System

Abstract

An object of a system according to an embodiment is to uniformly manage skill sheets in different formats.SOLUTION: A system includes a skill sheet reading part, an analysis part, and a registration part. The skill sheet reading unit reads a skill sheet. The analysis section analyzes the contents of the skill sheet read by the skill sheet reading section. The registration part registers the information of the skill sheet analyzed by the analysis part in a database in a unified format.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 techniques have had the problem that it is difficult to uniformly manage skill sheets in different formats for each company.

[0005] The system according to the embodiment aims to manage skill sheets in different formats in a unified manner. [Means for solving the problem]

[0006] The system according to the embodiment includes a skill sheet reading unit, an analysis unit, and a registration unit. The skill sheet reading unit reads a skill sheet. The analysis unit analyzes the content of the skill sheet read by the skill sheet reading unit. The registration unit registers the information of the skill sheet analyzed by the analysis unit in a database in a unified format. [Effects of the Invention]

[0007] The system according to the embodiment can manage skill sheets in different formats in a unified manner. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 skill sheet management system according to an embodiment of the present invention is a system that uses a generation AI to read skill sheets, each with a different format for each company, and creates a database in a unified format. This allows the skill sheet management system to efficiently manage skill sheets in different formats and facilitate search and analysis.

[0029] A skill sheet management system according to an embodiment includes a skill sheet reading unit, an analysis unit, and a registration unit. The skill sheet reading unit reads a skill sheet. For example, the skill sheet reading unit can read skill sheets in different formats provided by each company. The skill sheet reading unit can also directly read digital skill sheets. The skill sheet reading unit can also digitize paper-based skill sheets using a scanner. For example, the skill sheet reading unit can use a scanner to save the paper-based skill sheet as image data and convert it into text data using OCR technology. The analysis unit analyzes the content of the skill sheet read by the skill sheet reading unit. For example, the analysis unit can analyze the content of the skill sheet using a generation AI to extract information such as the engineer's name, years of experience, skill set, and project history. The analysis unit can also convert the content of the skill sheet into a unified format using the generation AI. For example, the generation AI can analyze the content of the skill sheet using a text generation AI (e.g., LLM) and convert it into a unified format. The registration unit registers the skill sheet information analyzed by the analysis unit in a database in a unified format. For example, the registration unit appropriately stores information such as an engineer's name, years of experience, skill set, and project history in each field of the database. The registration unit can also automatically generate a database schema using a generation AI. For example, the generation AI generates a database schema based on the content of a skill sheet and stores appropriate data in each field. This allows the skill sheet management system according to the embodiment to efficiently manage skill sheets in different formats and easily perform searches and analyses. For example, the skill sheet management system can search for engineers with a specific skill set or engineers with specific project experience. The skill sheet management system can also generate reports based on the information registered in the database. For example, it can output reports such as the number of engineers with a specific skill set or the percentage of engineers with specific project experience.

[0030] The skill sheet reader can support voice input or handwritten input. For example, to support voice input, the generation AI uses voice recognition technology to convert the engineer's voice into text and analyze the content of the skill sheet. To support handwritten input, the generation AI uses handwriting recognition technology to convert the engineer's handwritten characters into text and analyze the content of the skill sheet. For example, the generation AI can convert a skill sheet handwritten using a tablet into text in real time. Furthermore, the skill sheet reader can also photograph a handwritten skill sheet using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. This allows engineers to provide information via voice or handwriting.

[0031] The analysis unit can analyze skill sheets from different industries to identify commonalities and differences between skills. For example, the generation AI can analyze skill sheets from different industries, compare the skill sets of each industry, and extract common and unique skills to identify common and unique skills. The analysis unit can also analyze skill sheets from different industries, cluster the skill sets of each industry, and visualize the common and unique skills to identify common and unique skills. For example, the generation AI can group skill sets using a clustering algorithm to identify commonalities and differences. Furthermore, the analysis unit can analyze skill sheets from different industries, and analyze the skill sets of each industry based on a topic model to identify commonalities and differences between skills. For example, the generation AI can use LDA (Latent Dirichlet Allocation) to break down skill sets into topics and identify commonalities and differences. This can identify commonalities and differences between skills across different industries.

