System

Abstract

An object of a system according to an embodiment is to efficiently sell and buy a script created by an individual and distribute a profit for each use.SOLUTION: A system includes a script analysis part, a fee setting part, a profit sharing part, and a question-and-answer part. The script analysis unit analyzes the contents of the script. The fee setting unit sets a usage fee based on the script analyzed by the script analysis unit. The profit distribution unit calculates the cost every time the user uses the script based on the fee set by the fee setting unit, and distributes the profit to the creator. The question-and-answer unit allows the user to freely repeat questions and answers according to the generation AI.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 had the problem of not providing a sufficient system for efficiently buying and selling scripts created by individuals and distributing revenue for each use.

[0005] The system according to the embodiment aims to efficiently buy and sell scripts created by individuals and distribute profits for each use. [Means for solving the problem]

[0006] The system according to the embodiment includes a script analysis unit, a fee setting unit, a revenue distribution unit, and a question and answer unit. The script analysis unit analyzes the contents of the script. The fee setting unit sets a usage fee based on the script analyzed by the script analysis unit. The revenue distribution unit calculates the cost each time a user uses a script based on the fee set by the fee setting unit, and distributes revenue to the creator. The question and answer unit allows the user to freely repeat question and answer sessions using a generation AI. [Effects of the Invention]

[0007] The system according to the embodiment allows scripts created by individuals to be bought and sold efficiently, and revenues can be distributed for each use. [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 script trading platform according to an embodiment of the present invention allows individuals to trade scripts created in a standard format. This platform utilizes a generation AI to manage script usage, allowing script creators to set the price per use. Each time a user uses a script, a fee is incurred, and revenue is distributed to the creator. Furthermore, the generation AI allows users to freely continue to ask and answer questions afterward, rather than simply providing a single answer. This allows the script trading platform to efficiently trade scripts created by individuals, thereby increasing revenue for script creators and improving user convenience.

[0029] A script trading platform according to an embodiment includes a script analysis unit, a fee setting unit, a revenue distribution unit, and a question and answer unit. The script analysis unit analyzes the contents of a script. For example, a generation AI analyzes the contents of the script and converts them into a standard format. The script analysis unit can also suggest improvements to the efficiency and optimization of the script. The fee setting unit sets a usage fee based on the script analyzed by the script analysis unit. For example, the fee may be set based on the number of times the script is used and the duration of use. The revenue distribution unit calculates the cost each time a user uses a script based on the fee set by the fee setting unit and distributes revenue to the creator. For example, revenue is returned to the creator each time a user uses a script. The question and answer unit allows users to freely engage in repeated question and answer sessions using the generation AI. For example, the generation AI analyzes questions from users and generates appropriate answers. This allows the script trading platform to efficiently manage script trading and increase creator revenue.

[0030] The script analysis unit can make suggestions for improving script efficiency and optimization. In this case, for example, the generation AI analyzes the contents of the script and evaluates its efficiency. For example, it detects redundant code and suggests replacing it with more concise code. In addition, in order to suggest script optimization, the generation AI analyzes execution speed and memory usage and suggests areas for improvement. For example, it suggests optimizing loops or deleting unnecessary variables. In addition, the script analysis unit analyzes the contents of the script, detects security vulnerabilities, and suggests fixes. For example, it suggests code fixes to reduce the risk of SQL injection. In this way, by suggesting script efficiency and optimization, it is possible to improve user convenience.

[0031] The script analysis unit can automatically convert scripts between different programming languages. In the script analysis unit, for example, a generation AI analyzes a script and automatically converts it into a different programming language. For example, it converts a Python script into JavaScript. In addition, when formatting a script, the generation AI takes into account differences in syntax and libraries between languages ​​and performs appropriate conversion. For example, it replaces a Python library with a corresponding JavaScript library. In addition, the script analysis unit uses a generation AI to analyze the contents of the script and propose the optimal conversion method between different languages. For example, it suggests how to efficiently implement a specific algorithm in a different language. This makes it possible to improve user convenience by automatically converting between different programming languages.

[0032] The script analysis unit can also apply standardized script formatting to fields other than programming. For example, the script analysis unit formats automation scripts for business processes so that they can be bought and sold on the platform. For example, it provides an automation script for accounting work. The script analysis unit also formats scripts for educational materials and shares them on the platform. For example, it provides sample code for learning programming. The script analysis unit also applies standardized script formatting to fields other than business processes and educational materials. For example, it provides scripts for data analysis and marketing automation. In this way, standardized script formatting can be applied to fields other than programming, thereby improving user convenience.

[0033] The script analysis unit can provide a collaborative script editing function, allowing multiple users to improve scripts in real time. The script analysis unit, for example, provides a collaborative script editing function on the platform, allowing multiple users to edit scripts simultaneously. For example, code changes are reflected in real time. The script analysis unit also uses the collaborative editing function to enable multiple users to cooperate in improving scripts and fixing bugs. For example, it supports script development in team projects. The script analysis unit also provides a collaborative script editing function on the platform, promoting the exchange of opinions and feedback between users. For example, a comment function or chat function is added. This allows multiple users to improve scripts in real time, thereby improving the quality of scripts and enabling more efficient development.

