system

Abstract

The system according to the embodiment aims to quickly perform design-conscious coding. [Solution] A system according to an embodiment includes a receiving unit, a generating unit, a design unit, and an evaluation unit. The receiving unit receives original code and requests. The generating unit generates code based on the information received by the receiving unit. The design unit designs an LP based on the code generated by the generating unit. The evaluation unit evaluates the quality of the code generated by the design unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, design-conscious coding in creating landing pages was difficult and time-consuming.

[0005] The system according to the embodiment aims to quickly perform design-conscious coding. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a design unit, and an evaluation unit. The receiving unit receives the original code and a request. The generating unit generates code based on the information received by the receiving unit. The design unit designs an LP based on the code generated by the generating unit. The evaluation unit evaluates the quality of the code generated by the design unit. [Effects of the Invention]

[0007] The system according to the embodiment allows rapid coding that takes design into consideration. [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 landing page (LP) creation system according to an embodiment of the present invention utilizes a generative AI to streamline design-conscious coding when creating LPs based on internal or external requests. This system includes a process for transmitting the original code and user requirements to a generative AI, generating appropriate code based on that information, designing an LP based on the generated code, and evaluating the quality of the code. This significantly reduces the time required compared to traditional search or human inquiries, and is expected to improve the quality and number of LP designs produced. For example, a user inputs specific requests and design instructions, such as "I want to use specific colors and layouts." This information is then input into the generative AI. The generative AI then analyzes the input information and generates appropriate code. The generative AI then generates optimal code based on the original code and user requirements. For example, it generates HTML and CSS code based on the user-specified colors and layout. The LP is then designed based on the generated code. For example, the generated HTML and CSS code is used to create an LP based on the specified design. This process streamlines design-conscious coding. Furthermore, the quality of the generated code is evaluated, for example, to ensure that the generated code works correctly and meets the user's requirements. This evaluation process ensures the quality of the generated code. This system significantly reduces the time required compared to traditional searches or asking people questions, and is expected to improve the quality of LP designs and the number of productions. For example, coding work that previously took several hours can now be completed in just a few minutes using generative AI. This time reduction makes it possible to create more LPs with high quality. This allows the LP creation system to efficiently create LPs based on internal and external requests and ensure their quality.

[0029] The LP creation system according to the embodiment includes a reception unit, a generation unit, a design unit, and an evaluation unit. The reception unit receives original code and requests. The original code may include, but is not limited to, programming languages ​​such as HTML, CSS, and JavaScript (registered trademark). The requests may include, but are not limited to, specifications for specific colors or layouts, requests for additional features, and the like. The reception unit, for example, receives requests entered by a user in text format. The reception unit can also receive requests using voice input. For example, when a user verbally communicates a request, the reception unit converts the speech into text and accepts it as a request. The generation unit uses a generation AI to generate code based on the information received by the reception unit. The generation unit generates HTML or CSS code, for example, based on the colors and layout specified by the user. The generation unit uses a generation AI to analyze the original code and requests and generate optimal code. For example, the generation AI receives a prompt such as, "Please generate optimal code based on this request," and generates code. The design unit designs the LP based on the code generated by the generation unit. The design unit creates a landing page based on a specified design, for example, using the generated HTML or CSS code. The design unit can also use a generation AI to create a design based on the generated code. For example, the generation AI receives a prompt such as, "Please create a design based on this code," and creates a design. The evaluation unit evaluates the quality of the code generated by the generation unit. The evaluation unit checks, for example, whether the generated code works correctly and whether the design is as desired. The evaluation unit can also use the generation AI to evaluate the quality of the generated code. For example, the generation AI receives a prompt such as, "Please evaluate the quality of this code," and performs an evaluation. This allows the landing page creation system according to the embodiment to efficiently create landing pages based on internal and external requests and ensure their quality.

[0030] The reception unit can receive user requests or design instructions. For example, the reception unit receives requests input by the user in text format. Requests include, but are not limited to, feature additions and design changes. The reception unit can also receive requests using voice input. For example, when a user communicates a request by voice, the reception unit converts the voice into text and accepts it as a request. Design instructions include, but are not limited to, color specifications, layout specifications, and the like. The reception unit receives design instructions input by the user in text format. For example, if a user inputs an instruction such as "Please color this part red," the reception unit accepts the instruction. In this way, by accepting the user's requests and design instructions, the generated code is in line with the user's intentions.

[0031] The generation unit can generate HTML and CSS code based on the original code and the user's requests. The generation unit generates HTML and CSS code based on, for example, colors and layouts specified by the user. The original code includes, but is not limited to, programming languages ​​such as HTML, CSS, and JavaScript. The requests include, but are not limited to, specifications of specific colors and layouts, requests for additional features, and the like. The generation unit uses a generation AI to analyze the original code and the user's requests and generate optimal code. For example, the generation AI receives a prompt saying, "Please generate optimal code based on these requests," and generates the code. The generated HTML and CSS code includes, but is not limited to, HTML versions, CSS frameworks, and the like. This allows for efficient creation of landing pages by generating HTML and CSS code based on the original code and the user's requests.