[0032] The registration unit can analyze correlations among the engineer's skill sets and automatically complement related skills. For example, when registering the engineer in the database, the registration unit analyzes correlations among the engineer's skill sets and automatically complements related skills by having the generation AI analyze the correlations among the skill sets and automatically complement related skills. The registration unit can also analyze correlations among the engineer's skill sets and automatically complement related skills by having the generation AI cluster the correlations among the skill sets and automatically complement related skills. For example, the generation AI uses a clustering algorithm to group skill sets and automatically complement related skills. The registration unit can also analyze correlations among the engineer's skill sets and automatically complement related skills by having the generation AI analyze the correlations among the skill sets based on a topic model and automatically complement related skills. For example, the generation AI uses LDA (Latent Dirichlet Allocation) to break down skill sets into topics and automatically complement related skills. This makes it possible to analyze correlations among the engineer's skill sets and automatically complement related skills.

[0033] The analysis unit can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. For example, the analysis unit analyzes the completion rate and goal achievement rate of each project so that the generation AI can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. The analysis unit can also analyze the suspension rate and problem occurrence rate of each project so that the generation AI can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. For example, the generation AI calculates the success rate based on the project completion rate and goal achievement rate, and calculates the failure rate based on the project suspension rate and problem occurrence rate. Furthermore, the analysis unit can analyze the deliverables and feedback of each project so that the generation AI can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. For example, the generation AI evaluates the success and failure rates based on the project deliverables and feedback and complements the content of the skill sheet. In this way, the success and failure rates of the engineer's past projects can be evaluated and the content of the skill sheet can be complemented.

[0034] The registration unit can evaluate the success rate and failure rate of the engineer's past projects and set priorities within the database. For example, when registering the engineer in the database, the registration unit evaluates the success rate and failure rate of the engineer's past projects, and the generation AI analyzes the success rate and failure rate of the projects to set priorities within the database. The registration unit can also evaluate the success rate and failure rate of the engineer's past projects and the generation AI analyzes the project deliverables and feedback to set priorities. For example, the generation AI can set priorities based on the success rate and failure rate of the projects and adjust the search results and display order within the database. Furthermore, the registration unit can evaluate the success rate and failure rate of the engineer's past projects and the generation AI can evaluate the importance and urgency of the projects and set priorities. For example, the generation AI can set priorities based on the importance and urgency of the projects and adjust the display order within the database. In this way, the success rate and failure rate of the engineer's past projects can be evaluated and the generation AI can set priorities within the database.

[0035] The analysis unit can complement the skill set by referring to the engineer's social media activity and code repository. For example, when the generation AI analyzes the contents of the skill sheet, the analysis unit can refer to the engineer's social media activity and public code repository and analyze the content of social media posts and the number of followers to complement the skill set. The analysis unit can also analyze the code repository's commit history and project contribution level to complement the skill set by referring to the engineer's social media activity and public code repository. For example, the generation AI evaluates the engineer's skill set based on the content of social media posts and the number of followers, and complements the skill set based on the code repository's commit history and project contribution level. Furthermore, the analysis unit can analyze social media interactions and code repository reviews to complement the skill set by referring to the engineer's social media activity and public code repository. For example, the generation AI can evaluate and complement the engineer's skill set based on social media interactions and code repository reviews. This allows the generation AI to complement the skill set by referring to the engineer's social media activity and public code repository.

[0036] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0037] The skill sheet management system can further include a career prediction unit that predicts an engineer's career path. The career prediction unit analyzes the engineer's past skill sheets and project history to predict his or her future career path. For example, the career prediction unit analyzes the evolution of the engineer's skill set and suggests the next skills to be acquired and suitable projects. The career prediction unit can also predict the probability of success in future projects based on the engineer's success and failure rates in past projects. Furthermore, the career prediction unit can suggest the optimal career path based on the engineer's career goals and desired job type. This makes it easier for engineers to plan their own careers.

