System

Abstract

An object of a system according to an embodiment is to automatically set a task corresponding to a specific skill required by a company and efficiently measure a AI skill.SOLUTION: A system according to an embodiment includes a task setting unit, a data generation unit, and a skill measurement unit. The task setting unit automatically sets a task according to a specific skill required by a company. The data generation unit automatically generates data corresponding to the task set by the task setting unit. The skill measuring unit measures the AI skill of the examinee using the task set by the task setting unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of setting tasks based on the specific skills required by companies, generating the appropriate data, and measuring AI skills was done manually, resulting in low efficiency.

[0005] The system of the embodiment aims to automatically set tasks according to the specific skills required by companies and efficiently measure AI skills. [Means for solving the problem]

[0006] The system according to the embodiment includes a task setting unit, a data generation unit, and a skill measurement unit. The task setting unit automatically sets tasks according to specific skills required by a company. The data generation unit automatically generates data according to the tasks set by the task setting unit. The skill measurement unit measures the AI ​​skills of test takers using the tasks set by the task setting unit and the data generated by the data generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can automatically set tasks according to the specific skills required by the company and efficiently measure AI skills. [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) An evaluation system according to an embodiment of the present invention uses generative AI to efficiently evaluate human resources with specific AI skills that companies are looking for. In this evaluation system, generative AI automatically sets tasks and generates data, providing a platform for measuring AI skills. This allows the evaluation system to efficiently evaluate human resources with the specific AI skills that companies are looking for.

[0029] An evaluation system according to an embodiment includes a task setting unit, a data generation unit, and a skill measurement unit. The task setting unit automatically sets tasks according to specific skills desired by a company. For example, the generation AI generates a task such as "Design an algorithm that returns optimal search results from a specific dataset" as a task for evaluating the skills of a search algorithm based on prompts including the skills desired by the company and task requirements. The generation AI can also generate a task such as "Design an algorithm that recommends optimal products based on user behavior history" as a task for evaluating the skills of a recommendation system. The generation AI can also generate a task such as "Design an algorithm that analyzes text data and extracts specific information" as a task for evaluating natural language processing skills. The data generation unit automatically generates data according to the set task. For example, the generation AI generates a dataset including user behavior history and product information as data for evaluating the skills of a recommendation system based on prompts including the type and format of data required for the task. The generation AI can also generate large amounts of text data as data for evaluating the skills of a search algorithm. The generation AI can also generate text data on various topics as data for evaluating natural language processing skills. The skill measurement unit measures the AI ​​skills of the test taker using the set tasks and the generated data. For example, the generation AI can evaluate how accurately the test taker can perform text analysis using the generated text data to evaluate natural language processing skills. The generation AI can also evaluate how effectively the test taker can recommend products using the generated dataset to evaluate recommendation system skills. The generation AI can also evaluate how accurately the test taker can return search results using the generated dataset to evaluate search algorithm skills. This allows the evaluation system according to the embodiment to efficiently evaluate personnel with specific AI skills desired by companies.For example, companies can automatically generate tasks and data to evaluate talent specializing in search algorithms and accurately measure their skills. Companies can also generate tasks and data to evaluate talent specializing in recommendation systems and natural language processing and measure their skills. This allows companies to efficiently find the right talent.

[0030] The task setting unit can refer to past performance data of test takers and generate tasks optimized for each test taker. For example, the task setting unit uses a generation AI to analyze past performance data of test takers and identify each test taker's strengths and weaknesses. For example, the task setting unit generates tasks that focus on specific skill sets based on past test results and feedback. Furthermore, the task setting unit uses the generation AI to generate tasks optimized for each test taker based on the test taker's past performance data. For example, the task setting unit can set tasks related to areas in which the test taker was weak in the past and focus on evaluating those skills. Furthermore, the task setting unit uses the generation AI to generate tasks to promote the test taker's growth based on the test taker's past performance data. For example, the task setting unit can set tasks related to areas in which the test taker was strong in the past and further improve those skills. By generating tasks optimized for each test taker, the test taker's skills can be evaluated more accurately.

[0031] The task setting unit can refer to the latest industry reports and news to reflect challenges and trends specific to the company's industry. For example, the task setting unit has the generation AI collect the latest industry reports and news and generate tasks based on them. For example, it sets tasks that reflect current market trends and technological trends. The task setting unit also refers to the latest industry reports and news to reflect challenges specific to the industry. For example, it can generate tasks that reflect technical challenges and market needs in a specific industry. The task setting unit also refers to the latest industry reports and news to reflect industry trends. For example, it can generate tasks that reflect current technological trends and market changes. In this way, by generating tasks that reflect challenges and trends specific to the industry, the skills required by companies can be more accurately evaluated.

