prefers visual learning, it suggests a department that makes heavy use of visual content. The learning style analysis unit can also suggest assignments where new employees can work efficiently based on their learning style. For example, for a new employee who prefers practical learning, it suggests a department that offers extensive hands-on training. This makes it possible to suggest assignments that take into account the learning style of new employees.

[0043] The career finder system can further include a network analysis unit that analyzes the new employee's social network and suggests an assignment. The network analysis unit collects and analyzes data on the new employee's social media and professional network. For example, it suggests a department where there are many seniors who graduated from the same university. The network analysis unit can also suggest an assignment where the new employee can smoothly adapt based on the new employee's social network. For example, it suggests a department where there are many colleagues who share common hobbies and interests. This makes it possible to suggest an assignment that takes into account the new employee's social network.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The information input unit inputs information about new employees. For example, new employees input information such as their skills, experience, and desired assignment. The information input unit can also collect information about new employees via LINE. Step 2: The analysis unit analyzes the information about the new employee input by the information input unit. For example, the analysis unit may use data mining technology to analyze the skills and experience of the new employee. The analysis unit may also use statistical analysis techniques to analyze the new employee's assignment preferences. Step 3: The proposal unit proposes the optimal assignment and career map based on the information analyzed by the analysis unit. For example, the proposal unit uses the generation AI to propose the optimal assignment for a new employee. The proposal unit can also use the generation AI to propose a career map for the new employee. Step 4: The matching degree calculation unit calculates the degree of match between the new employee's inclinations and tendencies and the department information of the candidate for assignment. For example, the matching degree calculation unit calculates the degree of match based on information such as the new employee's skills, experience, and assignment preferences, as well as the work content and required skill sets of each department. The matching degree calculation unit can also calculate the degree of match using generation AI. Step 5: The assignment determination unit automatically determines the assignment destination based on the match degree calculated by the match degree calculation unit. For example, the assignment determination unit automatically determines the assignment destination using a global optimization algorithm. The assignment determination unit can also automatically determine the assignment destination using a generation AI.

[0046] (Example 2) The career finder system according to an embodiment of the present invention is a system that proposes optimal assignments and career maps based on information about new employees, and calculates the degree of match between the new employee's inclinations and tendencies and the department information of potential assignments for human resources personnel. As a result, the career finder system can find assignments that match the new employee's skills and aspirations, and human resources personnel can efficiently decide on assignments.

[0047] A career finder system according to an embodiment includes an information input unit, an analysis unit, a proposal unit, a match degree calculation unit, and an assignment determination unit. The information input unit inputs information about new employees. For example, the new employees input information such as their skills, experience, and assignment preferences. The information input unit can also collect information about new employees via LINE. The analysis unit analyzes the information about new employees input by the information input unit. For example, the analysis unit analyzes the skills and experience of new employees using data mining technology. The analysis unit can also analyze the assignment preferences of new employees using statistical analysis methods. The proposal unit proposes an optimal assignment and career map based on the information analyzed by the analysis unit. For example, the proposal unit proposes an optimal assignment for the new employee using a generation AI. The proposal unit can also propose a career map for the new employee using the generation AI. The match degree calculation unit calculates the degree of match between the inclinations and tendencies of the new employee and department information of the assignment candidate. For example, the match degree calculation unit calculates the match degree based on information such as the new employee's skills, experience, and assignment preferences, as well as the work content and required skill set of each department. The match degree calculation unit can also calculate the match degree using a generation AI. The assignment determination unit automatically determines the assignment destination based on the match degree calculated by the match degree calculation unit. For example, the assignment determination unit automatically determines the assignment destination using a global optimization algorithm. The assignment determination unit can also automatically determine the assignment destination using a generation AI. As a result, the career finder system according to the embodiment proposes the optimal assignment destination and career map based on the new employee's information and automatically determines the assignment destination, thereby significantly reducing the workload of human resources personnel. For example, the career finder system proposes the optimal assignment destination based on the new employee's information and automatically determines the assignment destination, thereby reducing the burden on human resources personnel. Furthermore, the career finder system proposes a career map for the new employee, thereby clarifying the new employee's career path and creating a specific action plan toward their goal.