[0032] The design department can create a landing page using the generated HTML and CSS code. The design department, for example, uses the generated HTML and CSS code to create a landing page based on a specified design. The generated HTML and CSS code includes, but is not limited to, for example, an HTML version, a CSS framework, etc. The design department can also use a generation AI to create a design based on the generated code. For example, the generation AI receives a prompt saying, "Please create a design based on this code," and then creates a design. This allows design-conscious coding to be done more efficiently by creating a landing page using the generated HTML and CSS code.

[0033] The evaluation unit can check whether the generated code works and whether the design is as desired. For example, the evaluation unit checks whether the generated code works correctly and whether the design is as desired. Operational checks include, but are not limited to, test cases, operating environments, etc. Confirmation of as desired includes, but are not limited to, the content of the request, evaluation criteria, etc. The evaluation unit can also evaluate the quality of the generated code using the generation AI. For example, the generation AI receives a prompt such as "Please evaluate the quality of this code" and performs the evaluation. In this way, by evaluating the quality of the generated code, the quality of the generated code can be ensured.

[0034] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit automatically displays requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period based on the user's past request history. The past request history includes, for example, the type of request, the method of saving the history, etc., but is not limited to these examples. In this way, the optimal reception method can be provided by analyzing the user's past request history.

[0035] When receiving a request, the reception unit can filter the requests based on the user's current project or field of interest. For example, the reception unit preferentially receives requests related to a project currently in progress by the user. The reception unit can also filter and display related requests based on the user's field of interest. Furthermore, the reception unit can suggest related requests based on fields in which the user has previously shown interest. Examples of current projects include, but are not limited to, project types and progress statuses. Examples of fields of interest include, but are not limited to, technical fields and industries. In this way, by filtering requests based on the user's current project or field of interest, highly relevant requests can be preferentially received.

[0036] When receiving a request, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving requests related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving requests related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving requests related to the area around the user's home. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. Examples of highly relevant requests include, but are not limited to, geographical proximity, related projects, etc. In this way, by taking into account the user's geographical location information, highly relevant requests can be prioritized.

[0037] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can preferentially receive requests related to topics that the user frequently mentions on social media. The reception unit can also receive requests related to topics in which the user's social media followers are interested. Furthermore, the reception unit can also receive requests related to groups or communities in which the user participates on social media. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. Related requests include, for example, but are not limited to, social media topics and the user's interests. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related requests.

[0038] When generating code, the generator can adjust the level of detail of the generated code based on the importance of the requirement. For example, the generator generates detailed code for a requirement with a high importance. The generator can also generate concise code for a requirement with a low importance. Furthermore, the generator can generate code that includes comments and explanations depending on the importance. Examples of the importance of a requirement include, but are not limited to, business impact and technical difficulty. Examples of the level of detail of the generated code include, but are not limited to, the number of lines of code and detailed comments. In this way, by adjusting the level of detail of the generated code based on the importance of the requirement, detailed code can be generated for important requirements.

[0039] When generating code, the generation unit can apply different generation algorithms depending on the category of the request. For example, the generation unit can apply a design-specialized generation algorithm to a design request. Furthermore, the generation unit can also apply a function-specialized generation algorithm to a function request. Furthermore, the generation unit can also apply a performance-specialized generation algorithm to a performance request. The request categories include, for example, but are not limited to, feature additions and bug fixes. The generation algorithms include, for example, but are not limited to, template-based and machine learning-based. In this way, optimal code can be generated by applying different generation algorithms depending on the category of the request.

[0040] When generating code, the generation unit can determine a generation priority based on the time of request submission. For example, the generation unit generates code with the highest priority for urgent requests. The generation unit can also generate code with normal priority for normal requests. Furthermore, the generation unit can postpone code generation for long-term requests. The time of request submission includes, for example, but is not limited to, the submission date and time, the submission order, etc. The generation priority includes, for example, but is not limited to, the urgency and the importance, etc. In this way, by determining the generation priority based on the time of request submission, it is possible to respond quickly to highly urgent requests.

[0041] When generating code, the generation unit can adjust the order of generation based on the relevance of the requests. For example, the generation unit can generate code preferentially for requests with high relevance. The generation unit can also postpone generating code for requests with low relevance. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the requests. Examples of the relevance of the requests include, but are not limited to, technical relevance and business relevance. Examples of the order of generation include, but are not limited to, a method of prioritizing requests with high relevance. In this way, by adjusting the order of generation based on the relevance of the requests, highly relevant requests can be addressed preferentially.

[0042] When creating a design, the design department can adjust the level of detail of the design based on the importance of the generated code. For example, the design department can provide a detailed design for code with high importance. The design department can also provide a simple design for code with low importance. Furthermore, the design department can adjust the complexity of the design depending on the importance. Examples of the importance of the generated code include, but are not limited to, business impact and technical difficulty. Examples of the detail of the design include, but are not limited to, design details and detailed layout. In this way, by adjusting the level of detail of the design based on the importance of the generated code, a detailed design can be provided for important code.

