System

Abstract

An object of the system according to the embodiment is to propose an optimal department in consideration of an individual's interest, strength, and weak field.SOLUTION: A system includes an input unit, an analysis unit, and a proposal unit. An input part inputs the interest, strength and weak field of an individual. The analysis unit analyzes the interest, the strength, and the weak field input by the input unit. The proposal unit proposes an optimum department based on the information analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately assign employees to the most appropriate departments, taking into account their individual interests, strengths, and areas of weakness, and there is room for improvement.

[0005] The system according to the embodiment aims to propose the most suitable department for an individual, taking into consideration their interests, strengths, and areas of weakness. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs an individual's interests, strengths, and areas of weakness. The analysis unit analyzes the interests, strengths, and areas of weakness input by the input unit. The suggestion unit suggests the most suitable department based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable department in consideration of an individual's interests, strengths, and areas of weakness. [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 assignment suggestion system according to the embodiment of the present invention is a system that suggests the most suitable department for an individual by taking into consideration the individual's interests, strengths, and areas of weakness. As a result, the assignment suggestion system can suggest the most suitable department for an individual based on their aptitude.

[0029] An assignment suggestion system according to an embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs an individual's interests, strengths, and weaknesses. For example, an individual can input, "I'm interested in marketing and good at data analysis, but I'm not good at presentations." The input unit can also automatically analyze the individual's past work history and project participation history to complement the individual's interests, strengths, and weaknesses. For example, the input unit complements the individual's strengths and weaknesses based on the individual's roles and achievements in past projects. The input unit can also analyze the individual's social media and blog posts to infer the individual's interests, strengths, and weaknesses. For example, if the individual posts frequently about technology, it can infer that the individual is interested in that field. The analysis unit analyzes the interests, strengths, and weaknesses input by the input unit. For example, a generation AI analyzes the input information and comprehensively evaluates the individual's interests, strengths, and weaknesses. The suggestion unit proposes the most appropriate department based on the information analyzed by the analysis unit. For example, if the individual is interested in marketing and good at data analysis, the generation AI can propose the marketing department or the data analysis department. As a result, the assignment suggestion system according to the embodiment can suggest the most suitable department based on the aptitude of an individual.

[0030] The input unit can automatically analyze an individual's past work history and project participation history to complement their interests, strengths, and weak areas. For example, the input unit analyzes an individual's past work history to determine which tasks they performed well in. For example, it complements their strong areas based on their roles and achievements in past projects. The input unit also analyzes their project participation history to determine what projects they were interested in and participated in. For example, if they participated in many technical projects, it determines that they are interested in that field. The input unit also integrates their work history and project history to comprehensively evaluate an individual's strengths and weak areas. For example, if an individual achieved high results in a data analysis project but had few opportunities to give presentations, it complements that they are good at data analysis but not at presentations. This allows for a more accurate evaluation of an individual's aptitude based on their past work history and project participation history.

[0031] The input unit can analyze the content of an individual's social media and blog posts to estimate their interests, strengths, and areas of weakness. For example, the input unit analyzes the content of social media posts to extract frequently mentioned topics and keywords. For example, if there are many technology-related posts, it can be inferred that the individual is interested in that field. The input unit can also analyze the content of blog posts to identify what topics are written in detail. For example, if there are many detailed articles about data analysis, it can be inferred that the individual has strengths in that field. The input unit can also integrate the content of social media and blog posts to comprehensively evaluate the individual's interests, strengths, and areas of weakness. For example, if there are many technology-related posts and few posts about presentations, it can be inferred that the individual has strengths in technology and weaknesses in presentations. This allows for a more accurate evaluation of an individual's aptitude based on the content of social media and blog posts.