[0038] The skill sheet reader can also analyze engineers' handwritten diagrams and sketches. For example, the skill sheet reader can analyze handwritten flowcharts and diagrams and convert them into digital format. The skill sheet reader can also analyze handwritten blueprints and prototypes and recreate them as 3D models. The skill sheet reader can also analyze handwritten notes and ideas and save them as text data. This allows for the digitization and efficient management of information provided by engineers by hand.

[0039] The analysis department can analyze skill sheets from different industries and evaluate the transferability of skills between industries. For example, the analysis department can compare skill sheets from the IT industry and the manufacturing industry to identify common and transferable skills. The analysis department can also analyze skill sheets from the medical industry and the financial industry to evaluate the transferability of skills. Furthermore, the analysis department can analyze skill sheets from the education industry and the entertainment industry to evaluate the transferability of skills. This can evaluate the transferability of skills between different industries and broaden the career paths of engineers.

[0040] The registration unit can analyze the correlations between an engineer's skill sets and suggest the order in which the skills should be acquired. For example, the registration unit can analyze an engineer's current skill set and suggest the next skill that should be acquired. The registration unit can also suggest the optimal order in which skills should be acquired based on the engineer's career goals. Furthermore, the registration unit can analyze the engineer's past project history and suggest that the skills required for the project should be acquired first. This allows engineers to acquire skills efficiently.

[0041] The analysis department can evaluate the success and failure rates of engineers' past projects to identify their strengths and weaknesses. For example, the analysis department can analyze commonalities between the engineers' successful projects to identify their strengths. The analysis department can also analyze commonalities between the engineers' unsuccessful projects to identify their weaknesses. Furthermore, the analysis department can suggest skill sets to utilize the engineers' strengths and provide learning resources to overcome their weaknesses. This can identify the engineers' strengths and weaknesses and support their career advancement.

[0042] The analysis unit can analyze an engineer's network by referencing the engineer's social media activity and code repository. For example, the analysis unit can analyze the engineer's social media followers and the accounts they follow to evaluate the breadth of the engineer's network. The analysis unit can also analyze the collaborators in the engineer's code repository to identify the engineer's collaborative relationships. Furthermore, the analysis unit can suggest possibilities for collaboration with other engineers based on the engineer's network. This allows the analysis of an engineer's network to support career advancement.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The skill sheet reader reads the skill sheet. For example, the skill sheet reader can read skill sheets in different formats provided by each company. It can also read digital skill sheets directly, or it can use a scanner to digitize paper-based skill sheets. Specifically, a scanner is used to save the paper-based skill sheet as image data, and OCR technology is used to convert it into text data. Step 2: The analysis unit analyzes the content of the skill sheet read by the skill sheet reading unit. For example, the generation AI can be used to analyze the content of the skill sheet and extract information such as the engineer's name, years of experience, skill set, and project history. The generation AI can also be used to convert the content of the skill sheet into a unified format. Specifically, a text generation AI (e.g., LLM) can be used to analyze the content of the skill sheet and convert it into a unified format. Step 3: The registration unit registers the skill sheet information analyzed by the analysis unit in a database in a unified format. For example, information such as the engineer's name, years of experience, skill set, and project history is appropriately stored in each field of the database. It is also possible to automatically generate a database schema using generative AI. Specifically, the database schema is generated based on the content of the skill sheet, and appropriate data is stored in each field.

[0045] (Example 2) The skill sheet management system according to an embodiment of the present invention is a system that uses a generation AI to read skill sheets, each with a different format for each company, and creates a database in a unified format. This allows the skill sheet management system to efficiently manage skill sheets in different formats and facilitate search and analysis.

