System

Abstract

An object of a system according to an embodiment is to reduce skill mismatches and find appropriate human resources.SOLUTION: A system according to an embodiment includes a performance data registration unit, a performance data analysis unit, and a skill matching unit. The result data registration unit registers result data. The result data analysis unit analyzes the result data registered by the result data registration unit. The skill matching unit performs skill matching on the basis of a result of the analysis by the performance data analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to find suitable personnel due to a mismatch in skills.

[0005] The system according to the embodiment aims to reduce skill mismatches and find suitable talent. [Means for solving the problem]

[0006] The system according to the embodiment includes a performance data registration unit, a performance data analysis unit, and a skill matching unit. The performance data registration unit registers performance data. The performance data analysis unit analyzes the performance data registered by the performance data registration unit. The skill matching unit performs skill matching based on the results of the analysis by the performance data analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce skill mismatch and find suitable talent. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The matching system according to an embodiment of the present invention is a system in which performance data is registered on a matching site, analyzed by a generation AI, and the system resolves skill mismatches and matches candidates with compatible personnel. This allows the matching system to resolve skill mismatches based on the performance data and match candidates with compatible personnel.

[0029] A matching system according to an embodiment includes a performance data registration unit, a performance data analysis unit, and a skill matching unit. The performance data registration unit registers a user's performance data. For example, it can register programming code, past project performance, video data, etc. The performance data analysis unit uses a generation AI to analyze the registered performance data. For example, the generation AI evaluates the quality and efficiency of programming code, the beauty and originality of design work, video editing skills, etc. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The skill matching unit matches skills based on the analysis results. For example, a job posting seeking a programmer proficient in a specific programming language matches a programmer highly rated in that language. This allows the matching system according to an embodiment to resolve skill mismatches based on performance data and match candidates with compatible talent.

[0030] The achievement data registration unit can gamify the process of registering achievement data, allowing the user to register data while having fun. The achievement data registration unit can gamify, for example, the process of registering achievement data, allowing the user to register data while having fun. For example, points can be earned for each registration, and a badge or title can be earned when a certain number of points are reached. In this way, by gamifying the process of registering achievement data, the user can register data while having fun.

[0031] The achievement data registration unit can provide feedback that visualizes the progress and growth of the user by comparing the achievement data with the previously registered data. For example, when the user registers achievement data, the achievement data registration unit provides feedback that visualizes the progress and growth by comparing the achievement data with the previously registered data. For example, the achievement data registration unit displays the trajectory of growth using a graph or chart. This makes it possible to compare the achievement data with the previously registered data of the user and visualize the progress and growth, thereby improving the motivation of the user.

[0032] The achievement data registration unit can enable the registration of achievement data using a new interface such as voice input or gesture input. The achievement data registration unit, for example, enables the registration of achievement data using voice input. For example, when a user speaks achievements into a microphone, the speech is automatically converted into text data using voice recognition technology and registered. The achievement data registration unit can also register achievement data using gesture input. For example, a motion sensor is used to recognize a user's gestures and register the data. This allows achievement data to be registered using a new interface such as voice input or gesture input.

[0033] The performance data registration unit can add a function that allows real-time feedback from other users. The performance data registration unit adds a function that allows real-time feedback from other users when registering performance data, for example. For example, a chat function is introduced that allows other users to provide comments and advice during registration. This allows the quality of performance data to be improved by receiving real-time feedback from other users.

[0034] The performance data analysis unit can perform a more accurate skill evaluation by taking into account the success rates and failure rates of the user's past projects. For example, when analyzing performance data, the performance data analysis unit can perform a more accurate skill evaluation by taking into account the success rates and failure rates of the user's past projects. For example, a higher evaluation is given to projects with a higher success rate. In this way, by taking into account the success rates and failure rates of past projects, a more accurate skill evaluation is possible.

[0035] The performance data analysis unit displays the analysis results of performance data in a visually easy-to-understand manner for the user, thereby increasing the transparency of the analysis process. The performance data analysis unit, for example, displays the analysis results of performance data in a visually easy-to-understand manner for the user. For example, the analysis results are visualized using graphs or charts. This allows the analysis results to be displayed in a visually easy-to-understand manner, increasing the transparency of the analysis process and promoting the user's understanding.