[0032] The input unit also allows the user to input information about the user's hobbies and lifestyle, and can suggest departments that take work-life balance into consideration. The input unit, for example, allows the user to input information about the user's hobbies, and suggests departments based on that information. For example, for a person whose hobby is the outdoors, it would suggest a department with a lot of fieldwork. The input unit also allows the user to input information about lifestyle, and suggests departments that take work-life balance into consideration. For example, for a person who needs flexible working hours due to family circumstances, it would suggest a department that allows remote work. The input unit also comprehensively evaluates the information about hobbies and lifestyle, and suggests the most suitable department. For example, for a person who enjoys sports and is very health-conscious, it would suggest a department with many health-related projects. In this way, it is possible to suggest the most suitable department taking into consideration the user's hobbies and lifestyle.

[0033] The input unit allows feedback from team members and superiors to be input, enabling aptitude to be evaluated from a third-party perspective. The input unit, for example, collects feedback from team members and evaluates individual aptitude. For example, areas highly rated by colleagues are supplemented as strengths. The input unit also collects feedback from superiors and evaluates individual aptitude. For example, aptitude is determined based on skills and tasks highly rated by superiors. The input unit also comprehensively evaluates feedback from team members and superiors and suggests the most suitable department. For example, areas where multiple pieces of feedback match are supplemented as strengths. This allows aptitude to be evaluated from a third-party perspective.

[0034] The analysis unit can analyze past assignment data and subsequent work performance, and perform aptitude analysis based on success cases. The analysis unit, for example, analyzes past assignment data and work performance, and performs aptitude analysis based on success cases. For example, it identifies departments that have demonstrated high performance in the past. The analysis unit also analyzes post-assignment work performance data, and performs aptitude analysis based on success cases. For example, it identifies departments where a specific skill set was highly evaluated. The analysis unit also integrates past assignment data and work performance, and performs aptitude analysis based on success cases. For example, it evaluates the aptitude of individuals who have achieved high results in a specific job. This allows aptitude analysis based on success cases, based on past assignment data and work performance.

[0035] The analysis department can perform a detailed analysis of the work content and required skill sets of each department and match them with individual skills. For example, the analysis department can perform a detailed analysis of the work content of each department and identify the required skill sets. For example, the data analysis department requires advanced analytical skills. The analysis department can also analyze individual skill sets and match them with the required skill sets of each department. For example, it can suggest the data analysis department for individuals with high data analysis skills. The analysis department can also comprehensively evaluate the work content of each department and individual skill sets and suggest the most suitable department. For example, it can suggest the data analysis department for individuals with high data analysis skills but who are not good at presentations. This allows for a detailed analysis of the work content of each department and individual skills, enabling optimal matching.

[0036] The analysis unit can refer to data from different industries and companies and perform aptitude analysis based on success stories from other companies. The analysis unit, for example, refers to data from different industries and performs aptitude analysis based on success stories from other companies. For example, it evaluates aptitude based on success stories from other companies in the same industry. The analysis unit also refers to data from other companies and performs aptitude analysis based on success stories. For example, it identifies departments that have demonstrated high performance in other companies. The analysis unit also integrates data from different industries and companies and performs aptitude analysis based on success stories. For example, it evaluates aptitude based on success stories from different industries. This makes it possible to perform aptitude analysis based on data from different industries and companies and with reference to success stories from other companies.

[0037] When proposing departments, the analysis department can take future career paths into consideration and suggest departments where long-term growth can be expected. For example, the analysis department can take future career paths into consideration and suggest departments where long-term growth can be expected. For example, it can prioritize suggesting departments where career advancement is expected. The analysis department can also input individuals' career goals and suggest departments where long-term growth can be expected based on those goals. For example, it can suggest departments that require leadership for an individual who wants to hone their management skills. The analysis department can also comprehensively evaluate future career paths and current skill sets and suggest departments where long-term growth can be expected. For example, it can suggest departments where data analysis skills can be utilized while also honing management skills in the future. This makes it possible to suggest departments where long-term growth can be expected, taking future career paths into consideration.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The analysis unit can analyze an individual's health condition and stress level, and suggest departments that take health management into consideration. For example, if the individual's health condition is not good, it will suggest a department with less stress. The analysis unit also suggests departments that make it easy to manage health based on the individual's health data. For example, if regular exercise is required, it will suggest a department with good fitness facilities. The analysis unit also analyzes the relationship between health condition and work performance, and suggests departments that take into consideration the impact of health management on work. For example, it will suggest a department where work performance will improve as health condition improves. This makes it possible to suggest the optimal department taking into consideration an individual's health condition.