[0043] When creating a design, the design unit can apply different design algorithms depending on the category of the generated code. For example, the design unit can apply a design-specific algorithm to a design request. Furthermore, the design unit can apply a function-specific algorithm to a function request. Furthermore, the design unit can apply a performance-specific algorithm to a performance request. Examples of categories of generated code include, but are not limited to, feature additions and bug fixes. Examples of design algorithms include, but are not limited to, template-based and machine learning-based algorithms. In this way, by applying different design algorithms depending on the category of generated code, an optimal design can be provided.

[0044] When creating a design, the design department can determine the priority of the design based on the submission time of the generated code. For example, the design department can provide a design with the highest priority for an urgent request. The design department can also provide a design with a normal priority for a normal request. Furthermore, the design department can postpone the provision of a design for a long-term request. The submission time of the generated code includes, for example, but is not limited to, the submission date and time, the submission order, etc. The priority of the design includes, for example, but is not limited to, the urgency, the importance, etc. In this way, by determining the priority of the design based on the submission time of the generated code, it is possible to respond quickly to highly urgent requests.

[0045] When creating a design, the design department can adjust the order of the designs based on the relevance of the generated code. For example, the design department can provide designs preferentially for highly relevant code. The design department can also provide designs later for less relevant code. Furthermore, the design department can dynamically adjust the order of the designs according to the relevance of the code. Examples of the relevance of the generated code include, but are not limited to, technical relevance and business relevance. Examples of the order of the designs include, but are not limited to, a method of prioritizing highly relevant requests. In this way, by adjusting the order of the designs based on the relevance of the generated code, highly relevant requests can be addressed preferentially.

[0046] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data of the generated code. The evaluation unit, for example, adjusts the evaluation algorithm based on the past evaluation data. The evaluation unit can also extract specific patterns from the past evaluation data and reflect them in the evaluation algorithm. The evaluation unit can also analyze the past evaluation data to improve the accuracy of the evaluation algorithm. Past evaluation data includes, for example, evaluation results and evaluation criteria, but is not limited to these examples. Optimization of the evaluation algorithm includes, for example, parameter adjustment and algorithm improvement, but is not limited to these examples. In this way, by referring to the past evaluation data of the generated code, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved.

[0047] During evaluation, the evaluation unit can apply different evaluation methods depending on the category of the generated code. For example, the evaluation unit can apply a design-specific evaluation method to design code. The evaluation unit can also apply a function-specific evaluation method to function code. The evaluation unit can also apply a performance-specific evaluation method to performance code. Examples of categories of generated code include, but are not limited to, feature additions and bug fixes. Examples of evaluation methods include, but are not limited to, test cases and evaluation criteria. This allows for optimal evaluation by applying different evaluation methods depending on the category of generated code.

[0048] During evaluation, the evaluation unit can weight the evaluation based on the submission time of the generated code. For example, the evaluation unit can give top priority to urgent code. The evaluation unit can also evaluate normal code with normal priority. Furthermore, the evaluation unit can postpone evaluation of long-term code. The submission time of the generated code includes, for example, but is not limited to, the submission date and time, the submission order, etc. The weighting of the evaluation includes, for example, but is not limited to, the urgency and importance. In this way, by weighting the evaluation based on the submission time of the generated code, it is possible to respond quickly to highly urgent code.

[0049] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the generated code. The evaluation unit, for example, adjusts the evaluation criteria based on the related literature. The evaluation unit can also extract specific patterns from the related literature and reflect them in the evaluation criteria. Furthermore, the evaluation unit can analyze the related literature to improve the accuracy of the evaluation criteria. Examples of related literature include, but are not limited to, academic papers and technical reports. Examples of improving the accuracy of the evaluation include, but are not limited to, refining the evaluation criteria and improving the evaluation process. In this way, the accuracy of the evaluation can be improved by referring to literature related to the generated code.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's past behavior history and suggest the optimal method for receiving a request. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of requests that the user has input in the past and automatically display similar requests as candidates. Furthermore, the reception unit can predict and suggest requests that will be used in specific time periods based on the user's past behavior history. In this way, the optimal method for receiving a request can be provided by analyzing the user's past behavior history.

[0052] The design department can adjust the level of detail of the design based on the importance of the generated code. For example, a detailed design can be provided for code with high importance. A simple design can also be provided for code with low importance. Furthermore, the complexity of the design can be adjusted according to the importance. In this way, by adjusting the level of detail of the design based on the importance of the generated code, a detailed design can be provided for important code.

[0053] The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. For example, if the user is in a specific area, requests related to that area can be prioritized. Also, if the user is traveling, requests related to the travel destination can be prioritized. Furthermore, if the user is at home, requests related to the area around the user's home can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant requests can be prioritized.

[0054] When generating code, the generation unit can apply different generation algorithms depending on the category of requirements. For example, a generation algorithm specialized for design can be applied to a design requirement. A generation algorithm specialized for function can be applied to a function requirement. Furthermore, a generation algorithm specialized for performance can be applied to a performance requirement. In this way, by applying different generation algorithms depending on the category of requirements, optimal code can be generated.

[0055] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data of the generated code. For example, the evaluation algorithm can be adjusted based on the past evaluation data. Specific patterns can also be extracted from the past evaluation data and reflected in the evaluation algorithm. Furthermore, the accuracy of the evaluation algorithm can be improved by analyzing the past evaluation data. Thus, by referring to the past evaluation data of the generated code, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved.