[0032] The data generation unit can incorporate data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit uses a generation AI to incorporate stock price data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit generates investment simulation data based on stock price fluctuations. The data generation unit can also incorporate weather data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit can generate agricultural simulation data based on weather data. The data generation unit can also incorporate traffic data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit can generate urban planning simulation data based on traffic data. This allows for the generation of a dataset that reflects real-world fluctuations, enabling more realistic evaluations.

[0033] The data generation unit can increase the diversity of the data by including data from different regions and cultural spheres. For example, the data generation unit has the generation AI collect data from different regions and generate a dataset that includes the data to increase the diversity of the data. For example, the data generation unit generates marketing data based on consumer behavior data from different countries. The data generation unit also has the generation AI collect data from different cultural spheres and generate a dataset that includes the data to increase the diversity of the data. For example, the data generation unit can generate natural language processing data based on language data from different cultural spheres. The data generation unit also has the generation AI collect data from different regions and generate a dataset that includes the data to increase the diversity of the data. For example, the data generation unit can generate weather forecast data based on weather data from different regions. This increases the diversity of the data, allowing for a more comprehensive evaluation.

[0034] The skill measurement unit can refer to the test taker's past performance data and evaluate individual growth rates and areas for improvement. In the skill measurement unit, for example, the generation AI analyzes the test taker's past performance data and evaluates individual growth rates. For example, it draws a growth curve based on past test results. The skill measurement unit also evaluates individual areas for improvement based on the test taker's past performance data. For example, it can identify shortcomings in the test taker's skills based on past feedback. The skill measurement unit also evaluates individual growth rates and areas for improvement based on the test taker's past performance data. For example, it can compare past test results with current test results and calculate the growth rate. This makes it possible to encourage test takers to improve their skills by evaluating individual growth rates and areas for improvement.

[0035] The skill measurement unit can evaluate the relevance between different tasks and evaluate the overall skill set. For example, the skill measurement unit evaluates the relevance between different tasks using a generation AI to evaluate the overall skill set of the test taker. For example, the results of multiple tasks are integrated to perform an overall evaluation. The skill measurement unit also comprehensively evaluates the test taker's skills based on the relevance between different tasks using a generation AI. For example, the results of each task can be integrated to perform an overall evaluation. The skill measurement unit also develops an algorithm for comprehensively evaluating the test taker's skills based on the relevance between different tasks using a generation AI. For example, the results of each task can be weighted to calculate an overall evaluation. This allows the test taker's skills to be more accurately evaluated by evaluating the relevance between different tasks and evaluating the overall skill set.

[0036] The skill measurement unit can include tasks for evaluating team cooperation and communication skills. For example, the skill measurement unit generates tasks for evaluating team cooperation using the generation AI to measure the examinee's cooperation skills. For example, a task for working together on a project can be set. The skill measurement unit also generates tasks for evaluating communication skills using the generation AI to measure the examinee's communication skills. For example, a task for sharing information and exchanging opinions with team members can be set. The skill measurement unit also generates tasks for evaluating team cooperation and communication skills using the generation AI to comprehensively evaluate the examinee's skills. For example, a comprehensive evaluation can be made based on team cooperation and communication skills. In this way, by including tasks for evaluating team cooperation and communication skills, the examinee's skills can be more accurately evaluated.

[0037] When providing feedback on the results, the skill measurement unit can refer to the test taker's past feedback history and provide specific advice tailored to each test taker's individual growth. In the skill measurement unit, for example, the generation AI analyzes the test taker's past feedback history and provides specific advice tailored to each test taker's individual growth. For example, advice is generated based on past areas for improvement. In addition, the skill measurement unit can provide specific advice tailored to each test taker's individual growth based on the test taker's past feedback history. For example, advice to encourage test taker skill improvement can be generated based on the content of past feedback. In addition, the skill measurement unit develops an algorithm that enables the generation AI to provide specific advice tailored to each test taker's individual growth based on the test taker's past feedback history. For example, a specific action plan can be provided to encourage test taker skill improvement based on the content of past feedback. In this way, by referring to the test taker's past feedback history and providing specific advice tailored to each test taker's individual growth, test takers can be encouraged to improve their skills.

[0038] The skill measurement unit can reflect industry best practices and the latest research results when providing feedback on the results. For example, the generative AI in the skill measurement unit refers to industry best practices and provides feedback based on them. For example, it generates advice based on the latest technological trends and success stories. The skill measurement unit can also refer to the latest research results and provide feedback based on them. For example, it can generate advice based on the latest research papers and technical reports. The skill measurement unit can also provide feedback to improve the test taker's skills based on industry best practices and the latest research results. For example, it can provide a specific action plan based on the latest technological trends and success stories. In this way, by providing feedback that reflects industry best practices and the latest research results, it is possible to encourage the test taker to improve their skills.