[0040] The analysis department can analyze an individual's learning history and qualification acquisition status, and suggest departments where they can expect to improve their skills. For example, if they have a specific qualification, it will suggest a department where they can make use of that qualification. The analysis department can also suggest departments where they can expect to improve their skills based on an individual's learning history. For example, if they have completed a data science course, it will suggest the data analysis department. The analysis department can also analyze the relationship between learning history and work performance, and suggest departments that take into account the impact that skill improvement will have on work. For example, it will suggest departments where work performance will improve by acquiring new skills. This makes it possible to suggest the most suitable department, taking into account an individual's learning history and qualification acquisition status.

[0041] The analysis unit can analyze an individual's communication style and suggest a department where teamwork can proceed smoothly. For example, if an individual is good at face-to-face communication, it will suggest a department where there are many face-to-face meetings. The analysis unit can also determine whether remote work is suitable based on an individual's communication style and suggest an appropriate department. For example, if an individual is good at remote work, it will suggest a department where remote work is the main focus. The analysis unit can also analyze the relationship between communication style and work performance and suggest a department with an optimal communication environment. For example, for an individual who performs well in a department where teamwork is important, it will suggest a department where teamwork is emphasized. This makes it possible to suggest the optimal department taking into account an individual's communication style.

[0042] The analysis department allows individuals to input their career goals and can suggest departments where they can expect long-term growth based on those goals. For example, for an individual who wants to hone their management skills, it will suggest departments that require leadership. The analysis department also comprehensively evaluates future career paths and current skill sets and suggests departments where they can expect long-term growth. For example, it will suggest departments where they can utilize their data analysis skills while also honing their management skills in the future. The analysis department also analyzes the relationship between career goals and work performance and suggests departments where they can easily achieve their career goals. For example, for an individual with a specific career goal, it will suggest departments where they can easily achieve that goal. This makes it possible to suggest departments where they can expect long-term growth, taking future career paths into consideration.

[0043] The analysis unit can refer to data from different industries and companies and perform aptitude analysis based on success stories from other companies. For example, it can refer to data from different industries and perform aptitude analysis based on success stories from other companies. For example, it can evaluate aptitude based on success stories from other companies in the same industry. The analysis unit can also refer to data from other companies and perform aptitude analysis based on success stories. For example, it can identify departments that have demonstrated high performance in other companies. The analysis unit can also integrate data from different industries and companies and perform aptitude analysis based on success stories. For example, it can evaluate aptitude based on success stories from different industries. This makes it possible to perform aptitude analysis based on data from different industries and companies and with reference to success stories from other companies.

[0044] The analysis department can perform a detailed analysis of the work content and required skill sets of each department and match them with individual skills. For example, the work content of each department can be analyzed in detail to identify the required skill sets. For example, the data analysis department requires advanced analytical skills. The analysis department can also analyze individual skill sets and match them with the required skill sets of each department. For example, it can suggest the data analysis department for individuals with high data analysis skills. The analysis department can also comprehensively evaluate the work content of each department and individual skill sets and suggest the most suitable department. For example, it can suggest the data analysis department for individuals with high data analysis skills but who are not good at presentations. This allows for a detailed analysis of the work content of each department and individual skills, enabling optimal matching.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The input unit inputs an individual's interests, strengths, and weak areas. For example, an individual can input, "I'm interested in marketing and good at data analysis, but I'm not good at presentations." The input unit can also automatically analyze an individual's past work history and project participation history to complement their interests, strengths, and weak areas. Furthermore, the input unit can analyze the content of an individual's social media and blog posts to estimate their interests, strengths, and weak areas. Step 2: The analysis unit analyzes the interests, strengths, and areas of weakness entered by the input unit. For example, the generative AI analyzes the input information and comprehensively evaluates the individual's interests, strengths, and areas of weakness. Step 3: The proposal unit proposes the most suitable department based on the information analyzed by the analysis unit. For example, if the person is interested in marketing and good at data analysis, the generation AI will suggest the marketing department or the data analysis department.