[0036] The performance data analysis unit can perform cross-domain analysis using data sets from different industries to promote the discovery of new skills. The performance data analysis unit, for example, performs cross-domain analysis using data sets from different industries to analyze performance data, promoting the discovery of new skills. For example, data from the IT industry and the medical industry is combined for analysis. In this way, performing cross-domain analysis using data sets from different industries promotes the discovery of new skills.

[0037] The performance data analysis unit can add a function to evaluate a relative skill level by comparing the analysis results of the performance data with other users. The performance data analysis unit can add a function to evaluate a relative skill level by, for example, comparing the analysis results of the performance data with other users. For example, it can display a ranking among users with the same skill set. This allows the relative skill level to be evaluated by comparing the analysis results with other users.

[0038] The skill matching unit can take into account the user's past matching history and prioritize matching with a high success rate. For example, in skill matching, the skill matching unit takes into account the user's past matching history and prioritizes matching with a high success rate. For example, matching with projects similar to projects that have been successful in the past is performed. In this way, by taking into account the past matching history and prioritizing matching with a high success rate, user satisfaction is improved.

[0039] The skill matching unit can explore various possibilities for utilizing the user's skills, including job information from different industries and fields. For example, the skill matching unit can include job information from different industries and fields when matching skills, exploring various possibilities for utilizing the user's skills. For example, it can match a user with skills in the IT industry with a project in the medical industry. In this way, by including job information from different industries and fields, it is possible to explore various possibilities for utilizing the user's skills.

[0040] The skill matching unit can link the skill matching results with the user's geographical location information to perform region-specific matching. For example, the skill matching unit links the skill matching results with the user's geographical location information to perform region-specific matching. For example, projects close to the user's place of residence are given priority in matching. In this way, linking with the geographical location information makes it possible to perform region-specific matching.

[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 performance data registration unit can automatically suggest the optimal format depending on the type of data the user is registering. For example, when registering programming code, it will suggest the code format and comment writing method, and when registering design work, it will suggest the image resolution and file format. This allows users to register data efficiently and improves the quality of the data.

[0043] The skill matching unit can predict future skills based on the user's skill set and provide learning resources. For example, in addition to current skills, it can suggest programming languages ​​and technologies that are predicted to be in high demand in the future and provide related online courses and learning materials. This allows users to continuously improve their skills.

[0044] The performance data registration unit can add a function that allows users to receive evaluations and comments from other users on the data they register. For example, other users can review registered programming code and comment on areas for improvement or merit. This allows users to receive feedback from other perspectives and improve the quality of their data.

[0045] The skills matching unit can suggest career paths in different industries and fields based on the user's skill set. For example, for a user with skills in the IT industry, it can suggest career paths in the medical or education industry and provide related job information. This allows users to have opportunities to utilize their skills in a variety of fields.

[0046] The performance data analysis unit uses the user's past project data to analyze the factors that led to the success or failure of a project and can provide advice for future projects. For example, it can extract common elements from successful projects and make specific suggestions for applying them to future projects. This allows the user to use their past experience to more effectively advance their projects.

[0047] The performance data analysis unit can suggest collaboration possibilities with other users based on the user's skill set. For example, it can match users with different skills and propose joint projects. This allows users to utilize their own skills while also providing opportunities to work on new projects in collaboration with other users.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The performance data registration unit registers the user's performance data. For example, programming code, past project performance, video data, etc. can be registered. Step 2: In the performance data analysis section, the generation AI analyzes the registered performance data. For example, the generation AI evaluates the quality and efficiency of programming code, the beauty and originality of design work, and video editing techniques. The generation AI performs the analysis using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The skill matching unit performs skill matching based on the analysis results. For example, for a job posting requesting a programmer who is proficient in a specific programming language, the skill matching unit matches the job posting with a programmer who has a high reputation in that language. In this way, the matching system according to the embodiment can eliminate skill mismatches based on performance data and match candidates with compatible personnel.

[0050] (Example 2) The matching system according to an embodiment of the present invention is a system in which performance data is registered on a matching site, analyzed by a generation AI, and the system resolves skill mismatches and matches candidates with compatible personnel. This allows the matching system to resolve skill mismatches based on the performance data and match candidates with compatible personnel.