[0039] The skill measurement unit can integrate feedback from different evaluators and provide advice from multiple perspectives. For example, the generation AI can integrate feedback from different evaluators and provide advice from multiple perspectives. For example, the generation AI can generate feedback that combines technical and business evaluations. The skill measurement unit also comprehensively evaluates the examinee's skills based on feedback from different evaluators. For example, the generation AI can integrate feedback from superiors, colleagues, and external evaluators to provide an overall evaluation. The skill measurement unit also develops an algorithm for the generation AI to comprehensively evaluate the examinee's skills based on feedback from different evaluators. For example, the generation AI can weight the feedback from each evaluator and calculate an overall evaluation. This allows the generation AI to integrate feedback from different evaluators and provide advice from multiple perspectives, thereby more accurately evaluating the examinee's skills.

[0040] The skill measurement unit can visualize the feedback content so that the test taker can intuitively understand it. For example, the skill measurement unit causes the generation AI to visualize the feedback content so that the test taker can intuitively understand it. For example, the skill measurement unit displays the feedback using graphs and charts. The skill measurement unit also develops an algorithm that causes the generation AI to visualize the feedback content so that the test taker can intuitively understand it. For example, the feedback content can be displayed as an infographic. The skill measurement unit also builds a system that causes the generation AI to visualize the feedback content so that the test taker can intuitively understand it. For example, the feedback content can be visualized in real time and provided to the test taker. In this way, by visualizing the feedback content so that the test taker can intuitively understand it, it is possible to encourage the test taker to improve their skills.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The evaluation system may further include a learning history reference unit that references the user's learning history. In the learning history reference unit, for example, the generation AI analyzes the user's past learning history and evaluates the user's learning progress. For example, it can suggest the next content to study based on the content previously studied. The learning history reference unit also allows the generation AI to evaluate the effectiveness of learning based on the user's learning history. For example, it can compare past learning content with the current skill level to measure the effectiveness of learning. The learning history reference unit also allows the generation AI to provide advice to increase learning motivation based on the user's learning history. For example, it can set the next learning goal based on past successful experiences. In this way, by referring to the user's learning history and evaluating the progress and effectiveness of learning, it is possible to encourage the user to improve their skills.

[0043] The evaluation system may further include a comparison unit that compares the user's skills with those of other users. For example, the comparison unit allows the generation AI to analyze the skill data of multiple users and perform a comparative evaluation. For example, the comparison unit can compare the results of multiple users performing the same task to evaluate their relative skill levels. The comparison unit also allows the generation AI to provide a comparison result with other users based on the user's skill data. For example, the comparison unit can evaluate the level of skill of the user compared to other users in the same industry or occupation. The comparison unit also develops an algorithm that allows the generation AI to provide a comparison result with other users based on the user's skill data. For example, the comparison unit can weight each user's skill data and calculate an overall evaluation. This allows the user's skills to be compared with those of other users to evaluate their relative skill levels.

[0044] The evaluation system may further include a visualization unit that visually displays the user's skills. The visualization unit, for example, uses the generation AI to analyze the user's skill data and visually display it. For example, it can display skill progress using graphs or charts. The visualization unit also visually displays the user's skill data based on the generation AI. For example, it can display skill strengths and weaknesses using different colors. The visualization unit also develops an algorithm for the generation AI to visually display the user's skill data based on the user's skill data. For example, it can display skill progress in real time. This allows the user to intuitively understand the skill progress by visually displaying the user's skills.

[0045] The evaluation system may further include a sharing unit that shares the user's skills with other users. In the sharing unit, for example, the generation AI analyzes the user's skill data and shares it with other users. For example, the sharing unit can compare the results with those of other users who performed the same task and provide feedback. The sharing unit also shares the user's skill data with other users based on the generation AI. For example, skill data can be shared with other users in the same industry or occupation and they can learn from each other. The sharing unit also develops an algorithm that allows the generation AI to share the user's skill data with other users based on the user's skill data. For example, each user's skill data can be anonymized and shared. In this way, by sharing the user's skills with other users, they can learn from each other and encourage skill improvement.