[0047] (Example 2) The assignment suggestion system according to the embodiment of the present invention is a system that suggests the most suitable department for an individual by taking into consideration the individual's interests, strengths, and areas of weakness. As a result, the assignment suggestion system can suggest the most suitable department for an individual based on their aptitude.

[0048] An assignment suggestion system according to an embodiment includes an input unit, an analysis unit, and a suggestion unit. The input unit inputs an individual's interests, strengths, and weaknesses. For example, an individual can input, "I'm interested in marketing and good at data analysis, but I'm not good at presentations." The input unit can also automatically analyze the individual's past work history and project participation history to complement the individual's interests, strengths, and weaknesses. For example, the input unit complements the individual's strengths and weaknesses based on the individual's roles and achievements in past projects. The input unit can also analyze the individual's social media and blog posts to infer the individual's interests, strengths, and weaknesses. For example, if the individual posts frequently about technology, it can infer that the individual is interested in that field. The analysis unit analyzes the interests, strengths, and weaknesses input by the input unit. For example, a generation AI analyzes the input information and comprehensively evaluates the individual's interests, strengths, and weaknesses. The suggestion unit proposes the most appropriate department based on the information analyzed by the analysis unit. For example, if the individual is interested in marketing and good at data analysis, the generation AI can propose the marketing department or the data analysis department. As a result, the assignment suggestion system according to the embodiment can suggest the most suitable department based on the aptitude of an individual.

[0049] The input unit can automatically analyze an individual's past work history and project participation history to complement their interests, strengths, and weak areas. For example, the input unit analyzes an individual's past work history to determine which tasks they performed well in. For example, it complements their strong areas based on their roles and achievements in past projects. The input unit also analyzes their project participation history to determine what projects they were interested in and participated in. For example, if they participated in many technical projects, it determines that they are interested in that field. The input unit also integrates their work history and project history to comprehensively evaluate an individual's strengths and weak areas. For example, if an individual achieved high results in a data analysis project but had few opportunities to give presentations, it complements that they are good at data analysis but not at presentations. This allows for a more accurate evaluation of an individual's aptitude based on their past work history and project participation history.

[0050] The input unit can analyze the content of an individual's social media and blog posts to estimate their interests, strengths, and areas of weakness. For example, the input unit analyzes the content of social media posts to extract frequently mentioned topics and keywords. For example, if there are many technology-related posts, it can be inferred that the individual is interested in that field. The input unit can also analyze the content of blog posts to identify what topics are written in detail. For example, if there are many detailed articles about data analysis, it can be inferred that the individual has strengths in that field. The input unit can also integrate the content of social media and blog posts to comprehensively evaluate the individual's interests, strengths, and areas of weakness. For example, if there are many technology-related posts and few posts about presentations, it can be inferred that the individual has strengths in technology and weaknesses in presentations. This allows for a more accurate evaluation of an individual's aptitude based on the content of social media and blog posts.