[0048] The suggestion unit uses the generation AI to refer to past success and failure cases based on the new employee's input information and suggest the optimal assignment. For example, based on the skills, experience, and assignment preferences entered by the new employee, the suggestion unit refers to past success and failure cases and suggests the optimal assignment. For example, it may prioritize departments where employees with a similar skill set have been successful in the past. The suggestion unit also analyzes past assignment data and suggests assignments based on which departments new employees with a similar background have been successful in. For example, for a new employee with marketing experience, it may refer to past success cases in the marketing department to determine the assignment. The suggestion unit also considers past failure cases based on the new employee's input information and suggests assignments that will avoid the same mistakes. For example, it may avoid assigning the new employee to a department that lacks a specific skill set. This allows for more accurate assignment suggestions by referring to past success and failure cases.

[0049] The proposal department can collect information on new employees' values ​​and lifestyles in addition to their skills and experience, and suggest assignments based on that information. For example, the proposal department can collect information on new employees' personal values ​​and lifestyles in addition to their skills and experience, and suggest assignments based on that information. For example, for a new employee who values ​​work-life balance, the proposal department can suggest a department that offers flexible working hours. The proposal department also has the generation AI conduct a survey on the new employee's values ​​and lifestyles, and suggest assignments based on the results. For example, for a new employee who values ​​teamwork, the proposal department can suggest a department with a cooperative culture. The proposal department also has the generation AI suggest the most suitable assignment based on information about the new employee's lifestyle. For example, for a new employee who wishes to work remotely, the proposal department can suggest a department that allows remote work. This makes it possible to suggest more suitable assignments based on the new employee's values ​​and lifestyle.

[0050] The suggestion unit can use the emotion estimation function to analyze the emotions felt by new employees when they enter data, and suggest an assignment that will bring out positive emotions. For example, the suggestion unit uses the emotion estimation function to analyze the emotions felt by new employees when they enter data, and suggest an assignment that will bring out positive emotions. For example, it preferentially suggests departments where positive emotions are strong when entering data. The suggestion unit also analyzes the facial expressions and voice of new employees when they enter data, and estimates their emotional state. It suggests an assignment that will bring out positive emotions. For example, it suggests a department where people smile a lot. The suggestion unit also uses the emotion estimation function to suggest an assignment that will reduce the stress and anxiety felt by new employees when entering data. For example, it suggests a department that provides a relaxing environment. In this way, it is possible to suggest an assignment that will bring out positive emotions in new employees.

[0051] In the proposal department, the generative AI can suggest different industries and job types in response to new employee assignment preferences, thereby promoting career diversity. For example, in the proposal department, the generative AI can suggest different industries and job types in response to new employee assignment preferences. For example, a new employee who wants to work in marketing can be presented with the possibility of sales or product management. The proposal department also uses the generative AI to analyze new employees' skill sets and evaluate their suitability for different industries and job types. For example, a new employee with data analysis skills can be presented with the possibility of working in the IT or research and development department. In addition to the new employee's assignment preferences, the generative AI can provide information on different industries and job types to promote career diversity. For example, a new employee who wants to work in a creative field can be presented with the possibility of working in the design or advertising department. This allows the generative AI to suggest different industries and job types in order to promote career diversity for new employees.

[0052] The proposal department can also have the generative AI suggest appropriate training programs and skill-up opportunities based on the skills and experience of new employees. For example, the proposal department has the generative AI suggest appropriate training programs based on the skills and experience of new employees. For example, for a new employee with data analysis skills, it would suggest a data science training program. The proposal department also has the generative AI analyze the skill set of a new employee and suggest skill-up opportunities. For example, for a new employee with programming skills, it would suggest training in the latest programming languages. The proposal department also has the generative AI identify the skills necessary for career advancement based on the experience of the new employee and suggest training programs based on those. For example, it would suggest training to improve leadership skills. This makes it possible to suggest appropriate training programs and skill-up opportunities based on the skills and experience of new employees.

