System

Abstract

An object of a system according to an embodiment is to objectively evaluate and improve talent of a user.SOLUTION: A system includes an evaluation unit and a feedback unit. The evaluation unit analyzes data provided by the user and evaluates the talent of the user based on the data. The feedback unit provides the user with a result evaluated by the evaluation 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 that it provides limited opportunities for ordinary individuals to have their talents objectively evaluated and improved.

[0005] The system according to the embodiment aims to objectively evaluate and improve the talent of a user. [Means for solving the problem]

[0006] The system according to the embodiment includes an evaluation unit and a feedback unit. The evaluation unit analyzes data provided by a user and evaluates the user's talent based on the data. The feedback unit provides the user with the results of the evaluation by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can objectively evaluate and improve the talent of a user. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A communication tool according to an embodiment of the present invention is a system in which a generation AI analyzes data provided by a user, an evaluation unit evaluates the data, and a feedback unit provides the evaluation results to the user. This allows the communication tool to objectively evaluate the user's talent and provide feedback, thereby discovering talent and improving performance.

[0029] A communication tool according to an embodiment includes a generation AI, an evaluation unit, and a feedback unit. The generation AI analyzes data provided by a user. For example, the generation AI analyzes an essay written by the user and evaluates its content, structure, grammar, etc. The generation AI can also analyze audio data provided by the user and evaluate pronunciation and intonation. The generation AI can also analyze video data provided by the user and evaluate facial expressions and gestures. The evaluation unit evaluates the user's talent based on the data analyzed by the generation AI. For example, the evaluation unit evaluates the user's writing ability based on the content, structure, and grammar of the essay. The evaluation unit can also evaluate the user's pronunciation and intonation based on the audio data. The evaluation unit can also evaluate the user's facial expressions and gestures based on the video data. The feedback unit provides the user with the results of the evaluation by the evaluation unit. For example, the feedback unit provides the user with the evaluation result of the essay as text feedback. The feedback unit can also provide the user with the evaluation result of the audio data as audio feedback. The feedback unit can also provide the user with the evaluation result of the video data as graphical feedback. As a result, the communication tool according to the embodiment can objectively evaluate the talent of the user and provide feedback, thereby discovering talent and improving performance.

[0030] The evaluation unit can learn the user's past data and analyze the long-term growth pattern of talent. For example, the generation AI collects the user's past essay and presentation data and analyzes the long-term growth pattern of the evaluation unit. For example, the evaluation unit evaluates the improvement of the user's writing ability and presentation skills based on data spanning several years. The generation AI also analyzes the growth pattern of talent based on the past data provided by the user and predicts future growth. For example, the generation AI identifies the user's strengths and weaknesses from the past data and indicates the direction of future growth. The evaluation unit also proposes a specific growth plan to the user by learning the user's past data and analyzing the long-term growth pattern of talent. For example, the generation AI indicates the direction of future learning and training based on the past data. This allows the generation AI to analyze the user's long-term growth pattern and propose a specific growth plan.

[0031] The evaluation unit can perform multifaceted evaluations using at least one different evaluation criterion from among creativity, logic, and expressiveness. For example, when the generation AI evaluates a user's essay, the evaluation unit performs multifaceted evaluations using different evaluation criteria, such as creativity, logic, and expressiveness. For example, the evaluation unit evaluates how original the essay's content is, how logically structured it is, and how expressive it is. Furthermore, when the generation AI evaluates a user's presentation, the evaluation unit performs multifaceted evaluations using different evaluation criteria, such as the presentation's structure, visual expression, and impact on the audience. Furthermore, when the generation AI evaluates a user's musical performance, the evaluation unit performs multifaceted evaluations using different evaluation criteria, such as technical skill, creativity, and expressiveness. For example, the evaluation unit evaluates the performance's technical accuracy, original arrangement, and rich emotional expression. This allows for a multifaceted evaluation of the user's talent, providing a more objective evaluation.

[0032] The evaluation unit can simultaneously evaluate talents in different fields. For example, the generation AI simultaneously evaluates a user's essays and musical performances to comprehensively evaluate talents in different fields. For example, it simultaneously evaluates writing ability and musical expressiveness to evaluate the user's versatility. The evaluation unit also simultaneously evaluates a painting and programming code provided by the user to comprehensively evaluate talents in different fields. For example, it simultaneously evaluates visual creativity and logical thinking ability. The evaluation unit also simultaneously evaluates a user's presentation and musical performances to comprehensively evaluate talents in different fields. For example, it simultaneously evaluates presentation composition ability and musical expressiveness. This allows the user's talents to be evaluated in different fields simultaneously and their versatility to be evaluated.