[0034] The fee setting unit can monitor the usage status of the script in real time and dynamically adjust the fee according to the user's satisfaction. For example, the generation AI monitors the usage status of the script in real time and dynamically adjusts the fee according to the user's satisfaction. For example, the fee is increased if the user's rating is high. The fee setting unit also builds a system that dynamically adjusts the fee of the script based on the user's satisfaction. For example, the fee is adjusted by analyzing user feedback. The fee setting unit also monitors the usage status of the script using the generation AI and dynamically adjusts the fee according to the user's satisfaction. For example, the fee is decreased if the user's rating is low. In this way, user satisfaction can be improved by dynamically adjusting the fee according to the user's satisfaction.

[0035] The fee setting unit can customize the script usage fee based on the user profile. The fee setting unit, for example, builds a system that customizes the script usage fee based on the user profile. For example, it provides discounts to users with high levels of specialized knowledge. The fee setting unit also customizes the script fee according to the user's purpose of use. For example, it sets a special fee for use for educational purposes. The fee setting unit also develops a system that analyzes user profile data and sets optimal fees. For example, it adjusts fees based on the user's past usage history. In this way, customizing fees based on the user profile can improve user satisfaction.

[0036] The fee setting unit can set the usage fees for the scripts to accommodate different business models, such as a subscription model or bundled sales. For example, the fee setting unit can set the usage fees to accommodate a subscription model, allowing scripts to be used for a flat rate. For example, multiple scripts can be used for a monthly fee. The fee setting unit can also introduce a bundled sales model to sell multiple scripts as a set. For example, scripts related to a specific task can be provided together. The fee setting unit can also build a system that sets the usage fees to accommodate different business models. For example, a pay-as-you-go model based on the number of uses can be introduced. This allows for compatibility with different business models, expanding user options and maximizing revenue.

[0037] The fee setting unit can introduce a mechanism for distributing rewards to users who contribute to improving and updating the script when distributing revenue. The fee setting unit, for example, introduces a mechanism for distributing rewards to users who contribute to improving and updating the script. For example, it provides rewards to users who fix bugs or add features. The fee setting unit also constructs a system for evaluating users who contribute to improving the script when distributing revenue and distributing rewards. For example, it pays rewards to users whose improvement suggestions are adopted. The fee setting unit also introduces a mechanism for distributing rewards to users who contribute to updating the script. For example, it provides rewards to users who perform regular maintenance or version upgrades. In this way, by distributing rewards to users who contribute to improving and updating the script, it is possible to improve user motivation.

[0038] The question and answer section can add a function to analyze the question and answer history and evaluate the user's learning progress and level of understanding. The question and answer section, for example, analyzes the question and answer history and builds a system to evaluate the user's learning progress. For example, the learning progress is evaluated based on past question content and answer history. The question and answer section also analyzes the question and answer history to evaluate the user's level of understanding. For example, the level of understanding is evaluated based on the correct answer rate and the difficulty of the questions. The question and answer section also adds a function to evaluate the user's learning progress and level of understanding based on the question and answer history. For example, the learning progress status is displayed in a graph. This makes it possible to improve learning effectiveness by evaluating the user's learning progress and level of understanding.

[0039] The question and answer section can provide a user with a customized study plan or additional resources based on the content of the question and answer. The question and answer section, for example, analyzes the content of the question and answer and builds a system that provides a user with a customized study plan. For example, the study plan is adjusted based on the content of the user's question. The question and answer section also provides a user with additional resources based on the history of the question and answer. For example, related teaching materials and reference materials are recommended. The question and answer section also analyzes the content of the question and answer and develops a system that provides a user with a customized study plan or additional resources. For example, the study plan is adjusted according to the user's level of understanding. This makes it possible to improve learning effectiveness by providing a user with a customized study plan or additional resources.

[0040] The question and answer section can also apply the question and answer function to other platforms. For example, the question and answer section applies the question and answer function to an online education platform to provide an environment where students can freely ask questions. For example, questions can be accepted in real time during class. The question and answer section can also apply the question and answer function to a customer support platform to enable customers to freely ask questions about products and services. For example, questions can be answered using a chatbot. The question and answer section can also apply the question and answer function to other platforms to provide an environment where users can freely ask questions. For example, it can support question and answer sessions on forums and community sites. In this way, the question and answer function can be applied to other platforms to improve user convenience.

[0041] The question and answer section provides a community function that allows users to help each other based on the content of the question and answer, thereby promoting the sharing of knowledge. The question and answer section, for example, builds a system that provides a community function that allows users to help each other based on the content of the question and answer. For example, it allows other users to provide answers to questions. The question and answer section also uses the community function to provide an environment in which users can share knowledge with each other. For example, it rates answers to questions and highlights good answers. The question and answer section also develops a system that provides a community function that allows users to help each other based on the content of the question and answer, thereby promoting the sharing of knowledge. For example, other users complete answers to questions. In this way, by providing a community function that allows users to help each other, it is possible to promote the sharing of knowledge and improve user satisfaction.

[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] The script analysis unit analyzes the contents of the script. For example, a generation AI analyzes the contents of the script and converts it into a standard format. The script analysis unit can also make suggestions for improving the efficiency and optimization of the script. The pricing unit sets usage fees based on the script analyzed by the script analysis unit. For example, the fee may be set based on the number of times the script is used and the duration of use. The revenue distribution unit calculates the cost each time a user uses a script based on the fee set by the pricing unit and distributes revenue to the creator. For example, revenue is returned to the creator each time a user uses a script. The question and answer unit allows users to freely repeat question and answer sessions using the generation AI. For example, the generation AI analyzes questions from users and generates appropriate answers. This allows the script trading platform to efficiently manage script trading and increase creators' revenue.