[0046] The evaluation system may further include a tracking unit that tracks the user's skills over the long term. For example, the tracking unit allows the generation AI to analyze the user's skill data over the long term and track changes in the skills. For example, it can compare past skill data with current skill data to evaluate skill improvement or decline. The tracking unit also allows the generation AI to track long-term skill changes based on the user's skill data. For example, it can periodically conduct skill tests and record the results. The tracking unit also develops an algorithm for the generation AI to track long-term skill changes based on the user's skill data. For example, it can display skill changes in graphs or charts to make them visually understandable. By tracking the user's skills over the long term, it is possible to evaluate skill improvement or decline and provide appropriate feedback.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The task setting unit automatically sets tasks according to the specific skills required by the company. For example, based on prompts containing the skills and task requirements required by the company, the generation AI generates a task such as "Design an algorithm that returns optimal search results from a specific dataset" as a task for evaluating the skills of a search algorithm. The generation AI can also generate a task such as "Design an algorithm that recommends optimal products based on a user's behavioral history" as a task for evaluating the skills of a recommendation system. The generation AI can also generate a task such as "Design an algorithm that analyzes text data and extracts specific information" as a task for evaluating natural language processing skills. Step 2: The data generation unit automatically generates data according to the set task. For example, based on prompts including the type and format of data required for the task, the generation AI generates a dataset including user behavior history and product information as data for evaluating the skill of a recommendation system. The generation AI can also generate large amounts of text data as data for evaluating the skill of a search algorithm. The generation AI can also generate text data on various topics as data for evaluating natural language processing skills. Step 3: The skill measurement unit measures the test taker's AI skills using the set tasks and the generated data. For example, to evaluate natural language processing skills, the generation AI can evaluate how accurately the test taker can perform text analysis using the generated text data. The generation AI can also evaluate how effectively the test taker can recommend products using the generated dataset to evaluate recommendation system skills. The generation AI can also evaluate how accurately the test taker can return search results using the generated dataset to evaluate search algorithm skills.

[0049] (Example 2) An evaluation system according to an embodiment of the present invention uses generative AI to efficiently evaluate human resources with specific AI skills that companies are looking for. In this evaluation system, generative AI automatically sets tasks and generates data, providing a platform for measuring AI skills. This allows the evaluation system to efficiently evaluate human resources with the specific AI skills that companies are looking for.

[0050] An evaluation system according to an embodiment includes a task setting unit, a data generation unit, and a skill measurement unit. The task setting unit automatically sets tasks according to specific skills desired by a company. For example, the generation AI generates a task such as "Design an algorithm that returns optimal search results from a specific dataset" as a task for evaluating the skills of a search algorithm based on prompts including the skills desired by the company and task requirements. The generation AI can also generate a task such as "Design an algorithm that recommends optimal products based on user behavior history" as a task for evaluating the skills of a recommendation system. The generation AI can also generate a task such as "Design an algorithm that analyzes text data and extracts specific information" as a task for evaluating natural language processing skills. The data generation unit automatically generates data according to the set task. For example, the generation AI generates a dataset including user behavior history and product information as data for evaluating the skills of a recommendation system based on prompts including the type and format of data required for the task. The generation AI can also generate large amounts of text data as data for evaluating the skills of a search algorithm. The generation AI can also generate text data on various topics as data for evaluating natural language processing skills. The skill measurement unit measures the AI ​​skills of the test taker using the set tasks and the generated data. For example, the generation AI can evaluate how accurately the test taker can perform text analysis using the generated text data to evaluate natural language processing skills. The generation AI can also evaluate how effectively the test taker can recommend products using the generated dataset to evaluate recommendation system skills. The generation AI can also evaluate how accurately the test taker can return search results using the generated dataset to evaluate search algorithm skills. This allows the evaluation system according to the embodiment to efficiently evaluate personnel with specific AI skills desired by companies.For example, companies can automatically generate tasks and data to evaluate talent specializing in search algorithms and accurately measure their skills. Companies can also generate tasks and data to evaluate talent specializing in recommendation systems and natural language processing and measure their skills. This allows companies to efficiently find the right talent.

[0051] The task setting unit can refer to past performance data of test takers and generate tasks optimized for each test taker. For example, the task setting unit uses a generation AI to analyze past performance data of test takers and identify each test taker's strengths and weaknesses. For example, the task setting unit generates tasks that focus on specific skill sets based on past test results and feedback. Furthermore, the task setting unit uses the generation AI to generate tasks optimized for each test taker based on the test taker's past performance data. For example, the task setting unit can set tasks related to areas in which the test taker was weak in the past and focus on evaluating those skills. Furthermore, the task setting unit uses the generation AI to generate tasks to promote the test taker's growth based on the test taker's past performance data. For example, the task setting unit can set tasks related to areas in which the test taker was strong in the past and further improve those skills. By generating tasks optimized for each test taker, the test taker's skills can be evaluated more accurately.

[0052] The task setting unit can refer to the latest industry reports and news to reflect challenges and trends specific to the company's industry. For example, the task setting unit has the generation AI collect the latest industry reports and news and generate tasks based on them. For example, it sets tasks that reflect current market trends and technological trends. The task setting unit also refers to the latest industry reports and news to reflect challenges specific to the industry. For example, it can generate tasks that reflect technical challenges and market needs in a specific industry. The task setting unit also refers to the latest industry reports and news to reflect industry trends. For example, it can generate tasks that reflect current technological trends and market changes. In this way, by generating tasks that reflect challenges and trends specific to the industry, the skills required by companies can be more accurately evaluated.