[0056] When generating code, the generator can determine the priority of generation based on the time of request submission. For example, for urgent requests, code can be generated with the highest priority. For normal requests, code can be generated with normal priority. Furthermore, code can be generated later for long-term requests. In this way, by determining the priority of generation based on the time of request submission, it is possible to respond quickly to requests with high urgency.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives the original code and the request. The original code includes, but is not limited to, programming languages ​​such as HTML, CSS, and JavaScript. The request may include specifications for specific colors or layouts, requests for additional features, etc. The reception unit not only receives the request entered by the user in text format, but can also receive the request using voice input. For example, when the user communicates the request by voice, the reception unit converts the voice into text and accepts it as the request. Step 2: The generation unit uses the generation AI to generate code based on the information received by the reception unit. The generation unit generates HTML and CSS code based on the colors and layout specified by the user. The generation unit analyzes the original code and the request and generates the optimal code. For example, the generation AI receives a prompt such as "Please generate the optimal code based on this request" and generates the code. Step 3: The design department designs the landing page based on the code generated by the generation department. The design department uses the generated HTML and CSS code to create a landing page based on the specified design. The design department can also use the generation AI to design based on the generated code. For example, the generation AI receives a prompt saying, "Please create a design based on this code," and then designs. Step 4: The evaluator evaluates the quality of the code generated by the generator. The evaluator checks whether the generated code works correctly and whether the design is as desired. The evaluator can also use the generator AI to evaluate the quality of the generated code. For example, the generator AI receives a prompt saying, "Please evaluate the quality of this code," and performs the evaluation.

[0059] (Example 2) A landing page (LP) creation system according to an embodiment of the present invention utilizes a generative AI to streamline design-conscious coding when creating LPs based on internal or external requests. This system includes a process for transmitting the original code and user requirements to a generative AI, generating appropriate code based on that information, designing an LP based on the generated code, and evaluating the quality of the code. This significantly reduces the time required compared to traditional search or human inquiries, and is expected to improve the quality and number of LP designs produced. For example, a user inputs specific requests and design instructions, such as "I want to use specific colors and layouts." This information is then input into the generative AI. The generative AI then analyzes the input information and generates appropriate code. The generative AI then generates optimal code based on the original code and user requirements. For example, it generates HTML and CSS code based on the user-specified colors and layout. The LP is then designed based on the generated code. For example, the generated HTML and CSS code is used to create an LP based on the specified design. This process streamlines design-conscious coding. Furthermore, the quality of the generated code is evaluated, for example, to ensure that the generated code works correctly and meets the user's requirements. This evaluation process ensures the quality of the generated code. This system significantly reduces the time required compared to traditional searches or asking people questions, and is expected to improve the quality of LP designs and the number of productions. For example, coding work that previously took several hours can now be completed in just a few minutes using generative AI. This time reduction makes it possible to create more LPs with high quality. This allows the LP creation system to efficiently create LPs based on internal and external requests and ensure their quality.

[0060] The LP creation system according to the embodiment includes a reception unit, a generation unit, a design unit, and an evaluation unit. The reception unit receives original code and requests. The original code may include, but is not limited to, programming languages ​​such as HTML, CSS, and JavaScript. The requests may include, but are not limited to, specifications of specific colors and layouts, requests for additional functions, and the like. The reception unit, for example, receives requests entered by a user in text format. The reception unit can also receive requests using voice input. For example, when a user verbally communicates a request, the reception unit converts the speech into text and accepts it as a request. The generation unit uses a generation AI to generate code based on the information received by the reception unit. The generation unit generates HTML or CSS code based on, for example, the colors and layout specified by the user. The generation unit uses a generation AI to analyze the original code and requests and generate optimal code. For example, the generation AI receives a prompt such as, "Please generate optimal code based on this request," and generates code. The design unit designs the LP based on the code generated by the generation unit. The design unit creates a landing page based on a specified design, for example, using the generated HTML or CSS code. The design unit can also use a generation AI to create a design based on the generated code. For example, the generation AI receives a prompt such as, "Please create a design based on this code," and creates a design. The evaluation unit evaluates the quality of the code generated by the generation unit. The evaluation unit checks, for example, whether the generated code works correctly and whether the design is as desired. The evaluation unit can also use the generation AI to evaluate the quality of the generated code. For example, the generation AI receives a prompt such as, "Please evaluate the quality of this code," and performs an evaluation. This allows the landing page creation system according to the embodiment to efficiently create landing pages based on internal and external requests and ensure their quality.

[0061] The reception unit can receive user requests or design instructions. For example, the reception unit receives requests input by the user in text format. Requests include, but are not limited to, feature additions and design changes. The reception unit can also receive requests using voice input. For example, when a user communicates a request by voice, the reception unit converts the voice into text and accepts it as a request. Design instructions include, but are not limited to, color specifications, layout specifications, and the like. The reception unit receives design instructions input by the user in text format. For example, if a user inputs an instruction such as "Please color this part red," the reception unit accepts the instruction. In this way, by accepting the user's requests and design instructions, the generated code is in line with the user's intentions.