[0044] In the script analysis unit, for example, the generation AI analyzes the contents of the script and evaluates its efficiency. For example, it detects redundant code and suggests replacing it with more concise code. In addition, in order to suggest script optimization, the generation AI analyzes execution speed and memory usage and suggests areas for improvement. For example, it suggests optimizing loops or deleting unnecessary variables. In addition, in the script analysis unit, the generation AI analyzes the contents of the script, detects security vulnerabilities, and suggests fixes. For example, it suggests code fixes to reduce the risk of SQL injection. This makes it possible to improve user convenience by suggesting script efficiency and optimization.

[0045] In the script analysis unit, for example, the generation AI analyzes the script and automatically converts it into a different programming language. For example, it converts a Python script into JavaScript. In addition, when formatting the script, the generation AI takes into account differences in syntax and libraries between languages ​​and performs appropriate conversion. For example, it replaces a Python library with a corresponding JavaScript library. In addition, the generation AI analyzes the contents of the script and proposes the optimal conversion method between different languages. For example, it suggests how to efficiently implement a specific algorithm in a different language. This improves user convenience by automatically converting between different programming languages.

[0046] The script analysis unit, for example, formats automation scripts for business processes so that they can be bought and sold on the platform. For example, it provides an automation script for accounting work. The script analysis unit also formats scripts for educational materials and shares them on the platform. For example, it provides sample code for programming learning. The script analysis unit also applies standardized script formatting to fields other than business processes and educational materials. For example, it provides scripts for data analysis and marketing automation. This allows standardized script formatting to be applied to fields other than programming, thereby improving user convenience.

[0047] The script analysis unit, for example, provides a collaborative script editing function on the platform, allowing multiple users to edit scripts simultaneously. For example, it reflects code changes in real time. The script analysis unit also uses the collaborative editing function to allow multiple users to work together to improve scripts and fix bugs. For example, it supports script development in team projects. The script analysis unit also provides a collaborative script editing function on the platform, promoting the exchange of opinions and feedback between users. For example, it adds a comment function and a chat function. This allows multiple users to improve scripts in real time, improving the quality of scripts and enabling more efficient development.

[0048] The pricing unit, for example, has the generation AI monitor the usage of the script in real time and dynamically adjust the fee according to the user's satisfaction. For example, the fee may be increased if the user's rating is high. The pricing unit also builds a system that dynamically adjusts the fee for the script based on the user's satisfaction. For example, it analyzes user feedback and adjusts the fee. The pricing unit also has the generation AI monitor the usage of the script and dynamically adjusts the fee according to the user's satisfaction. For example, it may lower the fee if the user's rating is low. In this way, user satisfaction can be improved by dynamically adjusting the fee according to the user's satisfaction.

[0049] The fee setting unit, for example, builds a system that customizes the usage fee for scripts based on the user's profile. For example, it provides discounts to users with high levels of specialized knowledge. The fee setting unit also customizes the fee for scripts according to the user's purpose of use. For example, it sets a special fee for use for educational purposes. The fee setting unit also develops a system that analyzes user profile data and sets optimal fees. For example, it adjusts fees based on the user's past usage history. In this way, user satisfaction can be improved by customizing fees based on the user's profile.

[0050] The pricing unit may, for example, set usage fees to a subscription model, allowing scripts to be used for a flat fee. For example, multiple scripts may be available for a monthly fee. The pricing unit may also introduce a bundled sales model, selling multiple scripts as a set. For example, scripts related to a specific task may be provided together. The pricing unit may also build a system that sets usage fees to accommodate different business models. For example, a pay-as-you-go model based on the number of uses may be introduced. This allows for compatibility with different business models, expanding user options and maximizing revenue.

[0051] The fee setting unit, for example, introduces a mechanism for distributing rewards to users who contribute to improving and updating the script. For example, it provides rewards to users who fix bugs or add features. The fee setting unit also constructs a system for evaluating users who contribute to improving the script and distributing rewards when distributing revenue. For example, it pays rewards to users whose improvement suggestions are adopted. The fee setting unit also introduces a mechanism for distributing rewards to users who contribute to updating the script. For example, it provides rewards to users who perform regular maintenance or version upgrades. In this way, by distributing rewards to users who contribute to improving and updating the script, it is possible to improve user motivation.

[0052] The question and answer section, for example, analyzes the question and answer history to build a system that evaluates the user's learning progress. For example, the learning progress is evaluated based on the content of past questions and the answer history. The question and answer section also analyzes the question and answer history to evaluate the user's level of understanding. For example, the level of understanding is evaluated based on the correct answer rate and the difficulty of the questions. The question and answer section also adds a function to evaluate the user's learning progress and level of understanding based on the question and answer history. For example, the learning progress status is displayed in a graph. This makes it possible to improve the learning effect by evaluating the user's learning progress and level of understanding.

[0053] The Q&A department, for example, analyzes the content of the Q&A session and builds a system that provides a user with a customized study plan. For example, the study plan is adjusted based on the content of the user's question. The Q&A department also provides the user with additional resources based on the Q&A history. For example, related teaching materials and reference materials are recommended. The Q&A department also analyzes the content of the Q&A session and develops a system that provides the user with a customized study plan and additional resources. For example, the study plan is adjusted according to the user's level of understanding. This allows the user to be provided with a customized study plan and additional resources, thereby improving learning effectiveness.

[0054] For example, the Q&A department applies the Q&A function to an online education platform to provide an environment where students can freely ask questions. For example, questions can be accepted in real time during class. The Q&A department also applies the Q&A function to a customer support platform to allow customers to freely ask questions about products and services. For example, questions can be answered using a chatbot. The Q&A department also applies the Q&A function to other platforms to provide an environment where users can freely ask questions. For example, it supports Q&A on forums and community sites. In this way, the Q&A function can be applied to other platforms to improve user convenience.