[0053] The suggestion unit uses the emotion estimation function to monitor the emotions of new employees when they choose their assignment in real time, and can support them in making the optimal choice. The suggestion unit, for example, uses the emotion estimation function to monitor the emotions of new employees when they choose their assignment in real time. For example, when presenting options, it prioritizes displaying assignments with strong positive emotions. The suggestion unit also analyzes the facial expressions and voices of new employees when they choose their assignment, and monitors their emotional state in real time. For example, if they are feeling stressed or anxious, it suggests an assignment where they can relax. The suggestion unit also uses the emotion estimation function to collect emotional data when new employees choose their assignment, and supports them in making the optimal choice. For example, it prioritizes suggesting assignments with high emotion scores. This makes it possible to monitor the emotions of new employees when they choose their assignment in real time, and support them in making the optimal choice.

[0054] The match degree calculation unit can calculate a match degree that takes into account team dynamics and cultural compatibility in addition to the match degree of the new employee. For example, the generation AI calculates a match degree that takes into account team dynamics and cultural compatibility in addition to the match degree of the new employee. For example, it proposes new employees who are compatible with the team's communication style and culture. Furthermore, the match degree calculation unit calculates a match degree by having the generation AI consider not only the new employee's skills and experience, but also the team dynamics and cultural compatibility. For example, it proposes new employees who are compatible with the team's values ​​and work style. Furthermore, when calculating the match degree of the new employee, the generation AI considers team dynamics and cultural compatibility. For example, it proposes new employees who are compatible with the team's atmosphere and work style. This makes it possible to calculate a match degree that takes into account team dynamics and cultural compatibility.

[0055] The match degree calculation unit can use the emotion estimation function to analyze the emotional state of the new employee and suggest an emotionally stable assignment. The match degree calculation unit, for example, uses the emotion estimation function to analyze the emotional state of the new employee and suggest an emotionally stable assignment. For example, it can suggest a department with less stress and anxiety. The match degree calculation unit also uses the generation AI to analyze the emotional state of the new employee and suggest an emotionally stable assignment. For example, it can suggest a department that provides a relaxing environment. The match degree calculation unit also uses the emotion estimation function to monitor the emotional state of the new employee in real time and suggest an emotionally stable assignment. For example, it can suggest a department with strong positive emotions. In this way, it is possible to analyze the emotional state of the new employee and suggest an emotionally stable assignment.

[0056] The assignment determination unit uses a global optimization algorithm to determine assignments by taking into account not only the skills and experience of new employees, but also their career goals and ambitions. For example, the global optimization algorithm determines assignments by taking into account not only the skills and experience of new employees, but also their career goals and ambitions. For example, for a new employee aiming for a management position in the future, the assignment determination unit suggests a department where they can develop leadership skills. Furthermore, the assignment determination unit uses a generative AI to analyze the new employee's career goals and ambitions, and the global optimization algorithm determines assignments based on that analysis. For example, for a new employee who wants to improve their technical skills, the system suggests a department that offers comprehensive technical training. Furthermore, the assignment determination unit uses a global optimization algorithm to determine assignments by taking into account not only the skills and experience of new employees, but also their career goals and ambitions. For example, for a new employee aiming for an international career, the system suggests a department where they can participate in overseas projects. This allows the optimal assignment to be determined by taking into account the new employee's career goals and ambitions.