[0051] A matching system according to an embodiment includes a performance data registration unit, a performance data analysis unit, and a skill matching unit. The performance data registration unit registers a user's performance data. For example, it can register programming code, past project performance, video data, etc. The performance data analysis unit uses a generation AI to analyze the registered performance data. For example, the generation AI evaluates the quality and efficiency of programming code, the beauty and originality of design work, video editing skills, etc. The generation AI performs analysis using a text generation AI (e.g., LLM) or a multimodal generation AI. The skill matching unit matches skills based on the analysis results. For example, a job posting seeking a programmer proficient in a specific programming language matches a programmer highly rated in that language. This allows the matching system according to an embodiment to resolve skill mismatches based on performance data and match candidates with compatible talent.

[0052] The achievement data registration unit can analyze the user's emotional state using an emotion estimation function and provide an interface for eliciting positive emotions. For example, when the user registers achievement data, the achievement data registration unit uses a camera or microphone to analyze the user's facial expressions and voice in real time and estimate the emotional state. For example, if the user is nervous, a message to relax is displayed. This allows the user's emotional state to be analyzed and positive emotions to be elicited, making the registration of achievement data smoother.

[0053] The achievement data registration unit can gamify the process of registering achievement data, allowing the user to register data while having fun. The achievement data registration unit can gamify, for example, the process of registering achievement data, allowing the user to register data while having fun. For example, points can be earned for each registration, and a badge or title can be earned when a certain number of points are reached. In this way, by gamifying the process of registering achievement data, the user can register data while having fun.

[0054] The achievement data registration unit can provide feedback that visualizes the progress and growth of the user by comparing the achievement data with the previously registered data. For example, when the user registers achievement data, the achievement data registration unit provides feedback that visualizes the progress and growth by comparing the achievement data with the previously registered data. For example, the achievement data registration unit displays the trajectory of growth using a graph or chart. This makes it possible to compare the achievement data with the previously registered data of the user and visualize the progress and growth, thereby improving the motivation of the user.

[0055] The achievement data registration unit can enable the registration of achievement data using a new interface such as voice input or gesture input. The achievement data registration unit, for example, enables the registration of achievement data using voice input. For example, when a user speaks achievements into a microphone, the speech is automatically converted into text data using voice recognition technology and registered. The achievement data registration unit can also register achievement data using gesture input. For example, a motion sensor is used to recognize a user's gestures and register the data. This allows achievement data to be registered using a new interface such as voice input or gesture input.

[0056] The performance data registration unit can add a function that allows real-time feedback from other users. The performance data registration unit adds a function that allows real-time feedback from other users when registering performance data, for example. For example, a chat function is introduced that allows other users to provide comments and advice during registration. This allows the quality of performance data to be improved by receiving real-time feedback from other users.

[0057] The achievement data registration unit can use the emotion estimation function to analyze the emotional response of the user to the achievement data registered and make suggestions for improving the registered content. The achievement data registration unit, for example, uses the emotion estimation function to analyze the emotional response of the user to the achievement data registered and make suggestions for improving the registered content. For example, if the user has negative emotions, specific improvements are suggested. In this way, the emotion estimation function is used to analyze the user's emotional response and improve the quality of the registered content.

[0058] The performance data analysis unit can use the emotion estimation function to identify the user's emotional strengths and weaknesses and perform skill evaluation based on the identified strengths and weaknesses. For example, when analyzing performance data, the performance data analysis unit can use the emotion estimation function to identify the user's emotional strengths and weaknesses and perform skill evaluation based on the identified strengths and weaknesses. For example, the performance data analysis unit can give a high rating to projects for which the user has positive emotions. This allows for more accurate skill evaluation by identifying emotional strengths and weaknesses and performing skill evaluation based on the identified strengths and weaknesses.

[0059] The performance data analysis unit can perform a more accurate skill evaluation by taking into account the success rates and failure rates of the user's past projects. For example, when analyzing performance data, the performance data analysis unit can perform a more accurate skill evaluation by taking into account the success rates and failure rates of the user's past projects. For example, a higher evaluation is given to projects with a higher success rate. In this way, by taking into account the success rates and failure rates of past projects, a more accurate skill evaluation is possible.