[0062] The generation unit can generate HTML and CSS code based on the original code and the user's requests. The generation unit generates HTML and CSS code based on, for example, colors and layouts specified by the user. The original code includes, but is not limited to, programming languages ​​such as HTML, CSS, and JavaScript. The requests include, but are not limited to, specifications of specific colors and layouts, requests for additional features, and the like. The generation unit uses a generation AI to analyze the original code and the user's requests and generate optimal code. For example, the generation AI receives a prompt saying, "Please generate optimal code based on these requests," and generates the code. The generated HTML and CSS code includes, but is not limited to, HTML versions, CSS frameworks, and the like. This allows for efficient creation of landing pages by generating HTML and CSS code based on the original code and the user's requests.

[0063] The design department can create a landing page using the generated HTML and CSS code. The design department, for example, uses the generated HTML and CSS code to create a landing page based on a specified design. The generated HTML and CSS code includes, but is not limited to, for example, an HTML version, a CSS framework, etc. The design department can also use a generation AI to create a design based on the generated code. For example, the generation AI receives a prompt saying, "Please create a design based on this code," and then creates a design. This allows design-conscious coding to be done more efficiently by creating a landing page using the generated HTML and CSS code.

[0064] The evaluation unit can check whether the generated code works and whether the design is as desired. For example, the evaluation unit checks whether the generated code works correctly and whether the design is as desired. Operational checks include, but are not limited to, test cases, operating environments, etc. Confirmation of as desired includes, but are not limited to, the content of the request, evaluation criteria, etc. The evaluation unit can also evaluate the quality of the generated code using the generation AI. For example, the generation AI receives a prompt such as "Please evaluate the quality of this code" and performs the evaluation. In this way, by evaluating the quality of the generated code, the quality of the generated code can be ensured.

[0065] The reception unit can estimate the user's emotions and adjust the method of receiving requests based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Alternatively, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the user to input requests quickly. Emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. As a result, the method of receiving requests can be adjusted according to the user's emotions, thereby improving user satisfaction.

[0066] The reception unit can analyze the user's past request history and select the optimal reception method. For example, the reception unit automatically displays requests that the user has frequently input in the past as candidates. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest requests to be used in a specific time period based on the user's past request history. The past request history includes, for example, the type of request, the method of saving the history, etc., but is not limited to these examples. In this way, the optimal reception method can be provided by analyzing the user's past request history.

[0067] When receiving a request, the reception unit can filter the requests based on the user's current project or field of interest. For example, the reception unit preferentially receives requests related to a project currently in progress by the user. The reception unit can also filter and display related requests based on the user's field of interest. Furthermore, the reception unit can suggest related requests based on fields in which the user has previously shown interest. Examples of current projects include, but are not limited to, project types and progress statuses. Examples of fields of interest include, but are not limited to, technical fields and industries. In this way, by filtering requests based on the user's current project or field of interest, highly relevant requests can be preferentially received.

[0068] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user emotions. For example, when the user is feeling stressed, the reception unit can prioritize urgent requests. Furthermore, when the user is relaxed, the reception unit can also prioritize requests that require a quick response when the user is in a hurry. The emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. In this way, by determining the priority of requests according to the user's emotions, it is possible to prioritize urgent requests.

[0069] When receiving a request, the reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. For example, when the user is in a specific area, the reception unit can prioritize receiving requests related to that area. Furthermore, when the user is traveling, the reception unit can also prioritize receiving requests related to the travel destination. Furthermore, when the user is at home, the reception unit can also prioritize receiving requests related to the area around the user's home. Examples of geographical location information include, but are not limited to, GPS data, address information, etc. Examples of highly relevant requests include, but are not limited to, geographical proximity, related projects, etc. In this way, by taking into account the user's geographical location information, highly relevant requests can be prioritized.

[0070] When receiving a request, the reception unit can analyze the user's social media activity and receive related requests. For example, the reception unit can preferentially receive requests related to topics that the user frequently mentions on social media. The reception unit can also receive requests related to topics in which the user's social media followers are interested. Furthermore, the reception unit can also receive requests related to groups or communities in which the user participates on social media. Social media activity includes, for example, but is not limited to, the content of posts and the number of followers. Related requests include, for example, but are not limited to, social media topics and the user's interests. In this way, by analyzing the user's social media activity, it is possible to preferentially receive related requests.

[0071] The generation unit can estimate the user's emotions and adjust the expression style of the generated code based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate easy-to-read code. Furthermore, if the user is in a hurry, the generation unit can generate concise and efficient code. Furthermore, if the user is excited, the generation unit can generate visually appealing code. The emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. As a result, the expression style of the code can be adjusted according to the user's emotions, thereby generating optimal code for the user.

[0072] When generating code, the generator can adjust the level of detail of the generated code based on the importance of the requirement. For example, the generator generates detailed code for a requirement with a high importance. The generator can also generate concise code for a requirement with a low importance. Furthermore, the generator can generate code that includes comments and explanations depending on the importance. Examples of the importance of a requirement include, but are not limited to, business impact and technical difficulty. Examples of the level of detail of the generated code include, but are not limited to, the number of lines of code and detailed comments. In this way, by adjusting the level of detail of the generated code based on the importance of the requirement, detailed code can be generated for important requirements.