[0055] The question and answer section, for example, builds a system that provides a community function where users can help each other based on the content of the question and answer session. For example, it allows other users to provide answers to questions. The question and answer section also uses the community function to provide an environment where users can share knowledge with each other. For example, it evaluates answers to questions and highlights good answers. The question and answer section also develops a system that provides a community function where users can help each other based on the content of the question and answer session, and promotes knowledge sharing. For example, other users complete answers to questions. In this way, by providing a community function where users can help each other, it is possible to promote knowledge sharing and improve user satisfaction.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The script analysis unit analyzes the contents of the script. For example, a generation AI analyzes the contents of the script and converts them into a standard format. The script analysis unit can also make suggestions for improving the efficiency and optimization of the script. Step 2: The fee setting unit sets a usage fee based on the script analyzed by the script analysis unit, for example, depending on the number of times the script is used or the duration of use. Step 3: The revenue distribution unit calculates the cost each time a user uses the script based on the fee set by the fee setting unit, and distributes the revenue to the creator. For example, revenue is returned to the creator each time a user uses the script. Step 4: In the Q&A section, the user can freely repeat the Q&A process using the generation AI. For example, the generation AI analyzes the user's question and generates an appropriate answer.

[0058] (Example 2) A script trading platform according to an embodiment of the present invention allows individuals to trade scripts created in a standard format. This platform utilizes a generation AI to manage script usage, allowing script creators to set the price per use. Each time a user uses a script, a fee is incurred, and revenue is distributed to the creator. Furthermore, the generation AI allows users to freely continue to ask and answer questions afterward, rather than simply providing a single answer. This allows the script trading platform to efficiently trade scripts created by individuals, thereby increasing revenue for script creators and improving user convenience.

[0059] A script trading platform according to an embodiment includes a script analysis unit, a fee setting unit, a revenue distribution unit, and a question and answer unit. The script analysis unit analyzes the contents of a script. For example, a generation AI analyzes the contents of the script and converts them into a standard format. The script analysis unit can also suggest improvements to the efficiency and optimization of the script. The fee setting unit sets a usage fee based on the script analyzed by the script analysis unit. For example, the fee may be set based on the number of times the script is used and the duration of use. The revenue distribution unit calculates the cost each time a user uses a script based on the fee set by the fee setting unit and distributes revenue to the creator. For example, revenue is returned to the creator each time a user uses a script. The question and answer unit allows users to freely engage in repeated question and answer sessions using the generation AI. For example, the generation AI analyzes questions from users and generates appropriate answers. This allows the script trading platform to efficiently manage script trading and increase creator revenue.

[0060] The script analysis unit can make suggestions for improving script efficiency and optimization. In this case, for example, the generation AI analyzes the contents of the script and evaluates its efficiency. For example, it detects redundant code and suggests replacing it with more concise code. In addition, in order to suggest script optimization, the generation AI analyzes execution speed and memory usage and suggests areas for improvement. For example, it suggests optimizing loops or deleting unnecessary variables. In addition, the script analysis unit analyzes the contents of the script, detects security vulnerabilities, and suggests fixes. For example, it suggests code fixes to reduce the risk of SQL injection. In this way, by suggesting script efficiency and optimization, it is possible to improve user convenience.

[0061] The script analysis unit can automatically convert scripts between different programming languages. In the script analysis unit, for example, a generation AI analyzes a script and automatically converts it into a different programming language. For example, it converts a Python script into JavaScript. In addition, when formatting a script, the generation AI takes into account differences in syntax and libraries between languages ​​and performs appropriate conversion. For example, it replaces a Python library with a corresponding JavaScript library. In addition, the script analysis unit uses a generation AI to analyze the contents of the script and propose the optimal conversion method between different languages. For example, it suggests how to efficiently implement a specific algorithm in a different language. This makes it possible to improve user convenience by automatically converting between different programming languages.

[0062] The script analysis unit can use the emotion estimation function to infer the intent and purpose of the script creator and generate an optimal format based on that. The script analysis unit, for example, uses the emotion estimation function to analyze the intent of the script creator and generate an optimal format. For example, if the creator places importance on efficiency, the script analysis unit suggests an efficient code format. The script analysis unit also analyzes the intent of the script creator using the emotion estimation function and adjusts the format based on that. For example, if the script is for educational purposes, it adds easy-to-understand comments and explanations. The script analysis unit also uses the emotion estimation function to infer the intent of the script creator and generate an optimal format. For example, it strengthens error handling to make debugging easier. This makes it possible to improve the quality of scripts by generating an optimal format based on the intent and purpose of the script creator.

[0063] The script analysis unit can also apply standardized script formatting to fields other than programming. For example, the script analysis unit formats automation scripts for business processes so that they can be bought and sold on the platform. For example, it provides an automation script for accounting work. The script analysis unit also formats scripts for educational materials and shares them on the platform. For example, it provides sample code for learning programming. The script analysis unit also applies standardized script formatting to fields other than business processes and educational materials. For example, it provides scripts for data analysis and marketing automation. In this way, standardized script formatting can be applied to fields other than programming, thereby improving user convenience.