[0033] The evaluation unit can suggest optimal conditions for demonstrating talent based on the user's lifestyle and environmental factors. For example, when the generation AI evaluates a user's essay, the evaluation unit takes into account the user's lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it suggests the optimal writing time and environment. Furthermore, when the user gives a presentation, the evaluation unit takes into account lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it suggests the optimal practice time and place. Furthermore, when the generation AI evaluates a user's musical performance, the evaluation unit takes into account lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it suggests the optimal practice time and environment. In this way, it is possible to suggest optimal conditions for demonstrating talent by taking into account the user's lifestyle and environmental factors.

[0034] When rating and feedback between users is performed, the reliability of the rating can be improved based on the evaluator's expertise and experience. When rating and feedback is performed between users, for example, the evaluator's expertise and experience are taken into consideration to improve the reliability of the rating. For example, based on the evaluator's profile information, evaluations by evaluators with expertise and experience are prioritized. Furthermore, when rating and feedback is performed between users, a system is constructed to consider the evaluator's expertise and experience to improve the reliability of the rating. For example, based on the evaluator's past rating history, a highly reliable evaluator is identified. Furthermore, an algorithm is developed to consider the evaluator's expertise and experience to improve the reliability of the rating when rating and feedback is performed between users. For example, the rating is weighted based on the evaluator's expertise and experience. In this way, the evaluator's expertise and experience are taken into consideration to improve the reliability of the rating.

[0035] The generative AI can analyze the evaluation results between users and detect and correct consistency and bias in the evaluations. For example, the generative AI analyzes the evaluation results between users and detects and corrects consistency and bias in the evaluations. For example, if there is bias in the evaluation results, the generative AI corrects the bias. The generative AI can also analyze the evaluation results between users and build a system that detects and corrects consistency and bias. For example, if there is inconsistency in the evaluation results, the generative AI ensures that consistency. The generative AI can also analyze the evaluation results between users and develop an algorithm that detects and corrects consistency and bias in the evaluations. For example, if there is bias in the evaluation results, it develops an algorithm to correct the bias. This makes it possible to detect and correct consistency and bias in the evaluations, thereby providing fairer evaluations.

[0036] When conducting peer-to-peer evaluations, it is possible to combine users from different cultural backgrounds and regions to realize evaluations from diverse perspectives. When conducting peer-to-peer evaluations, for example, it is possible to combine users from different cultural backgrounds and regions to realize evaluations from diverse perspectives. For example, users from different countries and regions make evaluations. Furthermore, a system is constructed for combining users from different cultural backgrounds and regions to realize evaluations from diverse perspectives when conducting peer-to-peer evaluations. For example, evaluators are selected based on the evaluator's cultural background and regional information. Furthermore, an algorithm is developed for combining users from different cultural backgrounds and regions to realize evaluations from diverse perspectives when conducting peer-to-peer evaluations. For example, an algorithm is developed for selecting evaluators based on the evaluator's cultural background and regional information. In this way, it is possible to provide evaluations from diverse perspectives by combining users from different cultural backgrounds and regions.

[0037] When users evaluate each other, the evaluation results can be visualized in real time, allowing users to check the progress of their evaluation. When users evaluate each other, for example, a system is constructed that visualizes the evaluation results in real time, allowing users to check the progress of their evaluation. For example, the evaluation results are displayed in a graph or chart. Furthermore, when users evaluate each other, an interface is developed that visualizes the evaluation results in real time, allowing users to check the progress of their evaluation. For example, the evaluation results are displayed on a dashboard. Furthermore, when users evaluate each other, an algorithm is developed that visualizes the evaluation results in real time, allowing users to check the progress of their evaluation. For example, the evaluation results are updated in real time and notified to the user. This visualizes the evaluation results in real time, allowing users to check the progress of their evaluation.

[0038] Generative AI can learn from past successes and failures to discover talent with greater accuracy. For example, generative AI can learn from past successes and failures and use that knowledge when discovering users' talents. For example, it can identify the characteristics of talents that are likely to be successful based on past data. Furthermore, when discovering users' talents, generative AI can analyze past successes and failures to build a system that discovers talent with greater accuracy. For example, it can extract commonalities between successes and use them in talent discovery. Furthermore, generative AI can learn from past successes and failures and develop algorithms that utilize that knowledge when discovering users' talents. For example, it can learn patterns from successes and failures and apply that knowledge to talent discovery. This allows it to learn from past successes and failures to discover talent with greater accuracy.