[0051] The input unit can use the emotion estimation function to analyze the emotional state at the time of input and identify fields in which positive emotions are strongly expressed. The input unit, for example, analyzes facial expressions at the time of input and identifies fields in which positive emotions are strongly expressed. For example, if the user smiles frequently when inputting information related to data analysis, it determines that the user is interested in that field. The input unit also analyzes the voice tone at the time of input and identifies fields in which positive emotions are strongly expressed. For example, if the user's voice tone is high when inputting information related to marketing, it determines that the user is interested in that field. The input unit also uses the emotion estimation function to analyze the emotional state at the time of input in real time and identify fields in which positive emotions are strongly expressed. For example, if the user's positive emotion score is high when inputting information related to data analysis, it determines that the user is interested in that field. This allows for a more accurate evaluation of an individual's aptitude based on the emotional state at the time of input.

[0052] The input unit also allows the user to input information about the user's hobbies and lifestyle, and can suggest departments that take work-life balance into consideration. The input unit, for example, allows the user to input information about the user's hobbies, and suggests departments based on that information. For example, for a person whose hobby is the outdoors, it would suggest a department with a lot of fieldwork. The input unit also allows the user to input information about lifestyle, and suggests departments that take work-life balance into consideration. For example, for a person who needs flexible working hours due to family circumstances, it would suggest a department that allows remote work. The input unit also comprehensively evaluates the information about hobbies and lifestyle, and suggests the most suitable department. For example, for a person who enjoys sports and is very health-conscious, it would suggest a department with many health-related projects. In this way, it is possible to suggest the most suitable department taking into consideration the user's hobbies and lifestyle.

[0053] The input unit allows feedback from team members and superiors to be input, enabling aptitude to be evaluated from a third-party perspective. The input unit, for example, collects feedback from team members and evaluates individual aptitude. For example, areas highly rated by colleagues are supplemented as strengths. The input unit also collects feedback from superiors and evaluates individual aptitude. For example, aptitude is determined based on skills and tasks highly rated by superiors. The input unit also comprehensively evaluates feedback from team members and superiors and suggests the most suitable department. For example, areas where multiple pieces of feedback match are supplemented as strengths. This allows aptitude to be evaluated from a third-party perspective.

[0054] The input unit can use the emotion estimation function to provide real-time feedback on the emotional state at the time of input, thereby providing input support that draws out positive emotions. The input unit, for example, analyzes the emotional state at the time of input in real time and provides feedback to draw out positive emotions. For example, it displays an encouraging message according to the input content. The input unit also uses the emotion estimation function to provide real-time feedback on the emotional state at the time of input, thereby providing input support that draws out positive emotions. For example, it provides an interface that strengthens positive emotions. The input unit also analyzes the emotional state at the time of input and provides advice to draw out positive emotions. For example, it displays appropriate encouragement or praise according to the input content. In this way, the emotional state at the time of input can be fed back in real time, thereby drawing out positive emotions.

[0055] The analysis unit can analyze past assignment data and subsequent work performance, and perform aptitude analysis based on success cases. The analysis unit, for example, analyzes past assignment data and work performance, and performs aptitude analysis based on success cases. For example, it identifies departments that have demonstrated high performance in the past. The analysis unit also analyzes post-assignment work performance data, and performs aptitude analysis based on success cases. For example, it identifies departments where a specific skill set was highly evaluated. The analysis unit also integrates past assignment data and work performance, and performs aptitude analysis based on success cases. For example, it evaluates the aptitude of individuals who have achieved high results in a specific job. This allows aptitude analysis based on success cases, based on past assignment data and work performance.

[0056] The analysis department can perform a detailed analysis of the work content and required skill sets of each department and match them with individual skills. For example, the analysis department can perform a detailed analysis of the work content of each department and identify the required skill sets. For example, the data analysis department requires advanced analytical skills. The analysis department can also analyze individual skill sets and match them with the required skill sets of each department. For example, it can suggest the data analysis department for individuals with high data analysis skills. The analysis department can also comprehensively evaluate the work content of each department and individual skill sets and suggest the most suitable department. For example, it can suggest the data analysis department for individuals with high data analysis skills but who are not good at presentations. This allows for a detailed analysis of the work content of each department and individual skills, enabling optimal matching.