[0064] The script analysis unit can provide a collaborative script editing function, allowing multiple users to improve scripts in real time. The script analysis unit, for example, provides a collaborative script editing function on the platform, allowing multiple users to edit scripts simultaneously. For example, code changes are reflected in real time. The script analysis unit also uses the collaborative editing function to enable multiple users to cooperate in improving scripts and fixing bugs. For example, it supports script development in team projects. The script analysis unit also provides a collaborative script editing function on the platform, promoting the exchange of opinions and feedback between users. For example, a comment function or chat function is added. This allows multiple users to improve scripts in real time, thereby improving the quality of scripts and enabling more efficient development.

[0065] The script analysis unit can use the emotion estimation function to identify which parts of the script the user is having difficulty with and automatically make suggestions to improve those parts. The script analysis unit, for example, uses the emotion estimation function to identify which parts of the script the user is having difficulty with. For example, it analyzes error messages and debug information to identify problem areas. The script analysis unit also makes suggestions to automatically improve the parts of the script the user is having difficulty with, based on the user's emotion data. For example, it adds error handling or code comments. The script analysis unit also uses the emotion estimation function to identify which parts of the script the user is having difficulty with and makes suggestions to improve them. For example, it suggests optimizing the code based on user feedback. In this way, it is possible to improve user convenience by identifying which parts the user is having difficulty with and automatically making suggestions to improve them.

[0066] The fee setting unit can monitor the usage status of the script in real time and dynamically adjust the fee according to the user's satisfaction. For example, the generation AI monitors the usage status of the script in real time and dynamically adjusts the fee according to the user's satisfaction. For example, the fee is increased if the user's rating is high. The fee setting unit also builds a system that dynamically adjusts the fee of the script based on the user's satisfaction. For example, the fee is adjusted by analyzing user feedback. The fee setting unit also monitors the usage status of the script using the generation AI and dynamically adjusts the fee according to the user's satisfaction. For example, the fee is decreased if the user's rating is low. In this way, user satisfaction can be improved by dynamically adjusting the fee according to the user's satisfaction.

[0067] The fee setting unit can customize the script usage fee based on the user profile. The fee setting unit, for example, builds a system that customizes the script usage fee based on the user profile. For example, it provides discounts to users with high levels of specialized knowledge. The fee setting unit also customizes the script fee according to the user's purpose of use. For example, it sets a special fee for use for educational purposes. The fee setting unit also develops a system that analyzes user profile data and sets optimal fees. For example, it adjusts fees based on the user's past usage history. In this way, customizing fees based on the user profile can improve user satisfaction.

[0068] The fee setting unit can use the emotion estimation function to analyze users' emotional reactions and distribute bonus revenue to scripts with many positive reactions. The fee setting unit, for example, uses the emotion estimation function to analyze users' emotional reactions and build a system that distributes bonus revenue to scripts with many positive reactions. For example, it provides additional rewards to scripts with high emotional scores. The fee setting unit also evaluates scripts based on users' emotional data and distributes bonus revenue to scripts with many positive reactions. For example, it increases rewards for scripts with high user satisfaction. The fee setting unit also uses the emotion estimation function to monitor users' emotional reactions in real time and distribute bonus revenue to scripts with many positive reactions. For example, it adjusts rewards based on users' emotional scores. In this way, by distributing bonus revenue to scripts with many positive reactions, it is possible to improve the motivation of script creators.

[0069] The fee setting unit can set the usage fees for the scripts to accommodate different business models, such as a subscription model or bundled sales. For example, the fee setting unit can set the usage fees to accommodate a subscription model, allowing scripts to be used for a flat rate. For example, multiple scripts can be used for a monthly fee. The fee setting unit can also introduce a bundled sales model to sell multiple scripts as a set. For example, scripts related to a specific task can be provided together. The fee setting unit can also build a system that sets the usage fees to accommodate different business models. For example, a pay-as-you-go model based on the number of uses can be introduced. This allows for compatibility with different business models, expanding user options and maximizing revenue.

[0070] The fee setting unit can introduce a mechanism for distributing rewards to users who contribute to improving and updating the script when distributing revenue. The fee setting unit, for example, introduces a mechanism for distributing rewards to users who contribute to improving and updating the script. For example, it provides rewards to users who fix bugs or add features. The fee setting unit also constructs a system for evaluating users who contribute to improving the script when distributing revenue and distributing rewards. For example, it pays rewards to users whose improvement suggestions are adopted. The fee setting unit also introduces a mechanism for distributing rewards to users who contribute to updating the script. For example, it provides rewards to users who perform regular maintenance or version upgrades. In this way, by distributing rewards to users who contribute to improving and updating the script, it is possible to improve user motivation.

[0071] The fee setting unit can use the emotion estimation function to set additional fees for functions or parts that users find most valuable. For example, the fee setting unit uses the emotion estimation function to identify functions or parts that users find most valuable and builds a system for setting additional fees. For example, it sets a premium fee for a specific function. The fee setting unit also identifies the parts that users find most valuable based on user emotion data and sets additional fees. For example, it sets additional fees for functions that provide high user satisfaction. The fee setting unit also uses the emotion estimation function to develop a system for identifying the parts that users find most valuable and setting additional fees. For example, it adjusts fees based on the user's emotion score. This makes it possible to maximize revenue by setting additional fees for functions or parts that users find most valuable.

[0072] The question and answer section can add a function to analyze the question and answer history and evaluate the user's learning progress and level of understanding. The question and answer section, for example, analyzes the question and answer history and builds a system to evaluate the user's learning progress. For example, the learning progress is evaluated based on past question content and answer history. The question and answer section also analyzes the question and answer history to evaluate the user's level of understanding. For example, the level of understanding is evaluated based on the correct answer rate and the difficulty of the questions. The question and answer section also adds a function to evaluate the user's learning progress and level of understanding based on the question and answer history. For example, the learning progress status is displayed in a graph. This makes it possible to improve learning effectiveness by evaluating the user's learning progress and level of understanding.