[0039] The generative AI can analyze user data and provide an individually customized training plan. The generative AI, for example, analyzes user data and provides an individually customized training plan. For example, it identifies the user's weaknesses and suggests a training plan that addresses them. The generative AI also builds a system that provides individually customized training plans to improve user performance. For example, it creates a training plan based on the user's goals. The generative AI also analyzes user data and develops an algorithm that provides individually customized training plans. For example, it adjusts the training plan according to the user's progress. This makes it possible to analyze user data and provide an individually customized training plan.

[0040] Generative AI can combine talents from different fields to explore the possibility of new talents. For example, generative AI can analyze a user's essays and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine writing ability and musical expressiveness. Generative AI can also analyze a user's paintings and programming code to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine visual creativity and logical thinking ability. Generative AI can also analyze a user's presentations and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine presentation composition skills and musical expressiveness. This makes it possible to explore the possibility of new talents by combining talents from different fields.

[0041] Generative AI can provide functions that promote group training and collaboration to improve user performance. For example, generative AI analyzes user data and provides functions that promote group training and collaboration. For example, it groups users with the same goals and trains them together. Generative AI also builds a system that provides functions that promote group training and collaboration to improve user performance. For example, it provides a platform where users can train together. Generative AI also analyzes user data and develops algorithms that provide functions that promote group training and collaboration. For example, it forms optimal groups based on users' skills and goals. This can help improve user performance by promoting group training and collaboration.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] Generative AI can analyze user data and provide individually customized training plans. For example, it can identify the user's weaknesses and suggest training plans that address them. Generative AI can also build a system that provides individually customized training plans to improve user performance. For example, it can create training plans based on the user's goals. Generative AI can also analyze user data and develop algorithms that provide individually customized training plans. For example, it can adjust training plans according to the user's progress. This makes it possible to analyze user data and provide individually customized training plans.

[0044] The evaluation unit can suggest optimal conditions for demonstrating talent based on the user's lifestyle and environmental factors. For example, when the generation AI evaluates a user's essay, it takes into account the user's lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it can suggest the optimal writing time and environment. Furthermore, when the user gives a presentation, the evaluation unit can suggest optimal conditions for demonstrating talent by taking into account lifestyle and environmental factors. For example, it can suggest the optimal practice time and place. Furthermore, when the generation AI evaluates a user's musical performance, it can suggest optimal conditions for demonstrating talent by taking into account lifestyle and environmental factors. For example, it can suggest the optimal practice time and environment. In this way, it can suggest optimal conditions for demonstrating talent by taking into account the user's lifestyle and environmental factors.

[0045] The evaluation unit can simultaneously evaluate talents in different fields. For example, the generation AI can simultaneously evaluate a user's essays and musical performances to comprehensively evaluate talents in different fields. For example, it can simultaneously evaluate writing ability and musical expressiveness to evaluate the user's versatility. The evaluation unit can also simultaneously evaluate a painting and programming code provided by the user to comprehensively evaluate talents in different fields. For example, it can simultaneously evaluate visual creativity and logical thinking ability. The evaluation unit can also simultaneously evaluate a user's presentation and musical performances to comprehensively evaluate talents in different fields. For example, it can simultaneously evaluate presentation composition ability and musical expressiveness. This allows the generation AI to simultaneously evaluate a user's talents in different fields to evaluate their versatility.

[0046] Generative AI can analyze user data and provide individually customized training plans. For example, it can identify the user's weaknesses and suggest training plans that address them. Generative AI can also build a system that provides individually customized training plans to improve user performance. For example, it can create training plans based on the user's goals. Generative AI can also analyze user data and develop algorithms that provide individually customized training plans. For example, it can adjust training plans according to the user's progress. This makes it possible to analyze user data and provide individually customized training plans.

[0047] Generative AI can combine talents from different fields to explore the possibility of new talents. For example, it can analyze a user's essays and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine writing ability and musical expressiveness. Generative AI can also analyze a user's paintings and programming code to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine visual creativity and logical thinking ability. Generative AI can also analyze a user's presentations and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine presentation composition skills and musical expressiveness. This makes it possible to explore the possibility of new talents by combining talents from different fields.

[0048] Generative AI can provide functions that promote group training and collaboration to improve user performance. For example, it analyzes user data and provides functions that promote group training and collaboration. For example, it groups users with the same goals and trains them together. Generative AI can also build systems that provide functions that promote group training and collaboration to improve user performance. For example, it can provide a platform where users can train together. Generative AI can also analyze user data and develop algorithms that provide functions that promote group training and collaboration. For example, it can form optimal groups based on users' skills and goals. This can help improve user performance by promoting group training and collaboration.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generative AI analyzes the data provided by the user. For example, the generative AI analyzes an essay written by the user and evaluates its content, structure, grammar, etc. The generative AI can also analyze audio data provided by the user and evaluate pronunciation and intonation. The generative AI can also analyze video data provided by the user and evaluate facial expressions and gestures. Step 2: The evaluation unit evaluates the user's talent based on the data analyzed by the generative AI. For example, the evaluation unit may evaluate the user's writing ability based on the content, structure, and grammar of the essay. The evaluation unit may also evaluate the user's pronunciation and intonation based on audio data. The evaluation unit may also evaluate the user's facial expressions and gestures based on video data. Step 3: The feedback unit provides the user with the evaluation results from the evaluation unit. For example, the feedback unit may provide the user with the evaluation results of the essay as text feedback. The feedback unit may also provide the user with the evaluation results of the audio data as audio feedback. The feedback unit may also provide the user with the evaluation results of the video data as graphical feedback.