[0046] A skill sheet management system according to an embodiment includes a skill sheet reading unit, an analysis unit, and a registration unit. The skill sheet reading unit reads a skill sheet. For example, the skill sheet reading unit can read skill sheets in different formats provided by each company. The skill sheet reading unit can also directly read digital skill sheets. The skill sheet reading unit can also digitize paper-based skill sheets using a scanner. For example, the skill sheet reading unit can use a scanner to save the paper-based skill sheet as image data and convert it into text data using OCR technology. The analysis unit analyzes the content of the skill sheet read by the skill sheet reading unit. For example, the analysis unit can analyze the content of the skill sheet using a generation AI to extract information such as the engineer's name, years of experience, skill set, and project history. The analysis unit can also convert the content of the skill sheet into a unified format using the generation AI. For example, the generation AI can analyze the content of the skill sheet using a text generation AI (e.g., LLM) and convert it into a unified format. The registration unit registers the skill sheet information analyzed by the analysis unit in a database in a unified format. For example, the registration unit appropriately stores information such as an engineer's name, years of experience, skill set, and project history in each field of the database. The registration unit can also automatically generate a database schema using a generation AI. For example, the generation AI generates a database schema based on the content of a skill sheet and stores appropriate data in each field. This allows the skill sheet management system according to the embodiment to efficiently manage skill sheets in different formats and easily perform searches and analyses. For example, the skill sheet management system can search for engineers with a specific skill set or engineers with specific project experience. The skill sheet management system can also generate reports based on the information registered in the database. For example, it can output reports such as the number of engineers with a specific skill set or the percentage of engineers with specific project experience.

[0047] The analysis unit can estimate the engineer's emotions and motivation and complement the content of the skill sheet based on that information. For example, when the generation AI analyzes the content of a skill sheet, the analysis unit evaluates the success and failure rates of past projects and quantitatively evaluates the engineer's track record to estimate the engineer's emotions and motivation. The analysis unit can also refer to the engineer's social media activity and public code repository to estimate the engineer's emotions and motivation. For example, the generation AI can analyze the engineer's social media activity and code repository information to estimate the engineer's emotions and motivation. Furthermore, to estimate the engineer's emotions and motivation, the analysis unit can collect the engineer's biometric data (heart rate and electrodermal activity) and analyze their emotions using an emotion estimation algorithm. For example, the generation AI can calculate an emotion score based on heart rate fluctuations to estimate the engineer's emotions and motivation. This makes it possible to complement the skill sheet by taking the engineer's emotions and motivation into account.

[0048] The skill sheet reader can support voice input or handwritten input. For example, to support voice input, the generation AI uses voice recognition technology to convert the engineer's voice into text and analyze the content of the skill sheet. To support handwritten input, the generation AI uses handwriting recognition technology to convert the engineer's handwritten characters into text and analyze the content of the skill sheet. For example, the generation AI can convert a skill sheet handwritten using a tablet into text in real time. Furthermore, the skill sheet reader can also photograph a handwritten skill sheet using a smartphone camera and convert the image data into text data using a dedicated app. For example, the app can automatically correct the image and perform character recognition. This allows engineers to provide information via voice or handwriting.

[0049] The analysis unit can analyze skill sheets from different industries to identify commonalities and differences between skills. For example, the generation AI can analyze skill sheets from different industries, compare the skill sets of each industry, and extract common and unique skills to identify common and unique skills. The analysis unit can also analyze skill sheets from different industries, cluster the skill sets of each industry, and visualize the common and unique skills to identify common and unique skills. For example, the generation AI can group skill sets using a clustering algorithm to identify commonalities and differences. Furthermore, the analysis unit can analyze skill sheets from different industries, and analyze the skill sets of each industry based on a topic model to identify commonalities and differences between skills. For example, the generation AI can use LDA (Latent Dirichlet Allocation) to break down skill sets into topics and identify commonalities and differences. This can identify commonalities and differences between skills across different industries.