[0073] The question and answer section can provide a user with a customized study plan or additional resources based on the content of the question and answer. The question and answer section, for example, analyzes the content of the question and answer and builds a system that provides a user with a customized study plan. For example, the study plan is adjusted based on the content of the user's question. The question and answer section also provides a user with additional resources based on the history of the question and answer. For example, related teaching materials and reference materials are recommended. The question and answer section also analyzes the content of the question and answer and develops a system that provides a user with a customized study plan or additional resources. For example, the study plan is adjusted according to the user's level of understanding. This makes it possible to improve learning effectiveness by providing a user with a customized study plan or additional resources.

[0074] The question and answer unit can use the emotion estimation function to analyze the user's emotional response to a question and generate a more friendly answer. For example, the question and answer unit uses the emotion estimation function to analyze the user's emotional response to a question and build a system that generates a friendly answer. For example, an answer that gives a sense of security when the user is feeling anxious is provided. The question and answer unit also analyzes the emotional response to a question based on the user's emotion data and generates a friendly answer. For example, an easy-to-understand explanation is provided when the user is confused. The question and answer unit also uses the emotion estimation function to develop a system that analyzes the user's emotional response to a question and generates a more friendly answer. For example, a positive answer that makes the user feel happy is provided. In this way, by analyzing the user's emotional response and generating a friendly answer, user satisfaction can be improved.

[0075] The question and answer section can also apply the question and answer function to other platforms. For example, the question and answer section applies the question and answer function to an online education platform to provide an environment where students can freely ask questions. For example, questions can be accepted in real time during class. The question and answer section can also apply the question and answer function to a customer support platform to enable customers to freely ask questions about products and services. For example, questions can be answered using a chatbot. The question and answer section can also apply the question and answer function to other platforms to provide an environment where users can freely ask questions. For example, it can support question and answer sessions on forums and community sites. In this way, the question and answer function can be applied to other platforms to improve user convenience.

[0076] The question and answer section provides a community function that allows users to help each other based on the content of the question and answer, thereby promoting the sharing of knowledge. The question and answer section, for example, builds a system that provides a community function that allows users to help each other based on the content of the question and answer. For example, it allows other users to provide answers to questions. The question and answer section also uses the community function to provide an environment in which users can share knowledge with each other. For example, it rates answers to questions and highlights good answers. The question and answer section also develops a system that provides a community function that allows users to help each other based on the content of the question and answer, thereby promoting the sharing of knowledge. For example, other users complete answers to questions. In this way, by providing a community function that allows users to help each other, it is possible to promote the sharing of knowledge and improve user satisfaction.

[0077] The question and answer unit can use the emotion estimation function to monitor the emotion of a user when asking a question in real time and provide support to reduce stress. For example, the question and answer unit uses the emotion estimation function to build a system that monitors the emotion of a user when asking a question in real time and provides support to reduce stress. For example, if the user is nervous, a message to help the user relax is displayed. The question and answer unit also monitors the emotion of a user when asking a question based on the user's emotion data and provides support to reduce stress. For example, if the user is feeling anxious, an answer that gives a sense of security is provided. The question and answer unit also uses the emotion estimation function to develop a system that monitors the emotion of a user when asking a question in real time and provides support to reduce stress. For example, if the user is confused, an easy-to-understand explanation is provided. In this way, the emotion of a user when asking a question can be monitored in real time and support to reduce stress can be provided, thereby improving user satisfaction.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The script analysis unit analyzes the contents of the script. For example, a generation AI analyzes the contents of the script and converts it into a standard format. The script analysis unit can also make suggestions for improving the efficiency and optimization of the script. The pricing unit sets usage fees based on the script analyzed by the script analysis unit. For example, the fee may be set based on the number of times the script is used and the duration of use. The revenue distribution unit calculates the cost each time a user uses a script based on the fee set by the pricing unit and distributes revenue to the creator. For example, revenue is returned to the creator each time a user uses a script. The question and answer unit allows users to freely repeat question and answer sessions using the generation AI. For example, the generation AI analyzes questions from users and generates appropriate answers. This allows the script trading platform to efficiently manage script trading and increase creators' revenue.

[0080] In the script analysis unit, for example, the generation AI analyzes the contents of the script and evaluates its efficiency. For example, it detects redundant code and suggests replacing it with more concise code. In addition, in order to suggest script optimization, the generation AI analyzes execution speed and memory usage and suggests areas for improvement. For example, it suggests optimizing loops or deleting unnecessary variables. In addition, in the script analysis unit, the generation AI analyzes the contents of the script, detects security vulnerabilities, and suggests fixes. For example, it suggests code fixes to reduce the risk of SQL injection. This makes it possible to improve user convenience by suggesting script efficiency and optimization.

[0081] In the script analysis unit, for example, the generation AI analyzes the script and automatically converts it into a different programming language. For example, it converts a Python script into JavaScript. In addition, when formatting the script, the generation AI takes into account differences in syntax and libraries between languages ​​and performs appropriate conversion. For example, it replaces a Python library with a corresponding JavaScript library. In addition, the generation AI analyzes the contents of the script and proposes the optimal conversion method between different languages. For example, it suggests how to efficiently implement a specific algorithm in a different language. This improves user convenience by automatically converting between different programming languages.