[0057] The assignment determination unit uses a global optimization algorithm to learn past assignment results and subsequent performance data, allowing it to propose more accurate assignments. For example, the assignment determination unit uses a global optimization algorithm to learn past assignment results and subsequent performance data to propose more accurate assignments. For example, it determines the assignment of a new employee based on past successful assignment patterns. In addition, the assignment determination unit uses a generation AI to analyze past assignment results and performance data, and the global optimization algorithm learns that data to propose assignments. For example, it proposes departments in which new employees with similar skill sets have been successful. In addition, the assignment determination unit uses a global optimization algorithm to learn past assignment results and performance data to propose more accurate assignments. For example, it prioritizes suggesting departments where a specific skill set has received high evaluations. In this way, by learning past assignment results and performance data, it is possible to propose more accurate assignments.

[0058] The assignment determination unit can use the emotion estimation function to analyze the emotional state of the new employee and automatically determine an assignment that will provide high emotional satisfaction. The assignment determination unit, for example, uses the emotion estimation function to analyze the emotional state of the new employee and automatically determine an assignment that will provide high emotional satisfaction. For example, it may preferentially suggest departments with strong positive emotions. The assignment determination unit also uses a generation AI to analyze the emotional state of the new employee and an overall optimization algorithm to automatically determine an assignment that will provide high emotional satisfaction. For example, it may suggest a department with low stress and anxiety. The assignment determination unit also uses the emotion estimation function to monitor the emotional state of the new employee in real time and automatically determine an assignment that will provide high emotional satisfaction. For example, it may preferentially suggest departments with high emotion scores. This makes it possible to analyze the emotional state of the new employee and automatically determine an assignment that will provide high emotional satisfaction.

[0059] The assignment determination unit can simulate different scenarios using a global optimization algorithm and propose multiple optimal assignments. For example, the assignment determination unit simulates different scenarios using a global optimization algorithm and proposes multiple optimal assignments. For example, it compares multiple assignment scenarios and selects the most suitable assignment. The assignment determination unit also simulates scenarios using different generation AIs and proposes multiple optimal assignments using a global optimization algorithm. For example, it presents multiple assignments based on skill sets and career goals. The assignment determination unit also simulates different scenarios using a global optimization algorithm and proposes multiple optimal assignments. For example, it proposes assignments based on short-term goals and long-term goals. In this way, it is possible to simulate different scenarios and propose multiple optimal assignments.

[0060] The assignment determination unit uses a global optimization algorithm to monitor the performance of new employees after assignment and propose reassignment as necessary. For example, the assignment determination unit uses a global optimization algorithm to monitor the performance of new employees after assignment and propose reassignment as necessary. For example, if performance declines, it proposes reassignment to an appropriate department. In addition, the assignment determination unit uses a generative AI to analyze the performance data of new employees after assignment and a global optimization algorithm to propose reassignment. For example, it proposes reassignment to a department that suits their skill set. In addition, the assignment determination unit uses a global optimization algorithm to monitor the performance of new employees after assignment and propose reassignment as necessary. For example, it proposes reassignment to a department that suits their career goals. In this way, by monitoring the performance of new employees after assignment and proposing reassignment as necessary, it is possible to maintain appropriate assignments.

[0061] The assignment determination unit can use the emotion estimation function to monitor the emotional state of new employees in real time and provide follow-up after assignment. The assignment determination unit, for example, uses the emotion estimation function to monitor the emotional state of new employees in real time and provide follow-up after assignment. For example, if the emotion score is low, it provides appropriate support. The assignment determination unit also uses the generation AI to analyze the emotional state of new employees and provide follow-up in real time. For example, it provides advice on maintaining positive emotions. The assignment determination unit also uses the emotion estimation function to monitor the emotional state of new employees and provide follow-up after assignment. For example, if the emotion score drops, it suggests mental health support. In this way, it is possible to monitor the emotional state of new employees in real time and provide follow-up after assignment, thereby providing appropriate support.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The career finder system can further include a health management unit that monitors the health of new employees. The health management unit collects and analyzes the health data of new employees. For example, it may use a wearable device to monitor heart rate and sleep patterns and evaluate their health status. The health management unit can also suggest appropriate assignments based on the health data. For example, it may suggest departments with low stress or departments that offer a healthy environment. This allows the system to suggest assignments that take into account the health status of new employees.