[0057] The analysis unit can use the emotion estimation function to analyze an individual's emotional response to the proposed department and preferentially suggest departments that will receive a positive response. The analysis unit, for example, analyzes an individual's emotional response to the proposed department and preferentially suggests departments that will receive a positive response. For example, if there are many smiles toward a proposed department, the analysis unit preferentially suggests that department. The analysis unit also uses the emotion estimation function to analyze an individual's emotional response to the proposed department in real time and preferentially suggests departments that will receive a positive response. For example, if there is a high tone of voice toward a proposed department, the analysis unit preferentially suggests that department. The analysis unit also preferentially suggests departments that will receive a positive response based on the emotional response data toward the proposed department. For example, if the positive emotion score for a proposed department is high, the analysis unit preferentially suggests that department. This makes it possible to preferentially suggest departments that will receive a positive response based on the emotional response to the proposed department.

[0058] The analysis unit can refer to data from different industries and companies and perform aptitude analysis based on success stories from other companies. The analysis unit, for example, refers to data from different industries and performs aptitude analysis based on success stories from other companies. For example, it evaluates aptitude based on success stories from other companies in the same industry. The analysis unit also refers to data from other companies and performs aptitude analysis based on success stories. For example, it identifies departments that have demonstrated high performance in other companies. The analysis unit also integrates data from different industries and companies and performs aptitude analysis based on success stories. For example, it evaluates aptitude based on success stories from different industries. This makes it possible to perform aptitude analysis based on data from different industries and companies and with reference to success stories from other companies.

[0059] When proposing departments, the analysis department can take future career paths into consideration and suggest departments where long-term growth can be expected. For example, the analysis department can take future career paths into consideration and suggest departments where long-term growth can be expected. For example, it can prioritize suggesting departments where career advancement is expected. The analysis department can also input individuals' career goals and suggest departments where long-term growth can be expected based on those goals. For example, it can suggest departments that require leadership for an individual who wants to hone their management skills. The analysis department can also comprehensively evaluate future career paths and current skill sets and suggest departments where long-term growth can be expected. For example, it can suggest departments where data analysis skills can be utilized while also honing management skills in the future. This makes it possible to suggest departments where long-term growth can be expected, taking future career paths into consideration.

[0060] The analysis unit uses the emotion estimation function to monitor individuals' emotional reactions to the proposed departments in real time and continuously suggest optimal departments. The analysis unit, for example, monitors individuals' emotional reactions to the proposed departments in real time and continuously suggests optimal departments. For example, if there is strong positive emotion toward the proposed department, it preferentially suggests that department. The analysis unit also uses the emotion estimation function to monitor individuals' emotional reactions to the proposed departments in real time and continuously suggests optimal departments. For example, if there is strong negative emotion toward the proposed department, it suggests a different department. The analysis unit also continuously suggests optimal departments based on the emotional reaction data toward the proposed departments. For example, if there is a high positive emotion score toward the proposed department, it preferentially suggests that department. In this way, it is possible to monitor individuals' emotional reactions to the proposed departments in real time and continuously suggest optimal departments.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The analysis unit can analyze an individual's health condition and stress level, and suggest departments that take health management into consideration. For example, if the individual's health condition is not good, it will suggest a department with less stress. The analysis unit also suggests departments that make it easy to manage health based on the individual's health data. For example, if regular exercise is required, it will suggest a department with good fitness facilities. The analysis unit also analyzes the relationship between health condition and work performance, and suggests departments that take into consideration the impact of health management on work. For example, it will suggest a department where work performance will improve as health condition improves. This makes it possible to suggest the optimal department taking into consideration an individual's health condition.