[0082] The script analysis unit uses, for example, an emotion estimation function to analyze the script creator's intentions and generate the optimal format. For example, if the creator places importance on efficiency, it will suggest an efficient code format. The script analysis unit also analyzes the script creator's purpose using the emotion estimation function and adjusts the format based on that. For example, if the script is for educational purposes, it will add easy-to-understand comments and explanations. The script analysis unit also uses the emotion estimation function to infer the script creator's intentions and generate the optimal format. For example, it will enhance error handling to make debugging easier. This makes it possible to improve the quality of scripts by generating the optimal format based on the script creator's intentions and goals.

[0083] The script analysis unit, for example, formats automation scripts for business processes so that they can be bought and sold on the platform. For example, it provides an automation script for accounting work. The script analysis unit also formats scripts for educational materials and shares them on the platform. For example, it provides sample code for programming learning. The script analysis unit also applies standardized script formatting to fields other than business processes and educational materials. For example, it provides scripts for data analysis and marketing automation. This allows standardized script formatting to be applied to fields other than programming, thereby improving user convenience.

[0084] The script analysis unit, for example, provides a collaborative script editing function on the platform, allowing multiple users to edit scripts simultaneously. For example, it reflects code changes in real time. The script analysis unit also uses the collaborative editing function to allow multiple users to work together to improve scripts and fix bugs. For example, it supports script development in team projects. The script analysis unit also provides a collaborative script editing function on the platform, promoting the exchange of opinions and feedback between users. For example, it adds a comment function and a chat function. This allows multiple users to improve scripts in real time, improving the quality of scripts and enabling more efficient development.

[0085] The script analysis unit, for example, uses an emotion estimation function to identify parts of a script that the user is having difficulty with. For example, it analyzes error messages and debug information to identify problem areas. The script analysis unit also makes suggestions to automatically improve the parts that the user is having difficulty with based on the user's emotion data. For example, it adds error handling or code comments. The script analysis unit also uses the emotion estimation function to identify parts of a script that the user is having difficulty with and makes suggestions to improve them. For example, it suggests optimizing the code based on user feedback. In this way, it is possible to improve user convenience by identifying parts that the user is having difficulty with and automatically making suggestions to improve them.

[0086] The pricing unit, for example, has the generation AI monitor the usage of the script in real time and dynamically adjust the fee according to the user's satisfaction. For example, the fee may be increased if the user's rating is high. The pricing unit also builds a system that dynamically adjusts the fee for the script based on the user's satisfaction. For example, it analyzes user feedback and adjusts the fee. The pricing unit also has the generation AI monitor the usage of the script and dynamically adjusts the fee according to the user's satisfaction. For example, it may lower the fee if the user's rating is low. In this way, user satisfaction can be improved by dynamically adjusting the fee according to the user's satisfaction.

[0087] The fee setting unit, for example, builds a system that customizes the usage fee for scripts based on the user's profile. For example, it provides discounts to users with high levels of specialized knowledge. The fee setting unit also customizes the fee for scripts according to the user's purpose of use. For example, it sets a special fee for use for educational purposes. The fee setting unit also develops a system that analyzes user profile data and sets optimal fees. For example, it adjusts fees based on the user's past usage history. In this way, user satisfaction can be improved by customizing fees based on the user's profile.

[0088] The fee setting unit, for example, uses an emotion estimation function to analyze users' emotional reactions and build a system that distributes bonus revenue to scripts that receive many positive reactions. For example, it provides additional rewards to scripts with high emotional scores. The fee setting unit also evaluates scripts based on users' emotional data and distributes bonus revenue to scripts that receive many positive reactions. For example, it increases rewards for scripts that receive high user satisfaction. The fee setting unit also uses the emotion estimation function to monitor users' emotional reactions in real time and distributes bonus revenue to scripts that receive many positive reactions. For example, it adjusts rewards based on users' emotional scores. In this way, by distributing bonus revenue to scripts that receive many positive reactions, it is possible to improve the motivation of script creators.

[0089] The pricing unit may, for example, set usage fees to a subscription model, allowing scripts to be used for a flat fee. For example, multiple scripts may be available for a monthly fee. The pricing unit may also introduce a bundled sales model, selling multiple scripts as a set. For example, scripts related to a specific task may be provided together. The pricing unit may also build a system that sets usage fees to accommodate different business models. For example, a pay-as-you-go model based on the number of uses may be introduced. This allows for compatibility with different business models, expanding user options and maximizing revenue.

[0090] The fee setting unit, for example, introduces a mechanism for distributing rewards to users who contribute to improving and updating the script. For example, it provides rewards to users who fix bugs or add features. The fee setting unit also constructs a system for evaluating users who contribute to improving the script and distributing rewards when distributing revenue. For example, it pays rewards to users whose improvement suggestions are adopted. The fee setting unit also introduces a mechanism for distributing rewards to users who contribute to updating the script. For example, it provides rewards to users who perform regular maintenance or version upgrades. In this way, by distributing rewards to users who contribute to improving and updating the script, it is possible to improve user motivation.

[0091] The fee setting unit, for example, uses an emotion estimation function to identify the functions or parts that users find most valuable and builds a system to set additional fees. For example, a premium fee is set for a specific function. The fee setting unit also identifies the parts that users find most valuable based on user emotion data and sets additional fees. For example, additional fees are set for functions that provide high user satisfaction. The fee setting unit also uses the emotion estimation function to develop a system to identify the parts that users find most valuable and set additional fees. For example, fees are adjusted based on the user's emotion score. This makes it possible to maximize revenue by setting additional fees for the functions or parts that users find most valuable.