[0064] The career finder system can further include a hobby analysis unit that suggests assignments based on the hobbies and interests of new employees. The hobby analysis unit collects and analyzes information about the hobbies and interests of new employees. For example, for a new employee who likes sports, it would suggest a department in charge of sports-related projects. The hobby analysis unit can also suggest assignments that new employees can enjoy based on their hobbies and interests. For example, for a new employee who is interested in art, it would suggest a department that offers creative work. This makes it possible to suggest assignments that take into account the hobbies and interests of new employees.

[0065] The career finder system can further include a learning style analysis unit that suggests assignments based on the learning style of new employees. The learning style analysis unit collects and analyzes information about the learning style of new employees. For example, for a new employee who prefers visual learning, it suggests a department that makes heavy use of visual content. The learning style analysis unit can also suggest assignments where new employees can work efficiently based on their learning style. For example, for a new employee who prefers practical learning, it suggests a department that offers extensive hands-on training. This makes it possible to suggest assignments that take into account the learning style of new employees.

[0066] The career finder system can further include a network analysis unit that analyzes the new employee's social network and suggests an assignment. The network analysis unit collects and analyzes data on the new employee's social media and professional network. For example, it suggests a department where there are many seniors who graduated from the same university. The network analysis unit can also suggest an assignment where the new employee can smoothly adapt based on the new employee's social network. For example, it suggests a department where there are many colleagues who share common hobbies and interests. This makes it possible to suggest an assignment that takes into account the new employee's social network.

[0067] The career finder system can also use the emotion estimation function of new employees to evaluate the cultural fit of the new employee's new workplace. For example, the emotion estimation function can be used to analyze the new employee's feelings toward a specific department and suggest a new employee's new workplace that is a cultural fit. For example, the emotion estimation function can be used to suggest a department where the new employee can relax. The emotion estimation function can also be used to suggest a department where the new employee will feel less stressed. This makes it possible to suggest a new employee's new workplace that is a cultural fit that takes into account the new employee's emotions.

[0068] The career finder system can further use the new employee's emotion estimation function to evaluate the team dynamics of the new employee's assignment. For example, the emotion estimation function can be used to analyze the new employee's emotions toward a specific team, and then suggest an assignment that matches the team dynamics. For example, the emotion estimation function can be used to suggest that the new employee be assigned to a cooperative team. The emotion estimation function can also be used to suggest a team in which the new employee will feel less stressed. This makes it possible to suggest an assignment that matches the team dynamics, taking into account the new employee's emotions.

[0069] The career finder system can further use the new employee's emotion estimation function to evaluate the leadership style of the new employee's new assignment. For example, the emotion estimation function can be used to analyze the new employee's emotions toward a specific leadership style and suggest an assignment that suits that leadership style. For example, the emotion estimation function can be used to suggest a department with a leadership style that makes the new employee feel relaxed. The emotion estimation function can also be used to suggest a department with a leadership style that makes the new employee feel less stressed. This makes it possible to suggest an assignment that suits a leadership style that takes the new employee's emotions into consideration.

[0070] The career finder system can further use the emotion estimation function of new employees to evaluate the work-life balance of their new assignment. For example, the emotion estimation function can be used to analyze the emotions that new employees have toward a specific work-life balance, and suggest an assignment that suits their work-life balance. For example, the emotion estimation function can be used to suggest a department with a work-life balance that allows new employees to relax. The emotion estimation function can also be used to suggest a department with a work-life balance that reduces stress in new employees. This makes it possible to suggest an assignment that suits the work-life balance, taking into account the emotions of new employees.