[0051] (Example 2) A communication tool according to an embodiment of the present invention is a system in which a generation AI analyzes data provided by a user, an evaluation unit evaluates the data, and a feedback unit provides the evaluation results to the user. This allows the communication tool to objectively evaluate the user's talent and provide feedback, thereby discovering talent and improving performance.

[0052] A communication tool according to an embodiment includes a generation AI, an evaluation unit, and a feedback unit. The generation AI analyzes data provided by a user. For example, the generation AI analyzes an essay written by the user and evaluates its content, structure, grammar, etc. The generation AI can also analyze audio data provided by the user and evaluate pronunciation and intonation. The generation AI can also analyze video data provided by the user and evaluate facial expressions and gestures. The evaluation unit evaluates the user's talent based on the data analyzed by the generation AI. For example, the evaluation unit evaluates the user's writing ability based on the content, structure, and grammar of the essay. The evaluation unit can also evaluate the user's pronunciation and intonation based on the audio data. The evaluation unit can also evaluate the user's facial expressions and gestures based on the video data. The feedback unit provides the user with the results of the evaluation by the evaluation unit. For example, the feedback unit provides the user with the evaluation result of the essay as text feedback. The feedback unit can also provide the user with the evaluation result of the audio data as audio feedback. The feedback unit can also provide the user with the evaluation result of the video data as graphical feedback. As a result, the communication tool according to the embodiment can objectively evaluate the talent of the user and provide feedback, thereby discovering talent and improving performance.

[0053] The evaluation unit can learn the user's past data and analyze the long-term growth pattern of talent. For example, the generation AI collects the user's past essay and presentation data and analyzes the long-term growth pattern of the evaluation unit. For example, the evaluation unit evaluates the improvement of the user's writing ability and presentation skills based on data spanning several years. The generation AI also analyzes the growth pattern of talent based on the past data provided by the user and predicts future growth. For example, the generation AI identifies the user's strengths and weaknesses from the past data and indicates the direction of future growth. The evaluation unit also proposes a specific growth plan to the user by learning the user's past data and analyzing the long-term growth pattern of talent. For example, the generation AI indicates the direction of future learning and training based on the past data. This allows the generation AI to analyze the user's long-term growth pattern and propose a specific growth plan.

[0054] The evaluation unit can perform multifaceted evaluations using at least one different evaluation criterion from among creativity, logic, and expressiveness. For example, when the generation AI evaluates a user's essay, the evaluation unit performs multifaceted evaluations using different evaluation criteria, such as creativity, logic, and expressiveness. For example, the evaluation unit evaluates how original the essay's content is, how logically structured it is, and how expressive it is. Furthermore, when the generation AI evaluates a user's presentation, the evaluation unit performs multifaceted evaluations using different evaluation criteria, such as the presentation's structure, visual expression, and impact on the audience. Furthermore, when the generation AI evaluates a user's musical performance, the evaluation unit performs multifaceted evaluations using different evaluation criteria, such as technical skill, creativity, and expressiveness. For example, the evaluation unit evaluates the performance's technical accuracy, original arrangement, and rich emotional expression. This allows for a multifaceted evaluation of the user's talent, providing a more objective evaluation.

[0055] The evaluation unit uses the emotion estimation function to analyze the emotional state of the user when demonstrating their talent, and can evaluate the impact of positive emotions on the talent. For example, when the generation AI evaluates the user's essay, the evaluation unit uses the emotion estimation function to analyze the user's emotional state. For example, the evaluation unit analyzes the emotional state when the essay is being written and evaluates the impact of positive emotions on the quality of the writing. Furthermore, when the user gives a presentation, the evaluation unit uses the emotion estimation function to analyze the emotional state and evaluate the impact of positive emotions on the presentation performance. For example, the evaluation unit analyzes the level of nervousness and confidence. Furthermore, when the generation AI evaluates the user's musical performance, the evaluation unit uses the emotion estimation function to analyze the emotional state and evaluate the impact of positive emotions on the quality of the performance. For example, the evaluation unit evaluates the impact of increased emotions during a performance on technique and expressiveness. In this way, the user's emotional state can be analyzed and the impact of positive emotions on talent can be evaluated.