[0073] When generating code, the generation unit can apply different generation algorithms depending on the category of the request. For example, the generation unit can apply a design-specialized generation algorithm to a design request. Furthermore, the generation unit can also apply a function-specialized generation algorithm to a function request. Furthermore, the generation unit can also apply a performance-specialized generation algorithm to a performance request. The request categories include, for example, but are not limited to, feature additions and bug fixes. The generation algorithms include, for example, but are not limited to, template-based and machine learning-based. In this way, optimal code can be generated by applying different generation algorithms depending on the category of the request.

[0074] The generator can estimate the user's emotions and adjust the length of the generated chord based on the estimated user emotions. For example, if the user is in a hurry, the generator can generate a short, to-the-point chord. If the user is relaxed, the generator can generate a longer chord with detailed explanations. Furthermore, if the user is excited, the generator can generate a chord with visually appealing effects. The emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. This allows the optimal chord to be generated by adjusting the length of the chord according to the user's emotions.

[0075] When generating code, the generation unit can determine a generation priority based on the time of request submission. For example, the generation unit generates code with the highest priority for urgent requests. The generation unit can also generate code with normal priority for normal requests. Furthermore, the generation unit can postpone code generation for long-term requests. The time of request submission includes, for example, but is not limited to, the submission date and time, the submission order, etc. The generation priority includes, for example, but is not limited to, the urgency and the importance, etc. In this way, by determining the generation priority based on the time of request submission, it is possible to respond quickly to highly urgent requests.

[0076] When generating code, the generation unit can adjust the order of generation based on the relevance of the requests. For example, the generation unit can generate code preferentially for requests with high relevance. The generation unit can also postpone generating code for requests with low relevance. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the requests. Examples of the relevance of the requests include, but are not limited to, technical relevance and business relevance. Examples of the order of generation include, but are not limited to, a method of prioritizing requests with high relevance. In this way, by adjusting the order of generation based on the relevance of the requests, highly relevant requests can be addressed preferentially.

[0077] The design unit can estimate the user's emotions and adjust the way the design is presented based on the estimated user's emotions. For example, if the user is relaxed, the design unit can provide a design with soft colors. If the user is in a hurry, the design unit can also provide a simple, highly visible design. Furthermore, if the user is excited, the design unit can also provide a visually stimulating design. Emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. As a result, the way the design is presented can be adjusted according to the user's emotions, thereby providing the optimal design for the user.

[0078] When creating a design, the design department can adjust the level of detail of the design based on the importance of the generated code. For example, the design department can provide a detailed design for code with high importance. The design department can also provide a simple design for code with low importance. Furthermore, the design department can adjust the complexity of the design depending on the importance. Examples of the importance of the generated code include, but are not limited to, business impact and technical difficulty. Examples of the detail of the design include, but are not limited to, design details and detailed layout. In this way, by adjusting the level of detail of the design based on the importance of the generated code, a detailed design can be provided for important code.

[0079] When creating a design, the design unit can apply different design algorithms depending on the category of the generated code. For example, the design unit can apply a design-specific algorithm to a design request. Furthermore, the design unit can apply a function-specific algorithm to a function request. Furthermore, the design unit can apply a performance-specific algorithm to a performance request. Examples of categories of generated code include, but are not limited to, feature additions and bug fixes. Examples of design algorithms include, but are not limited to, template-based and machine learning-based algorithms. In this way, by applying different design algorithms depending on the category of generated code, an optimal design can be provided.

[0080] The design unit can estimate the user's emotions and adjust the length of the design based on the estimated user emotions. For example, if the user is in a hurry, the design unit can provide a short, to-the-point design. If the user is relaxed, the design unit can provide a longer design with detailed explanations. Furthermore, if the user is excited, the design unit can provide a design with visually appealing effects. The emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. As a result, the length of the design can be adjusted according to the user's emotions, thereby providing the optimal design for the user.

[0081] When creating a design, the design department can determine the priority of the design based on the submission time of the generated code. For example, the design department can provide a design with the highest priority for an urgent request. The design department can also provide a design with a normal priority for a normal request. Furthermore, the design department can postpone the provision of a design for a long-term request. The submission time of the generated code includes, for example, but is not limited to, the submission date and time, the submission order, etc. The priority of the design includes, for example, but is not limited to, the urgency, the importance, etc. In this way, by determining the priority of the design based on the submission time of the generated code, it is possible to respond quickly to highly urgent requests.

[0082] When creating a design, the design department can adjust the order of the designs based on the relevance of the generated code. For example, the design department can provide designs preferentially for highly relevant code. The design department can also provide designs later for less relevant code. Furthermore, the design department can dynamically adjust the order of the designs according to the relevance of the code. Examples of the relevance of the generated code include, but are not limited to, technical relevance and business relevance. Examples of the order of the designs include, but are not limited to, a method of prioritizing highly relevant requests. In this way, by adjusting the order of the designs based on the relevance of the generated code, highly relevant requests can be addressed preferentially.