[0050] The registration unit can design a database structure that takes into account the emotions and motivation of engineers and elicits positive emotions. For example, when registering data in the database, the registration unit designs database fields based on the engineer's emotion score in order to design a database structure that takes into account the engineer's emotions and motivation and elicits positive emotions. The registration unit can also have the generation AI analyze the engineer's emotion score and automatically generate a database schema in order to design a database structure that takes into account the engineer's emotions and motivation and elicits positive emotions. For example, the generation AI designs database fields based on the engineer's emotion score and generates a database structure that elicits positive emotions. The registration unit can also design a database interface based on the engineer's emotion score in order to design a database structure that takes into account the engineer's emotions and motivation and elicits positive emotions. For example, the generation AI designs a database interface based on the engineer's emotion score and generates a user interface that elicits positive emotions. This makes it possible to design a database structure that takes into account the engineer's emotions and motivation.

[0051] The registration unit can analyze correlations among the engineer's skill sets and automatically complement related skills. For example, when registering the engineer in the database, the registration unit analyzes correlations among the engineer's skill sets and automatically complements related skills by having the generation AI analyze the correlations among the skill sets and automatically complement related skills. The registration unit can also analyze correlations among the engineer's skill sets and automatically complement related skills by having the generation AI cluster the correlations among the skill sets and automatically complement related skills. For example, the generation AI uses a clustering algorithm to group skill sets and automatically complement related skills. The registration unit can also analyze correlations among the engineer's skill sets and automatically complement related skills by having the generation AI analyze the correlations among the skill sets based on a topic model and automatically complement related skills. For example, the generation AI uses LDA (Latent Dirichlet Allocation) to break down skill sets into topics and automatically complement related skills. This makes it possible to analyze correlations among the engineer's skill sets and automatically complement related skills.

[0052] The analysis unit can complement the skill set by referring to the engineer's social media activity and code repository. For example, when the generation AI analyzes the contents of a skill sheet, the analysis unit estimates the engineer's emotions and motivation. Based on this, the analysis unit evaluates the success and failure rates of past projects and quantitatively evaluates the engineer's track record to complement the contents of the skill sheet. The analysis unit can also have the generation AI refer to the engineer's social media activity and public code repository to estimate the engineer's emotions and motivation. For example, the generation AI analyzes the engineer's social media activity and code repository information to estimate the engineer's emotions and motivation. Furthermore, to estimate the engineer's emotions and motivation, the analysis unit can have the generation AI collect the engineer's biometric data (heart rate and electrodermal activity) and analyze the emotions using an emotion estimation algorithm. For example, the generation AI calculates an emotion score based on heart rate fluctuations to estimate the engineer's emotions and motivation. This makes it possible to complement the skill sheet by taking the engineer's emotions and motivation into account.

[0053] The analysis unit can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. For example, the analysis unit analyzes the completion rate and goal achievement rate of each project so that the generation AI can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. The analysis unit can also analyze the suspension rate and problem occurrence rate of each project so that the generation AI can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. For example, the generation AI calculates the success rate based on the project completion rate and goal achievement rate, and calculates the failure rate based on the project suspension rate and problem occurrence rate. Furthermore, the analysis unit can analyze the deliverables and feedback of each project so that the generation AI can evaluate the success and failure rates of the engineer's past projects and complement the content of the skill sheet. For example, the generation AI evaluates the success and failure rates based on the project deliverables and feedback and complements the content of the skill sheet. In this way, the success and failure rates of the engineer's past projects can be evaluated and the content of the skill sheet can be complemented.

[0054] The registration unit can evaluate the success rate and failure rate of the engineer's past projects and set priorities within the database. For example, when registering the engineer in the database, the registration unit evaluates the success rate and failure rate of the engineer's past projects, and the generation AI analyzes the success rate and failure rate of the projects to set priorities within the database. The registration unit can also evaluate the success rate and failure rate of the engineer's past projects and the generation AI analyzes the project deliverables and feedback to set priorities. For example, the generation AI can set priorities based on the success rate and failure rate of the projects and adjust the search results and display order within the database. Furthermore, the registration unit can evaluate the success rate and failure rate of the engineer's past projects and the generation AI can evaluate the importance and urgency of the projects and set priorities. For example, the generation AI can set priorities based on the importance and urgency of the projects and adjust the display order within the database. In this way, the success rate and failure rate of the engineer's past projects can be evaluated and the generation AI can set priorities within the database.