[0053] The task setting unit can use the emotion estimation function to estimate the examinee's current emotional state and generate a task with a level of difficulty appropriate to that state. For example, the generation AI of the task setting unit analyzes the examinee's facial expressions and voice to estimate the examinee's current emotional state. For example, if the examinee is highly stressed, the task setting unit sets a task with a lower level of difficulty. The task setting unit also generates a task with an appropriate level of difficulty based on the examinee's emotional state. For example, if the examinee is relaxed, the task setting unit can set a task with a higher level of difficulty. The task setting unit also generates a task to increase the examinee's motivation based on the examinee's emotional state. For example, the generation AI can set a task with content that will likely interest the examinee. This makes it possible to maximize the examinee's performance by generating a task with a level of difficulty appropriate to the examinee's emotional state.

[0054] The data generation unit can incorporate data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit uses a generation AI to incorporate stock price data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit generates investment simulation data based on stock price fluctuations. The data generation unit can also incorporate weather data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit can generate agricultural simulation data based on weather data. The data generation unit can also incorporate traffic data that changes in real time and generate a dataset that reflects real-world fluctuations. For example, the data generation unit can generate urban planning simulation data based on traffic data. This allows for the generation of a dataset that reflects real-world fluctuations, enabling more realistic evaluations.

[0055] The data generation unit can increase the diversity of the data by including data from different regions and cultural spheres. For example, the data generation unit has the generation AI collect data from different regions and generate a dataset that includes the data to increase the diversity of the data. For example, the data generation unit generates marketing data based on consumer behavior data from different countries. The data generation unit also has the generation AI collect data from different cultural spheres and generate a dataset that includes the data to increase the diversity of the data. For example, the data generation unit can generate natural language processing data based on language data from different cultural spheres. The data generation unit also has the generation AI collect data from different regions and generate a dataset that includes the data to increase the diversity of the data. For example, the data generation unit can generate weather forecast data based on weather data from different regions. This increases the diversity of the data, allowing for a more comprehensive evaluation.

[0056] The data generation unit can use the emotion estimation function to generate data based on user emotions. For example, the generation AI in the data generation unit uses the emotion estimation function to generate data based on positive user reviews. For example, product evaluation data is generated to reflect positive emotions. The data generation unit can also use the emotion estimation function to generate data based on negative user comments. For example, data reflecting areas for improvement in a service can be generated. The data generation unit can also use the emotion estimation function to generate data based on user emotions. For example, data can be generated based on a user's emotion score to evaluate emotional aspects. In this way, by generating data based on user emotions, emotional aspects can be reflected in the evaluation.

[0057] The skill measurement unit can refer to the test taker's past performance data and evaluate individual growth rates and areas for improvement. In the skill measurement unit, for example, the generation AI analyzes the test taker's past performance data and evaluates individual growth rates. For example, it draws a growth curve based on past test results. The skill measurement unit also evaluates individual areas for improvement based on the test taker's past performance data. For example, it can identify shortcomings in the test taker's skills based on past feedback. The skill measurement unit also evaluates individual growth rates and areas for improvement based on the test taker's past performance data. For example, it can compare past test results with current test results and calculate the growth rate. This makes it possible to encourage test takers to improve their skills by evaluating individual growth rates and areas for improvement.

[0058] The skill measurement unit uses the emotion estimation function to reflect the examinee's emotional state in the evaluation and can take into account the effects of stress and motivation. For example, the generation AI in the skill measurement unit uses the emotion estimation function to reflect the examinee's emotional state in the evaluation. For example, if stress is high, the evaluation criteria are adjusted. The skill measurement unit also considers the effects of stress and motivation based on the examinee's emotional state. For example, if the examinee is highly motivated, a task with increased difficulty can be set. The skill measurement unit also adjusts the evaluation criteria based on the examinee's emotional state. For example, if the examinee is relaxed, the normal evaluation criteria can be applied. This allows the examinee's emotional state to be reflected in the evaluation and takes into account the effects of stress and motivation, making for a more accurate evaluation.

[0059] The skill measurement unit can evaluate the relevance between different tasks and evaluate the overall skill set. For example, the skill measurement unit evaluates the relevance between different tasks using a generation AI to evaluate the overall skill set of the test taker. For example, the results of multiple tasks are integrated to perform an overall evaluation. The skill measurement unit also comprehensively evaluates the test taker's skills based on the relevance between different tasks using a generation AI. For example, the results of each task can be integrated to perform an overall evaluation. The skill measurement unit also develops an algorithm for comprehensively evaluating the test taker's skills based on the relevance between different tasks using a generation AI. For example, the results of each task can be weighted to calculate an overall evaluation. This allows the test taker's skills to be more accurately evaluated by evaluating the relevance between different tasks and evaluating the overall skill set.