[0083] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user's emotions. For example, the evaluation unit can provide a detailed evaluation when the user is relaxed. The evaluation unit can also provide a brief evaluation when the user is in a hurry. Furthermore, the evaluation unit can provide a visually appealing evaluation when the user is excited. The emotion estimation is performed using, for example, an emotion analysis algorithm or a data source. As a result, the evaluation method can be adjusted according to the user's emotions, thereby providing an optimal evaluation for the user.

[0084] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data of the generated code. The evaluation unit, for example, adjusts the evaluation algorithm based on the past evaluation data. The evaluation unit can also extract specific patterns from the past evaluation data and reflect them in the evaluation algorithm. The evaluation unit can also analyze the past evaluation data to improve the accuracy of the evaluation algorithm. Past evaluation data includes, for example, evaluation results and evaluation criteria, but is not limited to these examples. Optimization of the evaluation algorithm includes, for example, parameter adjustment and algorithm improvement, but is not limited to these examples. In this way, by referring to the past evaluation data of the generated code, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved.

[0085] During evaluation, the evaluation unit can apply different evaluation methods depending on the category of the generated code. For example, the evaluation unit can apply a design-specific evaluation method to design code. The evaluation unit can also apply a function-specific evaluation method to function code. The evaluation unit can also apply a performance-specific evaluation method to performance code. Examples of categories of generated code include, but are not limited to, feature additions and bug fixes. Examples of evaluation methods include, but are not limited to, test cases and evaluation criteria. This allows for optimal evaluation by applying different evaluation methods depending on the category of generated code.

[0086] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user's emotions. For example, when the user is feeling stressed, the evaluation unit can prioritize evaluations with a high level of urgency. Furthermore, when the user is relaxed, the evaluation unit can also prioritize evaluations with a normal level of priority. Furthermore, when the user is in a hurry, the evaluation unit can also prioritize evaluations that require a quick response. The estimation of emotions is performed using, for example, an emotion analysis algorithm or a data source. In this way, by determining the priority of evaluations according to the user's emotions, it is possible to prioritize evaluations with a high level of urgency.

[0087] During evaluation, the evaluation unit can weight the evaluation based on the submission time of the generated code. For example, the evaluation unit can give top priority to urgent code. The evaluation unit can also evaluate normal code with normal priority. Furthermore, the evaluation unit can postpone evaluation of long-term code. The submission time of the generated code includes, for example, but is not limited to, the submission date and time, the submission order, etc. The weighting of the evaluation includes, for example, but is not limited to, the urgency and importance. In this way, by weighting the evaluation based on the submission time of the generated code, it is possible to respond quickly to highly urgent code.

[0088] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to literature related to the generated code. The evaluation unit, for example, adjusts the evaluation criteria based on the related literature. The evaluation unit can also extract specific patterns from the related literature and reflect them in the evaluation criteria. Furthermore, the evaluation unit can analyze the related literature to improve the accuracy of the evaluation criteria. Examples of related literature include, but are not limited to, academic papers and technical reports. Examples of improving the accuracy of the evaluation include, but are not limited to, refining the evaluation criteria and improving the evaluation process. In this way, the accuracy of the evaluation can be improved by referring to literature related to the generated code. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, design unit, and evaluation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives requests entered by the user in text format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the original code and the requests using a generation AI to generate optimal code. The design unit is realized, for example, by the control unit 46A of the smart device 14 and creates an LP based on a specified design using the generated HTML and CSS code. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quality of the generated code. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, design unit, and evaluation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives requests entered by the user in text format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the original code and the request using a generation AI to generate optimal code. The design unit is realized, for example, by the control unit 46A of the smart glasses 214 and creates a landing page based on a specified design using the generated HTML and CSS code. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quality of the generated code. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, design unit, and evaluation unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives requests entered by the user in text format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the original code and the requests using a generation AI to generate optimal code. The design unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and creates a landing page based on a specified design using the generated HTML and CSS code. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quality of the generated code. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, design unit, and evaluation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives requests entered by the user in text format. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the original code and the requests using a generation AI to generate optimal code. The design unit is realized, for example, by the control unit 46A of the robot 414 and creates a landing page based on a specified design using the generated HTML and CSS code. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and evaluates the quality of the generated code.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The reception unit can analyze the user's past behavior history and suggest the optimal method for receiving a request. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also analyze patterns of requests that the user has input in the past and automatically display similar requests as candidates. Furthermore, the reception unit can predict and suggest requests that will be used in specific time periods based on the user's past behavior history. In this way, the optimal method for receiving a request can be provided by analyzing the user's past behavior history.

[0091] The generation unit can estimate the user's emotions and adjust the style of the generated code based on the estimated user's emotions. For example, if the user is relaxed, easy-to-read code can be generated. If the user is in a hurry, concise and efficient code can be generated. Furthermore, if the user is excited, visually appealing code can be generated. In this way, by adjusting the code style according to the user's emotions, it is possible to generate optimal code for the user.

[0092] The design department can adjust the level of detail of the design based on the importance of the generated code. For example, a detailed design can be provided for code with high importance. A simple design can also be provided for code with low importance. Furthermore, the complexity of the design can be adjusted according to the importance. In this way, by adjusting the level of detail of the design based on the importance of the generated code, a detailed design can be provided for important code.