[0055] The analysis unit can complement the skill set by referring to the engineer's social media activity and code repository. For example, when the generation AI analyzes the contents of the skill sheet, the analysis unit can refer to the engineer's social media activity and public code repository and analyze the content of social media posts and the number of followers to complement the skill set. The analysis unit can also analyze the code repository's commit history and project contribution level to complement the skill set by referring to the engineer's social media activity and public code repository. For example, the generation AI evaluates the engineer's skill set based on the content of social media posts and the number of followers, and complements the skill set based on the code repository's commit history and project contribution level. Furthermore, the analysis unit can analyze social media interactions and code repository reviews to complement the skill set by referring to the engineer's social media activity and public code repository. For example, the generation AI can evaluate and complement the engineer's skill set based on social media interactions and code repository reviews. This allows the generation AI to complement the skill set by referring to the engineer's social media activity and public code repository.

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

[0057] The skill sheet management system can further include a career prediction unit that predicts an engineer's career path. The career prediction unit analyzes the engineer's past skill sheets and project history to predict his or her future career path. For example, the career prediction unit analyzes the evolution of the engineer's skill set and suggests the next skills to be acquired and suitable projects. The career prediction unit can also predict the probability of success in future projects based on the engineer's success and failure rates in past projects. Furthermore, the career prediction unit can suggest the optimal career path based on the engineer's career goals and desired job type. This makes it easier for engineers to plan their own careers.

[0058] The analysis unit can estimate the engineer's emotions and provide learning resources appropriate for the engineer based on the estimated emotions. For example, if the engineer is feeling stressed, the analysis unit can provide learning resources that will help the engineer relax. If the engineer wants to increase their motivation, the analysis unit can also provide learning resources that will inspire them. Furthermore, if the engineer wants to acquire a specific skill, the analysis unit can also provide learning resources related to that skill. This makes it possible to provide learning resources that correspond to the engineer's emotions and motivation.

[0059] The skill sheet reader can also analyze engineers' handwritten diagrams and sketches. For example, the skill sheet reader can analyze handwritten flowcharts and diagrams and convert them into digital format. The skill sheet reader can also analyze handwritten blueprints and prototypes and recreate them as 3D models. The skill sheet reader can also analyze handwritten notes and ideas and save them as text data. This allows for the digitization and efficient management of information provided by engineers by hand.

[0060] The analysis department can analyze skill sheets from different industries and evaluate the transferability of skills between industries. For example, the analysis department can compare skill sheets from the IT industry and the manufacturing industry to identify common and transferable skills. The analysis department can also analyze skill sheets from the medical industry and the financial industry to evaluate the transferability of skills. Furthermore, the analysis department can analyze skill sheets from the education industry and the entertainment industry to evaluate the transferability of skills. This can evaluate the transferability of skills between different industries and broaden the career paths of engineers.

[0061] The registration unit can estimate the engineer's emotions and adjust the display method of information in the database based on the estimated emotions. For example, if the engineer is feeling stressed, the information can be displayed simply to reduce the burden. Also, if the engineer wants to increase their motivation, information that gives them a sense of accomplishment can be emphasized and displayed. Furthermore, if the engineer is interested in a particular skill, information related to that skill can be displayed preferentially. This makes it possible to display information according to the engineer's emotions and motivation.

[0062] The registration unit can analyze the correlations between an engineer's skill sets and suggest the order in which the skills should be acquired. For example, the registration unit can analyze an engineer's current skill set and suggest the next skill that should be acquired. The registration unit can also suggest the optimal order in which skills should be acquired based on the engineer's career goals. Furthermore, the registration unit can analyze the engineer's past project history and suggest that the skills required for the project should be acquired first. This allows engineers to acquire skills efficiently.