[0063] The analysis department can analyze an individual's learning history and qualification acquisition status, and suggest departments where they can expect to improve their skills. For example, if they have a specific qualification, it will suggest a department where they can make use of that qualification. The analysis department can also suggest departments where they can expect to improve their skills based on an individual's learning history. For example, if they have completed a data science course, it will suggest the data analysis department. The analysis department can also analyze the relationship between learning history and work performance, and suggest departments that take into account the impact that skill improvement will have on work. For example, it will suggest departments where work performance will improve by acquiring new skills. This makes it possible to suggest the most suitable department, taking into account an individual's learning history and qualification acquisition status.

[0064] The analysis unit can analyze an individual's communication style and suggest a department where teamwork can proceed smoothly. For example, if an individual is good at face-to-face communication, it will suggest a department where there are many face-to-face meetings. The analysis unit can also determine whether remote work is suitable based on an individual's communication style and suggest an appropriate department. For example, if an individual is good at remote work, it will suggest a department where remote work is the main focus. The analysis unit can also analyze the relationship between communication style and work performance and suggest a department with an optimal communication environment. For example, for an individual who performs well in a department where teamwork is important, it will suggest a department where teamwork is emphasized. This makes it possible to suggest the optimal department taking into account an individual's communication style.

[0065] The analysis department allows individuals to input their career goals and can suggest departments where they can expect long-term growth based on those goals. For example, for an individual who wants to hone their management skills, it will suggest departments that require leadership. The analysis department also comprehensively evaluates future career paths and current skill sets and suggests departments where they can expect long-term growth. For example, it will suggest departments where they can utilize their data analysis skills while also honing their management skills in the future. The analysis department also analyzes the relationship between career goals and work performance and suggests departments where they can easily achieve their career goals. For example, for an individual with a specific career goal, it will suggest departments where they can easily achieve that goal. This makes it possible to suggest departments where they can expect long-term growth, taking future career paths into consideration.

[0066] The analysis unit can use the individual emotion estimation function to analyze an individual's emotional response to the proposed department and preferentially suggest departments that will receive a positive response. For example, if there are many smiles toward the proposed department, the analysis unit preferentially suggests that department. The analysis unit also uses the emotion estimation function to analyze an individual's emotional response to the proposed department in real time and preferentially suggest departments that will receive a positive response. For example, if there is a high tone of voice toward the proposed department, the analysis unit preferentially suggests that department. The analysis unit also preferentially suggests departments that will receive a positive response based on the emotional response data toward the proposed department. For example, if there is a high positive emotion score toward the proposed department, the analysis unit preferentially suggests that department. In this way, it is possible to preferentially suggest departments that will receive a positive response based on the emotional response to the proposed department.

[0067] The analysis unit can use the emotion estimation function to provide real-time feedback on the emotional state at the time of input, thereby providing input support that draws out positive emotions. For example, the analysis unit can analyze the emotional state at the time of input in real time and provide feedback to draw out positive emotions. For example, it can display an encouraging message according to the input content. The analysis unit can also use the emotion estimation function to provide real-time feedback on the emotional state at the time of input, thereby providing input support that draws out positive emotions. For example, it can provide an interface that strengthens positive emotions. The analysis unit can also analyze the emotional state at the time of input, and provide advice to draw out positive emotions. For example, it can display appropriate words of encouragement or praise according to the input content. In this way, the analysis unit can provide real-time feedback on the emotional state at the time of input, thereby drawing out positive emotions.

[0068] The analysis unit uses the emotion estimation function to monitor an individual's emotional response to the proposed department in real time and continuously suggest the most appropriate department. For example, if there is strong positive emotion toward the proposed department, it will preferentially suggest that department. The analysis unit also uses the emotion estimation function to monitor an individual's emotional response to the proposed department in real time and continuously suggest the most appropriate department. For example, if there is strong negative emotion toward the proposed department, it will suggest a different department. The analysis unit also continuously suggest the most appropriate department based on the emotional response data toward the proposed department. For example, if there is a high positive emotion score toward the proposed department, it will preferentially suggest that department. In this way, it is possible to monitor the emotional response to the proposed department in real time and continuously suggest the most appropriate department.