[0060] The performance data analysis unit displays the analysis results of performance data in a visually easy-to-understand manner for the user, thereby increasing the transparency of the analysis process. The performance data analysis unit, for example, displays the analysis results of performance data in a visually easy-to-understand manner for the user. For example, the analysis results are visualized using graphs or charts. This allows the analysis results to be displayed in a visually easy-to-understand manner, increasing the transparency of the analysis process and promoting the user's understanding.

[0061] The performance data analysis unit can perform cross-domain analysis using data sets from different industries to promote the discovery of new skills. The performance data analysis unit, for example, performs cross-domain analysis using data sets from different industries to analyze performance data, promoting the discovery of new skills. For example, data from the IT industry and the medical industry is combined for analysis. In this way, performing cross-domain analysis using data sets from different industries promotes the discovery of new skills.

[0062] The performance data analysis unit can add a function to evaluate a relative skill level by comparing the analysis results of the performance data with other users. The performance data analysis unit can add a function to evaluate a relative skill level by, for example, comparing the analysis results of the performance data with other users. For example, it can display a ranking among users with the same skill set. This allows the relative skill level to be evaluated by comparing the analysis results with other users.

[0063] The performance data analysis unit can use the emotion estimation function to collect users' emotional reactions to the analysis results and use the collected information to improve the analysis algorithm. For example, the performance data analysis unit can use the emotion estimation function to collect users' emotional reactions to the analysis results and use the collected information to improve the analysis algorithm. For example, the performance data analysis unit can preferentially adopt analysis results in which users have positive reactions. In this way, collecting emotional reactions and using the collected information to improve the analysis algorithm enables more accurate analysis.

[0064] The skill matching unit can use the emotion estimation function to evaluate the emotional aptitude of the user and perform emotionally appropriate matching. For example, when matching skills, the skill matching unit uses the emotion estimation function to evaluate the emotional aptitude of the user and perform emotionally appropriate matching. For example, matching is preferentially performed for projects for which the user has positive emotions. In this way, evaluating emotional aptitude and performing emotionally appropriate matching improves user satisfaction.

[0065] The skill matching unit can take into account the user's past matching history and prioritize matching with a high success rate. For example, in skill matching, the skill matching unit takes into account the user's past matching history and prioritizes matching with a high success rate. For example, matching with projects similar to projects that have been successful in the past is performed. In this way, by taking into account the past matching history and prioritizing matching with a high success rate, user satisfaction is improved.

[0066] The skill matching unit can explore various possibilities for utilizing the user's skills, including job information from different industries and fields. For example, the skill matching unit can include job information from different industries and fields when matching skills, exploring various possibilities for utilizing the user's skills. For example, it can match a user with skills in the IT industry with a project in the medical industry. In this way, by including job information from different industries and fields, it is possible to explore various possibilities for utilizing the user's skills.

[0067] The skill matching unit can link the skill matching results with the user's geographical location information to perform region-specific matching. For example, the skill matching unit links the skill matching results with the user's geographical location information to perform region-specific matching. For example, projects close to the user's place of residence are given priority in matching. In this way, linking with the geographical location information makes it possible to perform region-specific matching.

[0068] The skill matching unit can use the emotion estimation function to monitor the user's emotional response to the matching results in real time and continuously search for the optimal match. The skill matching unit, for example, uses the emotion estimation function to monitor the user's emotional response to the matching results in real time and continuously search for the optimal match. For example, the skill matching unit preferentially adopts matching results in which the user shows positive emotions. In this way, by monitoring the emotional response in real time and continuously searching for the optimal match, user satisfaction is improved.

[0069] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0070] The performance data registration unit can automatically suggest the optimal format depending on the type of data the user is registering. For example, when registering programming code, it will suggest the code format and comment writing method, and when registering design work, it will suggest the image resolution and file format. This allows users to register data efficiently and improves the quality of the data.

[0071] The performance data analysis unit can estimate the user's emotions and customize the feedback of the analysis results based on the estimated emotions. For example, if the user has positive emotions, the analysis results can be highlighted, and if the user has negative emotions, the system can gently suggest areas for improvement. This makes it possible to provide feedback that takes the user's emotions into consideration.

[0072] The skill matching unit can predict future skills based on the user's skill set and provide learning resources. For example, in addition to current skills, it can suggest programming languages ​​and technologies that are predicted to be in high demand in the future and provide related online courses and learning materials. This allows users to continuously improve their skills.