[0056] The evaluation unit can simultaneously evaluate talents in different fields. For example, the generation AI simultaneously evaluates a user's essays and musical performances to comprehensively evaluate talents in different fields. For example, it simultaneously evaluates writing ability and musical expressiveness to evaluate the user's versatility. The evaluation unit also simultaneously evaluates a painting and programming code provided by the user to comprehensively evaluate talents in different fields. For example, it simultaneously evaluates visual creativity and logical thinking ability. The evaluation unit also simultaneously evaluates a user's presentation and musical performances to comprehensively evaluate talents in different fields. For example, it simultaneously evaluates presentation composition ability and musical expressiveness. This allows the user's talents to be evaluated in different fields simultaneously and their versatility to be evaluated.

[0057] The evaluation unit can suggest optimal conditions for demonstrating talent based on the user's lifestyle and environmental factors. For example, when the generation AI evaluates a user's essay, the evaluation unit takes into account the user's lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it suggests the optimal writing time and environment. Furthermore, when the user gives a presentation, the evaluation unit takes into account lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it suggests the optimal practice time and place. Furthermore, when the generation AI evaluates a user's musical performance, the evaluation unit takes into account lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it suggests the optimal practice time and environment. In this way, it is possible to suggest optimal conditions for demonstrating talent by taking into account the user's lifestyle and environmental factors.

[0058] The evaluation unit can use the emotion estimation function to analyze the emotional reactions of the user when their talent is evaluated, thereby improving the evaluation process. For example, when the generation AI evaluates the user's essay, the evaluation unit uses the emotion estimation function to analyze the user's emotional reactions, thereby improving the evaluation process. For example, the evaluation unit analyzes the user's emotional reactions to the evaluation results and adjusts the content of the feedback. Furthermore, when the user gives a presentation, the evaluation unit can use the emotion estimation function to analyze the emotional reactions, thereby improving the evaluation process. For example, the evaluation unit analyzes the user's emotional reactions to the evaluation results and adjusts the content of the feedback. Furthermore, when the generation AI evaluates the user's musical performance, the evaluation unit can use the emotion estimation function to analyze the emotional reactions, thereby improving the evaluation process. For example, the evaluation unit analyzes the user's emotional reactions to the evaluation results and adjusts the content of the feedback. In this way, the user's emotional reactions can be analyzed and the evaluation process can be improved.

[0059] When rating and feedback between users is performed, the reliability of the rating can be improved based on the evaluator's expertise and experience. When rating and feedback is performed between users, for example, the evaluator's expertise and experience are taken into consideration to improve the reliability of the rating. For example, based on the evaluator's profile information, evaluations by evaluators with expertise and experience are prioritized. Furthermore, when rating and feedback is performed between users, a system is constructed to consider the evaluator's expertise and experience to improve the reliability of the rating. For example, based on the evaluator's past rating history, a highly reliable evaluator is identified. Furthermore, an algorithm is developed to consider the evaluator's expertise and experience to improve the reliability of the rating when rating and feedback is performed between users. For example, the rating is weighted based on the evaluator's expertise and experience. In this way, the evaluator's expertise and experience are taken into consideration to improve the reliability of the rating.

[0060] The generative AI can analyze the evaluation results between users and detect and correct consistency and bias in the evaluations. For example, the generative AI analyzes the evaluation results between users and detects and corrects consistency and bias in the evaluations. For example, if there is bias in the evaluation results, the generative AI corrects the bias. The generative AI can also analyze the evaluation results between users and build a system that detects and corrects consistency and bias. For example, if there is inconsistency in the evaluation results, the generative AI ensures that consistency. The generative AI can also analyze the evaluation results between users and develop an algorithm that detects and corrects consistency and bias in the evaluations. For example, if there is bias in the evaluation results, it develops an algorithm to correct the bias. This makes it possible to detect and correct consistency and bias in the evaluations, thereby providing fairer evaluations.

[0061] When conducting peer-to-peer evaluations, it is possible to combine users from different cultural backgrounds and regions to realize evaluations from diverse perspectives. When conducting peer-to-peer evaluations, for example, it is possible to combine users from different cultural backgrounds and regions to realize evaluations from diverse perspectives. For example, users from different countries and regions make evaluations. Furthermore, a system is constructed for combining users from different cultural backgrounds and regions to realize evaluations from diverse perspectives when conducting peer-to-peer evaluations. For example, evaluators are selected based on the evaluator's cultural background and regional information. Furthermore, an algorithm is developed for combining users from different cultural backgrounds and regions to realize evaluations from diverse perspectives when conducting peer-to-peer evaluations. For example, an algorithm is developed for selecting evaluators based on the evaluator's cultural background and regional information. In this way, it is possible to provide evaluations from diverse perspectives by combining users from different cultural backgrounds and regions.