[0071] The career finder system can further use the new employee's emotion estimation function to evaluate the feedback culture of the workplace to which the new employee will be assigned. For example, the emotion estimation function can be used to analyze the emotions that the new employee has toward a specific feedback culture, and then suggest a workplace that is compatible with the feedback culture. For example, the emotion estimation function can be used to suggest a department with a feedback culture that makes the new employee feel relaxed. The emotion estimation function can also be used to suggest a department with a feedback culture that makes the new employee feel less stressed. This makes it possible to suggest a workplace that is compatible with a feedback culture that takes into account the emotions of the new employee.

[0072] The career finder system can further use the emotion estimation function of new employees to evaluate the growth opportunities of the new employee's new assignment. For example, the emotion estimation function can be used to analyze the emotions that the new employee has toward a specific growth opportunity, and then suggest an assignment that matches that growth opportunity. For example, the emotion estimation function can be used to suggest a department with growth opportunities that will allow the new employee to relax. The emotion estimation function can also be used to suggest a department with growth opportunities that will reduce the new employee's stress. This makes it possible to suggest an assignment that matches the growth opportunity, taking into account the new employee's emotions.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The information input unit inputs information about new employees. For example, new employees input information such as their skills, experience, and desired assignment. The information input unit can also collect information about new employees via LINE. Step 2: The analysis unit analyzes the information about the new employee input by the information input unit. For example, the analysis unit may use data mining technology to analyze the skills and experience of the new employee. The analysis unit may also use statistical analysis techniques to analyze the new employee's assignment preferences. Step 3: The proposal unit proposes the optimal assignment and career map based on the information analyzed by the analysis unit. For example, the proposal unit uses the generation AI to propose the optimal assignment for a new employee. The proposal unit can also use the generation AI to propose a career map for the new employee. Step 4: The matching degree calculation unit calculates the degree of match between the new employee's inclinations and tendencies and the department information of the candidate for assignment. For example, the matching degree calculation unit calculates the degree of match based on information such as the new employee's skills, experience, and assignment preferences, as well as the work content and required skill sets of each department. The matching degree calculation unit can also calculate the degree of match using generation AI. Step 5: The assignment determination unit automatically determines the assignment destination based on the match degree calculated by the match degree calculation unit. For example, the assignment determination unit automatically determines the assignment destination using a global optimization algorithm. The assignment determination unit can also automatically determine the assignment destination using a generation AI.

[0075] 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.

[0076] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0142] 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. an information input section for inputting information about new employees; an analysis unit that analyzes the information about the new employee input by the information input unit; a proposal unit that proposes an optimal assignment and career map based on the information analyzed by the analysis unit; a matching degree calculation unit that calculates a matching degree between the inclinations and tendencies of the new employee and department information of the assignment candidate; an assignment determination unit that automatically determines an assignment destination based on the degree of match calculated by the degree of match calculation unit; A system characterized by:

2. The proposal unit Based on the information entered by the new employee, the generation AI refers to past successes and failures and suggests the most suitable assignment.

2. The system of claim 1.

3. The proposal unit In addition to the skills and experience of the new employee, information about the new employee's values ​​and lifestyle is also collected, and assignments are suggested based on this information.

2. The system of claim 1.

4. The proposal unit Analyze the emotions felt by the new employee when they input their information and suggest a position that will elicit positive emotions.

2. The system of claim 1.

5. The proposal unit In response to the new employee's desired assignment, the AI ​​will suggest different industries and job types, promoting career diversity.

2. The system of claim 1.

6. The proposal unit Based on the skills and experience of the new employee, the generative AI will also suggest appropriate training programs and opportunities for skill development.

2. The system of claim 1.

7. The proposal unit Monitor the emotions of new employees in real time when choosing their placement and support them in making the best choice 2. The system of claim 1.

8. The match degree calculation unit In addition to the match score for the new hire, the match score is calculated taking into account team dynamics and cultural compatibility.

2. The system of claim 1.

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