[0060] The skill measurement unit can include tasks for evaluating team cooperation and communication skills. For example, the skill measurement unit generates tasks for evaluating team cooperation using the generation AI to measure the examinee's cooperation skills. For example, a task for working together on a project can be set. The skill measurement unit also generates tasks for evaluating communication skills using the generation AI to measure the examinee's communication skills. For example, a task for sharing information and exchanging opinions with team members can be set. The skill measurement unit also generates tasks for evaluating team cooperation and communication skills using the generation AI to comprehensively evaluate the examinee's skills. For example, a comprehensive evaluation can be made based on team cooperation and communication skills. In this way, by including tasks for evaluating team cooperation and communication skills, the examinee's skills can be more accurately evaluated.

[0061] The skill measurement unit can use the emotion estimation function to focus evaluation on skills in the area in which the test taker is strongest. For example, the generation AI uses the emotion estimation function to identify the area in which the test taker is strongest and focuses evaluation on skills in that area. For example, the generation AI sets tasks related to areas in which positive emotions are strong. The skill measurement unit also enables the generation AI to focus evaluation on skills in the area in which the test taker is strongest based on the test taker's emotional state. For example, tasks related to areas in which the test taker is highly motivated can be set. The skill measurement unit also develops an algorithm for the generation AI to focus evaluation on skills in the area in which the test taker is strongest based on the test taker's emotional state. For example, tasks related to areas in which positive emotions are strong can be set and the skill evaluation can be focused on. This allows for a more accurate evaluation of the test taker's skills by focusing evaluation on skills in the area in which the test taker is strongest.

[0062] When providing feedback on the results, the skill measurement unit can refer to the test taker's past feedback history and provide specific advice tailored to each test taker's individual growth. In the skill measurement unit, for example, the generation AI analyzes the test taker's past feedback history and provides specific advice tailored to each test taker's individual growth. For example, advice is generated based on past areas for improvement. In addition, the skill measurement unit can provide specific advice tailored to each test taker's individual growth based on the test taker's past feedback history. For example, advice to encourage test taker skill improvement can be generated based on the content of past feedback. In addition, the skill measurement unit develops an algorithm that enables the generation AI to provide specific advice tailored to each test taker's individual growth based on the test taker's past feedback history. For example, a specific action plan can be provided to encourage test taker skill improvement based on the content of past feedback. In this way, by referring to the test taker's past feedback history and providing specific advice tailored to each test taker's individual growth, test takers can be encouraged to improve their skills.

[0063] The skill measurement unit can reflect industry best practices and the latest research results when providing feedback on the results. For example, the generative AI in the skill measurement unit refers to industry best practices and provides feedback based on them. For example, it generates advice based on the latest technological trends and success stories. The skill measurement unit can also refer to the latest research results and provide feedback based on them. For example, it can generate advice based on the latest research papers and technical reports. The skill measurement unit can also provide feedback to improve the test taker's skills based on industry best practices and the latest research results. For example, it can provide a specific action plan based on the latest technological trends and success stories. In this way, by providing feedback that reflects industry best practices and the latest research results, it is possible to encourage the test taker to improve their skills.

[0064] The skill measurement unit uses the emotion estimation function to provide feedback according to the emotional state of the test taker, thereby increasing motivation. For example, the generation AI in the skill measurement unit uses the emotion estimation function to provide feedback according to the emotional state of the test taker. For example, it generates an encouraging message to elicit positive emotions. The skill measurement unit also allows the generation AI to provide feedback to increase motivation based on the test taker's emotional state. For example, if the test taker is highly motivated, it can provide a message encouraging further efforts. The skill measurement unit also develops an algorithm that allows the generation AI to provide feedback to increase motivation based on the test taker's emotional state. For example, it can analyze the test taker's emotional state and generate an optimal feedback message. This allows the test taker to improve their skills by providing feedback according to their emotional state and increasing their motivation.

[0065] The skill measurement unit can integrate feedback from different evaluators and provide advice from multiple perspectives. For example, the generation AI can integrate feedback from different evaluators and provide advice from multiple perspectives. For example, the generation AI can generate feedback that combines technical and business evaluations. The skill measurement unit also comprehensively evaluates the examinee's skills based on feedback from different evaluators. For example, the generation AI can integrate feedback from superiors, colleagues, and external evaluators to provide an overall evaluation. The skill measurement unit also develops an algorithm for the generation AI to comprehensively evaluate the examinee's skills based on feedback from different evaluators. For example, the generation AI can weight the feedback from each evaluator and calculate an overall evaluation. This allows the generation AI to integrate feedback from different evaluators and provide advice from multiple perspectives, thereby more accurately evaluating the examinee's skills.