[0062] When users evaluate each other, the evaluation results can be visualized in real time, allowing users to check the progress of their evaluation. When users evaluate each other, for example, a system is constructed that visualizes the evaluation results in real time, allowing users to check the progress of their evaluation. For example, the evaluation results are displayed in a graph or chart. Furthermore, when users evaluate each other, an interface is developed that visualizes the evaluation results in real time, allowing users to check the progress of their evaluation. For example, the evaluation results are displayed on a dashboard. Furthermore, when users evaluate each other, an algorithm is developed that visualizes the evaluation results in real time, allowing users to check the progress of their evaluation. For example, the evaluation results are updated in real time and notified to the user. This visualizes the evaluation results in real time, allowing users to check the progress of their evaluation.

[0063] The emotion estimation function can analyze the emotional response of the user receiving the evaluation and adjust the content of the feedback. For example, the emotion estimation function analyzes the emotional response of the user receiving the evaluation and adjusts the content of the feedback. For example, if the user has negative emotions, the content of the feedback can be changed to something more positive. The emotion estimation function also builds a system for analyzing the emotional response of the user receiving the evaluation and adjusting the content of the feedback. For example, it changes the tone and content of the feedback depending on the user's emotional state. The emotion estimation function also develops an algorithm for analyzing the emotional response of the user receiving the evaluation and adjusting the content of the feedback. For example, it dynamically changes the content of the feedback based on the user's emotional state. This makes it possible to analyze the emotional response of the user receiving the evaluation and adjust the content of the feedback.

[0064] Generative AI can learn from past successes and failures to discover talent with greater accuracy. For example, generative AI can learn from past successes and failures and use that knowledge when discovering users' talents. For example, it can identify the characteristics of talents that are likely to be successful based on past data. Furthermore, when discovering users' talents, generative AI can analyze past successes and failures to build a system that discovers talent with greater accuracy. For example, it can extract commonalities between successes and use them in talent discovery. Furthermore, generative AI can learn from past successes and failures and develop algorithms that utilize that knowledge when discovering users' talents. For example, it can learn patterns from successes and failures and apply that knowledge to talent discovery. This allows it to learn from past successes and failures to discover talent with greater accuracy.

[0065] The generative AI can analyze user data and provide an individually customized training plan. The generative AI, for example, analyzes user data and provides an individually customized training plan. For example, it identifies the user's weaknesses and suggests a training plan that addresses them. The generative AI also builds a system that provides individually customized training plans to improve user performance. For example, it creates a training plan based on the user's goals. The generative AI also analyzes user data and develops an algorithm that provides individually customized training plans. For example, it adjusts the training plan according to the user's progress. This makes it possible to analyze user data and provide an individually customized training plan.

[0066] The generative AI can analyze a user's emotional state and provide emotion-based advice to improve performance. For example, the generative AI uses an emotion estimation function to analyze a user's emotional state and provide emotion-based advice to improve performance. For example, if a user is feeling stressed, it provides advice to relax. The generative AI also builds a system to analyze a user's emotional state and provide emotion-based advice to improve performance. For example, if a user is feeling positive, it provides advice to maintain that emotion. The generative AI also uses the emotion estimation function to develop an algorithm that analyzes a user's emotional state and provides emotion-based advice to improve performance. For example, it adjusts a training plan according to the user's emotional state. This makes it possible to analyze a user's emotional state and provide emotion-based advice to improve performance.

[0067] Generative AI can combine talents from different fields to explore the possibility of new talents. For example, generative AI can analyze a user's essays and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine writing ability and musical expressiveness. Generative AI can also analyze a user's paintings and programming code to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine visual creativity and logical thinking ability. Generative AI can also analyze a user's presentations and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine presentation composition skills and musical expressiveness. This makes it possible to explore the possibility of new talents by combining talents from different fields.

[0068] Generative AI can provide functions that promote group training and collaboration to improve user performance. For example, generative AI analyzes user data and provides functions that promote group training and collaboration. For example, it groups users with the same goals and trains them together. Generative AI also builds a system that provides functions that promote group training and collaboration to improve user performance. For example, it provides a platform where users can train together. Generative AI also analyzes user data and develops algorithms that provide functions that promote group training and collaboration. For example, it forms optimal groups based on users' skills and goals. This can help improve user performance by promoting group training and collaboration.