[0069] The analysis unit can use the emotion estimation function to analyze the emotional state at the time of input and identify fields in which positive emotions are strongly expressed. For example, the analysis unit can analyze facial expressions at the time of input and identify fields in which positive emotions are strongly expressed. For example, if the user smiles frequently when inputting information related to data analysis, it can be determined that the user is interested in that field. The analysis unit can also analyze the voice tone at the time of input and identify fields in which positive emotions are strongly expressed. For example, if the user's voice tone is high when inputting information related to marketing, it can be determined that the user is interested in that field. The analysis unit can also use the emotion estimation function to analyze the emotional state at the time of input in real time and identify fields in which positive emotions are strongly expressed. For example, if the user's positive emotion score is high when inputting information related to data analysis, it can be determined that the user is interested in that field. This allows for a more accurate evaluation of an individual's aptitude based on the emotional state at the time of input.

[0070] The analysis unit can refer to data from different industries and companies and perform aptitude analysis based on success stories from other companies. For example, it can refer to data from different industries and perform aptitude analysis based on success stories from other companies. For example, it can evaluate aptitude based on success stories from other companies in the same industry. The analysis unit can also refer to data from other companies and perform aptitude analysis based on success stories. For example, it can identify departments that have demonstrated high performance in other companies. The analysis unit can also integrate data from different industries and companies and perform aptitude analysis based on success stories. For example, it can evaluate aptitude based on success stories from different industries. This makes it possible to perform aptitude analysis based on data from different industries and companies and with reference to success stories from other companies.

[0071] The analysis department can perform a detailed analysis of the work content and required skill sets of each department and match them with individual skills. For example, the work content of each department can be analyzed in detail to identify the required skill sets. For example, the data analysis department requires advanced analytical skills. The analysis department can also analyze individual skill sets and match them with the required skill sets of each department. For example, it can suggest the data analysis department for individuals with high data analysis skills. The analysis department can also comprehensively evaluate the work content of each department and individual skill sets and suggest the most suitable department. For example, it can suggest the data analysis department for individuals with high data analysis skills but who are not good at presentations. This allows for a detailed analysis of the work content of each department and individual skills, enabling optimal matching.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The input unit inputs an individual's interests, strengths, and weak areas. For example, an individual can input, "I'm interested in marketing and good at data analysis, but I'm not good at presentations." The input unit can also automatically analyze an individual's past work history and project participation history to complement their interests, strengths, and weak areas. Furthermore, the input unit can analyze the content of an individual's social media and blog posts to estimate their interests, strengths, and weak areas. Step 2: The analysis unit analyzes the interests, strengths, and areas of weakness entered by the input unit. For example, the generative AI analyzes the input information and comprehensively evaluates the individual's interests, strengths, and areas of weakness. Step 3: The proposal unit proposes the most suitable department based on the information analyzed by the analysis unit. For example, if the person is interested in marketing and good at data analysis, the generation AI will suggest the marketing department or the data analysis department.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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]

[0141] 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 input section where you can enter your personal interests, strengths, and weaknesses; an analysis unit that analyzes the interests, strengths, and weak areas input by the input unit; a proposal unit that proposes an optimal department based on the information analyzed by the analysis unit. A system characterized by:

2. The input unit Automatically analyze the individual's past work history and project participation history to complement the individual's interests, strengths, and weak areas.

2. The system of claim 1.

3. The input unit Analyzing the content posted on social media and blogs by the individual to estimate the individual's interests, strengths, and weaknesses 2. The system of claim 1.

4. The input unit Analyzes the emotional state of the user at the time of input and identifies areas where positive emotions are most prevalent 2. The system of claim 1.

5. The input unit The system also asks employees to enter information about their personal hobbies and lifestyles, and then proposes departments that take into account work-life balance.

2. The system of claim 1.

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