[0066] The skill measurement unit can visualize the feedback content so that the test taker can intuitively understand it. For example, the skill measurement unit causes the generation AI to visualize the feedback content so that the test taker can intuitively understand it. For example, the skill measurement unit displays the feedback using graphs and charts. The skill measurement unit also develops an algorithm that causes the generation AI to visualize the feedback content so that the test taker can intuitively understand it. For example, the feedback content can be displayed as an infographic. The skill measurement unit also builds a system that causes the generation AI to visualize the feedback content so that the test taker can intuitively understand it. For example, the feedback content can be visualized in real time and provided to the test taker. In this way, by visualizing the feedback content so that the test taker can intuitively understand it, it is possible to encourage the test taker to improve their skills.

[0067] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0068] The evaluation system may further include a health management unit that monitors the user's health condition. In the health management unit, for example, the generation AI analyzes the user's heart rate and sleep data to evaluate the user's health condition. For example, the generation AI can estimate the stress level based on heart rate fluctuations and set an appropriate task difficulty level. The health management unit also provides feedback according to the user's health condition based on the user's health data. For example, if a lack of sleep is detected, a message encouraging rest can be provided. The health management unit also provides advice to improve the user's health condition based on the user's health data. For example, if a lack of exercise is detected, a message encouraging exercise can be provided. This allows the user's health condition to be monitored and appropriate task difficulty and feedback to be provided, thereby maximizing the user's performance.

[0069] The evaluation system may further include a learning history reference unit that references the user's learning history. In the learning history reference unit, for example, the generation AI analyzes the user's past learning history and evaluates the user's learning progress. For example, it can suggest the next content to study based on the content previously studied. The learning history reference unit also allows the generation AI to evaluate the effectiveness of learning based on the user's learning history. For example, it can compare past learning content with the current skill level to measure the effectiveness of learning. The learning history reference unit also allows the generation AI to provide advice to increase learning motivation based on the user's learning history. For example, it can set the next learning goal based on past successful experiences. In this way, by referring to the user's learning history and evaluating the progress and effectiveness of learning, it is possible to encourage the user to improve their skills.

[0070] The evaluation system may further include an environment adjustment unit that estimates the user's emotions and adjusts the learning environment based on the estimated emotions. The environment adjustment unit, for example, uses a generating AI to analyze the user's emotional state and adjust the learning environment. For example, if the user is feeling stressed, relaxing music can be played. The environment adjustment unit also adjusts the learning environment based on the user's emotional state. For example, if the user is concentrating, notifications can be turned off to provide an environment where the user can concentrate on their studies. The environment adjustment unit also develops an algorithm for the generating AI to adjust the learning environment based on the user's emotional state. For example, the system can analyze the user's emotional state and provide an optimal learning environment. This maximizes the effectiveness of learning by providing a learning environment that matches the user's emotional state.

[0071] The evaluation system may further include a comparison unit that compares the user's skills with those of other users. For example, the comparison unit allows the generation AI to analyze the skill data of multiple users and perform a comparative evaluation. For example, the comparison unit can compare the results of multiple users performing the same task to evaluate their relative skill levels. The comparison unit also allows the generation AI to provide a comparison result with other users based on the user's skill data. For example, the comparison unit can evaluate the level of skill of the user compared to other users in the same industry or occupation. The comparison unit also develops an algorithm that allows the generation AI to provide a comparison result with other users based on the user's skill data. For example, the comparison unit can weight each user's skill data and calculate an overall evaluation. This allows the user's skills to be compared with those of other users to evaluate their relative skill levels.

[0072] The evaluation system may further include a feedback unit that estimates the user's emotions and provides feedback based on the estimated emotions. For example, the feedback unit may have the generation AI analyze the user's emotional state and provide appropriate feedback. For example, if the user has positive emotions, the feedback unit may provide feedback encouraging further challenges. Furthermore, the feedback unit may have the generation AI provide feedback based on the user's emotional state. For example, if the user has negative emotions, the feedback unit may provide an encouraging message. Furthermore, the feedback unit may develop an algorithm for the generation AI to provide feedback based on the user's emotional state. For example, the generation AI may analyze the user's emotional state and generate an optimal feedback message. This may increase the user's motivation by providing feedback according to the user's emotional state.

[0073] The evaluation system may further include a visualization unit that visually displays the user's skills. The visualization unit, for example, uses the generation AI to analyze the user's skill data and visually display it. For example, it can display skill progress using graphs or charts. The visualization unit also visually displays the user's skill data based on the generation AI. For example, it can display skill strengths and weaknesses using different colors. The visualization unit also develops an algorithm for the generation AI to visually display the user's skill data based on the user's skill data. For example, it can display skill progress in real time. This allows the user to intuitively understand the skill progress by visually displaying the user's skills.