[0093] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated user's emotions. For example, if the user is relaxed, a detailed evaluation can be made. If the user is in a hurry, a brief evaluation can be made. Furthermore, if the user is excited, a visually appealing evaluation can be made. In this way, by adjusting the evaluation method according to the user's emotions, it is possible to provide an optimal evaluation for the user.

[0094] The reception unit can prioritize receiving highly relevant requests by taking into account the user's geographical location information. For example, if the user is in a specific area, requests related to that area can be prioritized. Also, if the user is traveling, requests related to the travel destination can be prioritized. Furthermore, if the user is at home, requests related to the area around the user's home can be prioritized. In this way, by taking into account the user's geographical location information, highly relevant requests can be prioritized.

[0095] When generating code, the generation unit can apply different generation algorithms depending on the category of requirements. For example, a generation algorithm specialized for design can be applied to a design requirement. A generation algorithm specialized for function can be applied to a function requirement. Furthermore, a generation algorithm specialized for performance can be applied to a performance requirement. In this way, by applying different generation algorithms depending on the category of requirements, optimal code can be generated.

[0096] The design unit can estimate the user's emotions and adjust the way the design is presented based on the estimated user's emotions. For example, if the user is relaxed, a design with soft colors can be provided. If the user is in a hurry, a simple, highly visible design can be provided. Furthermore, if the user is excited, a visually stimulating design can be provided. In this way, by adjusting the way the design is presented according to the user's emotions, it is possible to provide the optimal design for the user.

[0097] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to past evaluation data of the generated code. For example, the evaluation algorithm can be adjusted based on the past evaluation data. Specific patterns can also be extracted from the past evaluation data and reflected in the evaluation algorithm. Furthermore, the accuracy of the evaluation algorithm can be improved by analyzing the past evaluation data. Thus, by referring to the past evaluation data of the generated code, the evaluation algorithm can be optimized and the accuracy of the evaluation can be improved.

[0098] The reception unit can estimate the user's emotions and determine the priority of requests to be received based on the estimated user's emotions. For example, if the user is feeling stressed, requests with high urgency can be received with priority. Also, if the user is relaxed, requests can be received with normal priority. Furthermore, if the user is in a hurry, requests that require a quick response can be received with priority. In this way, by determining the priority of requests according to the user's emotions, it is possible to receive requests with high urgency with priority.

[0099] When generating code, the generator can determine the priority of generation based on the time of request submission. For example, for urgent requests, code can be generated with the highest priority. For normal requests, code can be generated with normal priority. Furthermore, code can be generated later for long-term requests. In this way, by determining the priority of generation based on the time of request submission, it is possible to respond quickly to requests with high urgency.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The reception unit receives the original code and the request. The original code includes, but is not limited to, programming languages ​​such as HTML, CSS, and JavaScript. The request may include specifications for specific colors or layouts, requests for additional features, etc. The reception unit not only receives the request entered by the user in text format, but can also receive the request using voice input. For example, when the user communicates the request by voice, the reception unit converts the voice into text and accepts it as the request. Step 2: The generation unit uses the generation AI to generate code based on the information received by the reception unit. The generation unit generates HTML and CSS code based on the colors and layout specified by the user. The generation unit analyzes the original code and the request and generates the optimal code. For example, the generation AI receives a prompt such as "Please generate the optimal code based on this request" and generates the code. Step 3: The design department designs the landing page based on the code generated by the generation department. The design department uses the generated HTML and CSS code to create a landing page based on the specified design. The design department can also use the generation AI to design based on the generated code. For example, the generation AI receives a prompt saying, "Please create a design based on this code," and then designs. Step 4: The evaluator evaluates the quality of the code generated by the generator. The evaluator checks whether the generated code works correctly and whether the design is as desired. The evaluator can also use the generator AI to evaluate the quality of the generated code. For example, the generator AI receives a prompt saying, "Please evaluate the quality of this code," and performs the evaluation.

[0102] 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.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0104] 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.

[0105] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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 identification processing unit 290 using these models.

[0133] 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.

[0134] 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.

[0135] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] [Explanation of symbols]

[0174] 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 reception unit that receives the original code and requests; a generation unit that generates a code based on the information received by the reception unit; a design unit that designs an LP based on the code generated by the generation unit; an evaluation unit for evaluating the quality of the code generated by the design unit; Equipped with A system characterized by:

2. The reception unit Accept user requests or design instructions 2. The system of claim 1.

3. The generation unit Generate HTML and CSS code based on the original code and your requirements 2. The system of claim 1.

4. The design department Create a landing page using the generated HTML and CSS code 2. The system of claim 1.

5. The evaluation unit Verify that the generated code works and that the design meets your needs 2. The system of claim 1.

6. The reception unit Estimate the user's emotions and adjust the method of accepting requests based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit Analyze the user's past request history and select the optimal method of acceptance 2. The system of claim 1.

8. The reception unit When accepting requests, filter them based on the user's current projects and areas of interest 2. The system of claim 1.

Images