[0063] The analysis unit can estimate the engineer's emotions and suggest projects that are suitable for the engineer based on the estimated emotions. For example, if the engineer is feeling stressed, the analysis unit can suggest a less stressful project. Also, if the engineer wants to increase their motivation, the analysis unit can suggest a challenging project. Furthermore, if the engineer wants to improve a specific skill, the analysis unit can suggest a project that will allow them to utilize that skill. This makes it possible to suggest projects that correspond to the engineer's emotions and motivation.

[0064] The analysis department can evaluate the success and failure rates of engineers' past projects to identify their strengths and weaknesses. For example, the analysis department can analyze commonalities between the engineers' successful projects to identify their strengths. The analysis department can also analyze commonalities between the engineers' unsuccessful projects to identify their weaknesses. Furthermore, the analysis department can suggest skill sets to utilize the engineers' strengths and provide learning resources to overcome their weaknesses. This can identify the engineers' strengths and weaknesses and support their career advancement.

[0065] The registration unit can estimate the engineer's emotions and adjust the update frequency of the information in the database based on the estimated emotions. For example, if the engineer is feeling stressed, the frequency of information updates can be reduced to reduce the burden. Also, if the engineer wants to increase their motivation, the frequency of information updates can be increased to provide the latest information. Furthermore, if the engineer is interested in a particular skill, the information related to that skill can be updated preferentially. This makes it possible to update information according to the engineer's emotions and motivation.

[0066] The analysis unit can analyze an engineer's network by referencing the engineer's social media activity and code repository. For example, the analysis unit can analyze the engineer's social media followers and the accounts they follow to evaluate the breadth of the engineer's network. The analysis unit can also analyze the collaborators in the engineer's code repository to identify the engineer's collaborative relationships. Furthermore, the analysis unit can suggest possibilities for collaboration with other engineers based on the engineer's network. This allows the analysis of an engineer's network to support career advancement.

[0067] The processing flow of the second embodiment will be briefly explained below.

[0068] Step 1: The skill sheet reader reads the skill sheet. For example, the skill sheet reader can read skill sheets in different formats provided by each company. It can also read digital skill sheets directly, or it can use a scanner to digitize paper-based skill sheets. Specifically, a scanner is used to save the paper-based skill sheet as image data, and OCR technology is used to convert it into text data. Step 2: The analysis unit analyzes the content of the skill sheet read by the skill sheet reading unit. For example, the generation AI can be used to analyze the content of the skill sheet and extract information such as the engineer's name, years of experience, skill set, and project history. The generation AI can also be used to convert the content of the skill sheet into a unified format. Specifically, a text generation AI (e.g., LLM) can be used to analyze the content of the skill sheet and convert it into a unified format. Step 3: The registration unit registers the skill sheet information analyzed by the analysis unit in a database in a unified format. For example, information such as the engineer's name, years of experience, skill set, and project history is appropriately stored in each field of the database. It is also possible to automatically generate a database schema using generative AI. Specifically, the database schema is generated based on the content of the skill sheet, and appropriate data is stored in each field.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0073] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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).

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0082] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0088] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0095] 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.

[0096] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0103] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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."

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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, in order to avoid confusion and to 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.

[0135] 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]

[0136] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a skill sheet reading unit that reads a skill sheet; an analysis unit that analyzes the content of the skill sheet read by the skill sheet reading unit; a registration unit that registers the information on the skill sheet analyzed by the analysis unit in a database in a unified format. A system characterized by:

2. The skill sheet reading unit Supports voice or handwriting input 2. The system of claim 1.

3. The analysis unit Analyze the skill sheets from different industries to identify commonalities and differences in skills 2. The system of claim 1.

4. The registration unit Analyzes correlations between engineers' skill sets and automatically complements related skills 2. The system of claim 1.

5. The analysis unit Estimate the engineer's emotions and motivations and complement the content of the skill sheet accordingly 2. The system of claim 1.

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