[0074] The evaluation system may further include a customization unit that estimates the user's emotions and customizes the learning content based on the estimated emotions. For example, the customization unit may use a generation AI to analyze the user's emotional state and customize the learning content. For example, it may be possible to provide learning content that is likely to interest the user. Furthermore, the customization unit may use the generation AI to customize the learning content based on the user's emotional state. For example, it may be possible to provide learning content with a higher level of difficulty when the user is relaxed. Furthermore, the customization unit may develop an algorithm for the generation AI to customize the learning content based on the user's emotional state. For example, it may analyze the user's emotional state and provide optimal learning content. This may maximize the effectiveness of learning by providing learning content that matches the user's emotional state.

[0075] The evaluation system may further include a sharing unit that shares the user's skills with other users. In the sharing unit, for example, the generation AI analyzes the user's skill data and shares it with other users. For example, the sharing unit can compare the results with those of other users who performed the same task and provide feedback. The sharing unit also shares the user's skill data with other users based on the generation AI. For example, skill data can be shared with other users in the same industry or occupation and they can learn from each other. The sharing unit also develops an algorithm that allows the generation AI to share the user's skill data with other users based on the user's skill data. For example, each user's skill data can be anonymized and shared. In this way, by sharing the user's skills with other users, they can learn from each other and encourage skill improvement.

[0076] The evaluation system may further include a schedule adjustment unit that estimates the user's emotions and adjusts the study schedule based on the estimated emotions. The schedule adjustment unit, for example, uses a generation AI to analyze the user's emotional state and adjust the study schedule. For example, if the user is feeling stressed, the study time can be shortened. The schedule adjustment unit also uses a generation AI to adjust the study schedule based on the user's emotional state. For example, if the user is relaxed, the study time can be extended. The schedule adjustment unit also develops an algorithm for the generation AI to adjust the study schedule based on the user's emotional state. For example, the generation AI can analyze the user's emotional state and provide an optimal study schedule. This allows the study schedule to be provided according to the user's emotional state, maximizing the effectiveness of learning.

[0077] The evaluation system may further include a tracking unit that tracks the user's skills over the long term. For example, the tracking unit allows the generation AI to analyze the user's skill data over the long term and track changes in the skills. For example, it can compare past skill data with current skill data to evaluate skill improvement or decline. The tracking unit also allows the generation AI to track long-term skill changes based on the user's skill data. For example, it can periodically conduct skill tests and record the results. The tracking unit also develops an algorithm for the generation AI to track long-term skill changes based on the user's skill data. For example, it can display skill changes in graphs or charts to make them visually understandable. By tracking the user's skills over the long term, it is possible to evaluate skill improvement or decline and provide appropriate feedback.

[0078] The processing flow of the second embodiment will be briefly explained below.

[0079] Step 1: The task setting unit automatically sets tasks according to the specific skills required by the company. For example, based on prompts containing the skills and task requirements required by the company, the generation AI generates a task such as "Design an algorithm that returns optimal search results from a specific dataset" as a task for evaluating the skills of a search algorithm. The generation AI can also generate a task such as "Design an algorithm that recommends optimal products based on a user's behavioral history" as a task for evaluating the skills of a recommendation system. The generation AI can also generate a task such as "Design an algorithm that analyzes text data and extracts specific information" as a task for evaluating natural language processing skills. Step 2: The data generation unit automatically generates data according to the set task. For example, based on prompts including the type and format of data required for the task, the generation AI generates a dataset including user behavior history and product information as data for evaluating the skill of a recommendation system. The generation AI can also generate large amounts of text data as data for evaluating the skill of a search algorithm. The generation AI can also generate text data on various topics as data for evaluating natural language processing skills. Step 3: The skill measurement unit measures the test taker's AI skills using the set tasks and the generated data. For example, to evaluate natural language processing skills, the generation AI can evaluate how accurately the test taker can perform text analysis using the generated text data. The generation AI can also evaluate how effectively the test taker can recommend products using the generated dataset to evaluate recommendation system skills. The generation AI can also evaluate how accurately the test taker can return search results using the generated dataset to evaluate search algorithm skills.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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).

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0114] 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.

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

[0116] The 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.

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

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

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

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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."

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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]

[0147] 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 task setting unit that automatically sets tasks according to the specific skills required by the company; a data generating unit that automatically generates data according to the task set by the task setting unit; a skill measurement unit that measures the AI ​​skills of the examinee using the tasks set by the task setting unit and the data generated by the data generation unit. A system characterized by:

2. The data generation unit Incorporating real-time, changing data to generate datasets that reflect real-world fluctuations 2. The system of claim 1.

3. The skill measurement unit Review test-taker's past performance data to assess individual growth and areas for improvement 2. The system of claim 1.

4. The skill measurement unit When providing feedback on results, we refer to the candidate's past feedback history and provide specific advice tailored to their individual growth.

2. The system of claim 1.

5. The task setting unit Using emotion estimation functionality, the test-taker's current emotional state is estimated and tasks with a difficulty level appropriate to that state are generated.

2. The system of claim 1.

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