[0069] The generative AI can analyze a user's emotional responses and suggest measures to maintain motivation to improve performance. For example, using an emotion estimation function, the generative AI can analyze a user's emotional responses and suggest measures to maintain motivation to improve performance. For example, if a user is losing motivation, it can provide an encouraging message. The generative AI can also build a system that analyzes a user's emotional responses and suggest measures to maintain motivation to improve performance. For example, if a user is feeling positive, it can provide advice on how to maintain that emotion. The generative AI can also use the emotion estimation function to analyze a user's emotional responses and develop an algorithm that suggests measures to maintain motivation to improve performance. For example, it can dynamically adjust motivation maintenance measures according to the user's emotional state. This can support performance improvement by analyzing a user's emotional responses and suggesting measures to maintain motivation.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] Generative AI can analyze user data and provide individually customized training plans. For example, it can identify the user's weaknesses and suggest training plans that address them. Generative AI can also build a system that provides individually customized training plans to improve user performance. For example, it can create training plans based on the user's goals. Generative AI can also analyze user data and develop algorithms that provide individually customized training plans. For example, it can adjust training plans according to the user's progress. This makes it possible to analyze user data and provide individually customized training plans.

[0072] The evaluation unit can suggest optimal conditions for demonstrating talent based on the user's lifestyle and environmental factors. For example, when the generation AI evaluates a user's essay, it takes into account the user's lifestyle and environmental factors and suggests optimal conditions for demonstrating talent. For example, it can suggest the optimal writing time and environment. Furthermore, when the user gives a presentation, the evaluation unit can suggest optimal conditions for demonstrating talent by taking into account lifestyle and environmental factors. For example, it can suggest the optimal practice time and place. Furthermore, when the generation AI evaluates a user's musical performance, it can suggest optimal conditions for demonstrating talent by taking into account lifestyle and environmental factors. For example, it can suggest the optimal practice time and environment. In this way, it can suggest optimal conditions for demonstrating talent by taking into account the user's lifestyle and environmental factors.

[0073] The evaluation unit uses the emotion estimation function to analyze the emotional state of the user when demonstrating their talent and evaluate the impact of positive emotions on the talent. For example, when the generation AI evaluates a user's essay, it uses the emotion estimation function to analyze the user's emotional state. For example, it analyzes the emotional state when writing the essay and evaluates the impact of positive emotions on the quality of the writing. In addition, when the user gives a presentation, the evaluation unit uses the emotion estimation function to analyze the emotional state and evaluate the impact of positive emotions on the presentation performance. For example, it analyzes the level of nervousness and confidence. In addition, when the generation AI evaluates the user's musical performance, it uses the emotion estimation function to analyze the emotional state and evaluate the impact of positive emotions on the quality of the performance. For example, it evaluates the impact of increased emotions during a performance on technique and expressiveness. In this way, the user's emotional state can be analyzed and the impact of positive emotions on talent can be evaluated.

[0074] The evaluation unit can simultaneously evaluate talents in different fields. For example, the generation AI can simultaneously evaluate a user's essays and musical performances to comprehensively evaluate talents in different fields. For example, it can simultaneously evaluate writing ability and musical expressiveness to evaluate the user's versatility. The evaluation unit can also simultaneously evaluate a painting and programming code provided by the user to comprehensively evaluate talents in different fields. For example, it can simultaneously evaluate visual creativity and logical thinking ability. The evaluation unit can also simultaneously evaluate a user's presentation and musical performances to comprehensively evaluate talents in different fields. For example, it can simultaneously evaluate presentation composition ability and musical expressiveness. This allows the generation AI to simultaneously evaluate a user's talents in different fields to evaluate their versatility.

[0075] The evaluation unit can use the emotion estimation function to analyze the emotional reactions of a user when their talent is evaluated, thereby improving the evaluation process. For example, when the generation AI evaluates a user's essay, the emotion estimation function can be used to analyze the user's emotional reactions, thereby improving the evaluation process. For example, the user's emotional reactions to the evaluation results can be analyzed and the content of the feedback can be adjusted. In addition, when the user gives a presentation, the generation AI can use the emotion estimation function to analyze the emotional reactions, thereby improving the evaluation process. For example, the user's emotional reactions to the evaluation results can be analyzed and the content of the feedback can be adjusted. In addition, when the generation AI evaluates a user's musical performance, the evaluation unit can use the emotion estimation function to analyze the emotional reactions, thereby improving the evaluation process. For example, the user's emotional reactions to the evaluation results can be analyzed and the content of the feedback can be adjusted. In this way, the user's emotional reactions can be analyzed and the evaluation process can be improved.