[0073] The performance data registration unit can add a function that allows users to receive evaluations and comments from other users on the data they register. For example, other users can review registered programming code and comment on areas for improvement or merit. This allows users to receive feedback from other perspectives and improve the quality of their data.

[0074] The performance data analysis unit uses the emotion estimation function to analyze the user's emotional reactions to past successful projects and identify the factors that contributed to their success. For example, it can extract common elements from projects that the user has positive feelings about and provide advice on how to apply these to future projects. This allows the user to understand their own success patterns and progress with projects more effectively.

[0075] The skills matching unit can suggest career paths in different industries and fields based on the user's skill set. For example, for a user with skills in the IT industry, it can suggest career paths in the medical or education industry and provide related job information. This allows users to have opportunities to utilize their skills in a variety of fields.

[0076] The performance data registration unit can use the emotion estimation function to monitor the user's emotional response to the data they register in real time and optimize the registration process. For example, if the user is feeling stressed, the unit can suggest ways to simplify the registration process so that the user can continue registration in a relaxed state. This makes it possible to provide a registration process that takes the user's emotions into consideration.

[0077] The performance data analysis unit uses the user's past project data to analyze the factors that led to the success or failure of a project and can provide advice for future projects. For example, it can extract common elements from successful projects and make specific suggestions for applying them to future projects. This allows the user to use their past experience to more effectively advance their projects.

[0078] The skill matching unit can use the emotion estimation function to analyze the emotional reactions of users to matching results and improve the matching algorithm. For example, the algorithm can be adjusted to prioritize matching results that users have positive emotions about and avoid results that users have negative emotions about. This can improve user satisfaction.

[0079] The performance data analysis unit can suggest collaboration possibilities with other users based on the user's skill set. For example, it can match users with different skills and propose joint projects. This allows users to utilize their own skills while also providing opportunities to work on new projects in collaboration with other users.

[0080] The processing flow of the second embodiment will be briefly explained below.

[0081] Step 1: The performance data registration unit registers the user's performance data. For example, programming code, past project performance, video data, etc. can be registered. Step 2: In the performance data analysis section, the generation AI analyzes the registered performance data. For example, the generation AI evaluates the quality and efficiency of programming code, the beauty and originality of design work, and video editing techniques. The generation AI performs the analysis using text generation AI (e.g., LLM) and multimodal generation AI. Step 3: The skill matching unit performs skill matching based on the analysis results. For example, for a job posting requesting a programmer who is proficient in a specific programming language, the skill matching unit matches the job posting with a programmer who has a high reputation in that language. In this way, the matching system according to the embodiment can eliminate skill mismatches based on performance data and match candidates with compatible personnel.

[0082] 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.

[0083] 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.

[0084] 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.

[0085] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0086] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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).

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 AI 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.

[0099] 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.

[0100] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0101] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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 AI 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.

[0114] 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.

[0115] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0116] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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 AI 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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."

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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]

[0149] 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 performance data registration unit for registering performance data; a performance data analysis unit that analyzes the performance data registered by the performance data registration unit; a skill matching unit that performs skill matching based on the results of the analysis by the performance data analysis unit. A system characterized by:

2. The performance data registration unit Analyzes the user's emotional state using emotion estimation functionality and provides an interface to elicit positive emotions.

2. The system of claim 1.

3. The performance data registration unit The registration of the performance data can be performed using a new interface with voice input or gesture input.

2. The system of claim 1.

4. The performance data analysis unit Use emotion estimation to identify users' emotional strengths and weaknesses and evaluate their skills accordingly 2. The system of claim 1.

5. The skill matching unit Evaluating the user's emotional suitability using an emotion estimation function and performing the emotionally suitable matching 2. The system of claim 1.

6. The performance data registration unit Add a feature to receive real-time feedback from other users 2. The system of claim 1.

7. The performance data analysis unit The analysis results of the performance data are displayed visually and in an easy-to-understand manner for users, increasing the transparency of the analysis process.

2. The system of claim 1.

8. The skill matching unit Emotion estimation function monitors users' emotional reactions to matching results in real time, continuously searching for optimal matches.

2. The system of claim 1.

Images