[0092] The question and answer section, for example, analyzes the question and answer history to build a system that evaluates the user's learning progress. For example, the learning progress is evaluated based on the content of past questions and the answer history. The question and answer section also analyzes the question and answer history to evaluate the user's level of understanding. For example, the level of understanding is evaluated based on the correct answer rate and the difficulty of the questions. The question and answer section also adds a function to evaluate the user's learning progress and level of understanding based on the question and answer history. For example, the learning progress status is displayed in a graph. This makes it possible to improve the learning effect by evaluating the user's learning progress and level of understanding.

[0093] The Q&A department, for example, analyzes the content of the Q&A session and builds a system that provides a user with a customized study plan. For example, the study plan is adjusted based on the content of the user's question. The Q&A department also provides the user with additional resources based on the Q&A history. For example, related teaching materials and reference materials are recommended. The Q&A department also analyzes the content of the Q&A session and develops a system that provides the user with a customized study plan and additional resources. For example, the study plan is adjusted according to the user's level of understanding. This allows the user to be provided with a customized study plan and additional resources, thereby improving learning effectiveness.

[0094] The question and answering unit, for example, uses an emotion estimation function to analyze a user's emotional response to a question and builds a system that generates a friendly answer. For example, if the user is feeling anxious, it provides an answer that gives a sense of security. The question and answering unit also analyzes the emotional response to a question based on the user's emotion data and generates a friendly answer. For example, if the user is confused, it provides an easy-to-understand explanation. The question and answering unit also uses the emotion estimation function to develop a system that analyzes a user's emotional response to a question and generates a more friendly answer. For example, it provides a positive answer that makes the user feel happy. In this way, by analyzing the user's emotional response and generating a friendly answer, it is possible to improve user satisfaction.

[0095] For example, the Q&A department applies the Q&A function to an online education platform to provide an environment where students can freely ask questions. For example, questions can be accepted in real time during class. The Q&A department also applies the Q&A function to a customer support platform to allow customers to freely ask questions about products and services. For example, questions can be answered using a chatbot. The Q&A department also applies the Q&A function to other platforms to provide an environment where users can freely ask questions. For example, it supports Q&A on forums and community sites. In this way, the Q&A function can be applied to other platforms to improve user convenience.

[0096] The question and answer section, for example, builds a system that provides a community function where users can help each other based on the content of the question and answer session. For example, it allows other users to provide answers to questions. The question and answer section also uses the community function to provide an environment where users can share knowledge with each other. For example, it evaluates answers to questions and highlights good answers. The question and answer section also develops a system that provides a community function where users can help each other based on the content of the question and answer session, and promotes knowledge sharing. For example, other users complete answers to questions. In this way, by providing a community function where users can help each other, it is possible to promote knowledge sharing and improve user satisfaction.

[0097] The question and answer unit, for example, uses an emotion estimation function to monitor the emotions of a user when asking a question in real time and build a system that provides support to reduce stress. For example, if the user is nervous, a message to help the user relax is displayed. The question and answer unit also monitors the emotions of a user when asking a question based on the user's emotion data and provides support to reduce stress. For example, if the user is feeling anxious, an answer that gives a sense of security is provided. The question and answer unit also uses an emotion estimation function to develop a system that monitors the emotions of a user when asking a question in real time and provides support to reduce stress. For example, if the user is confused, an easy-to-understand explanation is provided. In this way, by monitoring the emotions of a user when asking a question in real time and providing support to reduce stress, user satisfaction can be improved.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The script analysis unit analyzes the contents of the script. For example, a generation AI analyzes the contents of the script and converts them into a standard format. The script analysis unit can also make suggestions for improving the efficiency and optimization of the script. Step 2: The fee setting unit sets a usage fee based on the script analyzed by the script analysis unit, for example, depending on the number of times the script is used or the duration of use. Step 3: The revenue distribution unit calculates the cost each time a user uses the script based on the fee set by the fee setting unit, and distributes the revenue to the creator. For example, revenue is returned to the creator each time a user uses the script. Step 4: In the Q&A section, the user can freely repeat the Q&A process using the generation AI. For example, the generation AI analyzes the user's question and generates an appropriate answer.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0113] 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.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] The data processing system 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a script analysis unit that analyzes the contents of a script; a fee setting unit that sets a usage fee based on the script analyzed by the script analysis unit; a revenue distribution unit that calculates a cost each time a user uses a script based on the fee set by the fee setting unit and distributes the revenue to the creator; A question and answer section in which users can freely repeat questions and answers using the generation AI. A system characterized by:

2. The script analysis unit Providing efficiency and optimization suggestions for said scripts 2. The system of claim 1.

3. The script analysis unit Automatically converting said scripts between different programming languages 2. The system of claim 1.

4. The script analysis unit Infer the script writer's intent and purpose and generate the optimal format based on that 2. The system of claim 1.

5. The script analysis unit Applying the standard formatting of the script to fields other than programming 2. The system of claim 1.

6. The script analysis unit Providing collaborative editing of the script, allowing multiple users to improve the script in real time 2. The system of claim 1.

7. The script analysis unit Identifying which parts of the script the user is having difficulty with and automatically making suggestions to improve those parts 2. The system of claim 1.

8. The fee setting unit The usage status of the script is monitored in real time, and the fee is dynamically adjusted according to the satisfaction level of the user.

2. The system of claim 1.

Images