[0076] Generative AI can analyze user data and provide individually customized training plans. For example, it can identify the user's weaknesses and suggest training plans that address them. Generative AI can also build a system that provides individually customized training plans to improve user performance. For example, it can create training plans based on the user's goals. Generative AI can also analyze user data and develop algorithms that provide individually customized training plans. For example, it can adjust training plans according to the user's progress. This makes it possible to analyze user data and provide individually customized training plans.

[0077] The generative AI can analyze a user's emotional state and provide emotion-based performance improvement advice. For example, using an emotion estimation function, the system can analyze a user's emotional state and provide emotion-based performance improvement advice. For example, if a user is feeling stressed, the system can provide advice to relax. The generative AI can also build a system that analyzes a user's emotional state and provides emotion-based performance improvement advice. For example, if a user is feeling positive, the system can provide advice to maintain that emotion. The generative AI can also use the emotion estimation function to develop an algorithm that analyzes a user's emotional state and provides emotion-based performance improvement advice. For example, the system can adjust a training plan according to the user's emotional state. This allows the system to analyze a user's emotional state and provide emotion-based performance improvement advice.

[0078] Generative AI can combine talents from different fields to explore the possibility of new talents. For example, it can analyze a user's essays and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine writing ability and musical expressiveness. Generative AI can also analyze a user's paintings and programming code to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine visual creativity and logical thinking ability. Generative AI can also analyze a user's presentations and musical performances to explore the possibility of new talents by combining talents from different fields. For example, it can discover new talents that combine presentation composition skills and musical expressiveness. This makes it possible to explore the possibility of new talents by combining talents from different fields.

[0079] Generative AI can provide functions that promote group training and collaboration to improve user performance. For example, it analyzes user data and provides functions that promote group training and collaboration. For example, it groups users with the same goals and trains them together. Generative AI can also build systems that provide functions that promote group training and collaboration to improve user performance. For example, it can provide a platform where users can train together. Generative AI can also analyze user data and develop algorithms that provide functions that promote group training and collaboration. For example, it can form optimal groups based on users' skills and goals. This can help improve user performance by promoting group training and collaboration.

[0080] The generative AI can analyze a user's emotional responses and suggest measures to maintain motivation to improve performance. For example, it uses an emotion estimation function to analyze a user's emotional responses and suggest measures to maintain motivation to improve performance. For example, if a user is losing motivation, it can provide an encouraging message. The generative AI can also build a system to analyze a user's emotional responses and suggest measures to maintain motivation to improve performance. For example, if a user is feeling positive, it can provide advice on how to maintain that emotion. The generative AI can also use the emotion estimation function to analyze a user's emotional responses and develop an algorithm to suggest measures to maintain motivation to improve performance. For example, it can dynamically adjust motivation maintenance measures depending on the user's emotional state. This makes it possible to support performance improvement by analyzing a user's emotional responses and suggesting measures to maintain motivation.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The generative AI analyzes the data provided by the user. For example, the generative AI analyzes an essay written by the user and evaluates its content, structure, grammar, etc. The generative AI can also analyze audio data provided by the user and evaluate pronunciation and intonation. The generative AI can also analyze video data provided by the user and evaluate facial expressions and gestures. Step 2: The evaluation unit evaluates the user's talent based on the data analyzed by the generative AI. For example, the evaluation unit may evaluate the user's writing ability based on the content, structure, and grammar of the essay. The evaluation unit may also evaluate the user's pronunciation and intonation based on audio data. The evaluation unit may also evaluate the user's facial expressions and gestures based on video data. Step 3: The feedback unit provides the user with the evaluation results from the evaluation unit. For example, the feedback unit may provide the user with the evaluation results of the essay as text feedback. The feedback unit may also provide the user with the evaluation results of the audio data as audio feedback. The feedback unit may also provide the user with the evaluation results of the video data as graphical feedback.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0127] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. Equipped with generative AI, The generated AI is Analyze the data you provide; an evaluation unit that evaluates the talent of the user based on the data; a feedback unit that provides a user with the results evaluated by the evaluation unit. A system characterized by:

2. The evaluation unit Study the user's past data and analyze their long-term talent growth patterns.

2. The system of claim 1.

3. The evaluation unit Multifaceted evaluation using at least one different evaluation criterion from creativity, logic, and expressiveness 2. The system of claim 1.

4. The evaluation unit Analyzing the emotional state of the user when he / she demonstrates his / her talent and evaluating the influence of positive emotions on the talent.

2. The system of claim 1.

5. The evaluation unit Evaluating talent in different fields simultaneously 2. The system of claim 1.

6. The evaluation unit Based on the user's lifestyle and environmental factors, it suggests optimal conditions for talent development.

2. The system of claim 1.

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