system

Abstract

The system according to this embodiment aims to automatically identify elements of a website or application and efficiently generate code. [Solution] The system according to the embodiment comprises a reading unit, a discrimination unit, and a generation unit. The reading unit reads the website or application to be analyzed. The discrimination unit automatically discriminates the elements read by the reading unit. The generation unit generates code based on the elements discriminated by the discrimination unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that the work of manually discriminating elements of a website or an application and generating code is complicated and inefficient.

[0005] The system according to the embodiment aims to automatically discriminate elements of a website or an application and efficiently generate code.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, a discrimination unit, and a generation unit. The reading unit reads the website or application to be analyzed. The discrimination unit automatically discriminates the elements read by the reading unit. The generation unit generates code based on the elements discriminated by the discrimination unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically identify elements of a website or application and efficiently generate code. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] [[ID=**14**]]As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The code generation system according to an embodiment of the present invention is a system that, upon loading a specified website, application, or its design, automatically identifies elements that can be interacted with on the screen and generates code to be embedded in the necessary locations based on the implementation rules of a specified analytics tool. This code generation system operates when a user loads the website, application, or its design to be analyzed into the service. The user can specify the type of analytics tool (e.g., Google® Analytics). The system automatically identifies elements that can be interacted with on the screen of the loaded website or application (e.g., buttons, links, forms, etc.). AI technology is used for this identification. The AI ​​analyzes the elements on the screen and identifies which elements are targets for user interaction. Subsequently, the system generates code to be embedded in the necessary locations based on the implementation rules of the specified analytics tool. For example, in the case of Google Analytics, code for tracking click events and scroll events is generated. This code is appropriately embedded for each element. Finally, the generated code is provided to the user. By integrating this code into the website or application, the user can observe detailed user behavior. This significantly reduces the amount of code creation required, necessitating only modifications to parts of the code that handle specific data collection methods. This system simplifies user behavior analysis on websites and applications, reducing the burden on staff. For example, it eliminates the need to manually create tracking code for click and scroll events, allowing staff to focus on more critical analytical tasks. Furthermore, because AI automatically identifies elements and generates appropriate code, it's easy to use even for staff unfamiliar with the implementation rules of analytics tools. This system is particularly useful for those using web analytics tools such as Google Analytics. For instance, it allows for easy observation of which buttons or forms are most frequently clicked or used on specific pages of a website.This allows for a detailed understanding of user behavior, which can then be used to improve websites and applications. Furthermore, the code generation system automatically identifies elements of the website or application being analyzed and generates the necessary code, thus reducing the amount of coding work required.

[0029] The code generation system according to the embodiment comprises a reading unit, a discrimination unit, and a generation unit. The reading unit reads the website or application to be analyzed. The reading unit can, for example, obtain HTML data from the website using an HTTP request. The reading unit can also read application design files stored locally using a file reading function. For example, the reading unit sends an HTTP request to a URL specified by the user and obtains HTML data returned as a response. The reading unit can also analyze design files uploaded by the user and extract the necessary data. The discrimination unit automatically discriminates the elements read by the reading unit. The discrimination unit analyzes the elements on the screen using, for example, a machine learning algorithm and identifies which elements are the target of user interaction. For example, the discrimination unit identifies elements such as buttons, links, and forms and assigns appropriate tags to each element. The discrimination unit can also classify elements based on specific conditions using a rule-based discrimination method. For example, the discrimination unit automatically identifies elements with specific CSS classes or IDs and identifies those elements as targets for interaction. The generation unit generates code based on the elements identified by the discrimination unit. The generation unit can generate code such as HTML, CSS, and JavaScript®. For example, the generation unit generates JavaScript code to track click and scroll events based on Google Analytics implementation rules. The generation unit can also generate code compatible with SEO and performance analysis tools. For example, the generation unit generates meta tags and structured data based on SEO analysis tool implementation rules. The generation unit also generates code to track page load time and resource usage based on performance analysis tool implementation rules. As a result, the code generation system according to the embodiment automatically identifies elements of the website or application being analyzed and generates the code, thereby reducing the amount of code creation work required.

[0030] The code generation system includes a specification section for specifying the type of analytics tool. This specification section provides an interface for the user to specify the type of analytics tool. For example, the specification section might allow the user to select an analytics tool using a dropdown menu or radio buttons. For instance, it might offer options such as Google Analytics, SEO analytics tools, and performance analytics tools. The specification section can also allow the user to specify a custom analytics tool. For example, it might provide a text field for the user to input settings for a custom analytics tool. This allows the specification section to generate code corresponding to the appropriate analytics tool based on the user's specified choice.

[0031] The code generation system includes a provider unit that provides the generated code. The provider unit provides an interface for providing the generated code to the user. For example, the provider unit generates a download link so that the user can download the generated code. For example, the provider unit compresses the generated code as a ZIP file and provides a download link. The provider unit can also provide the generated code via an API. For example, the provider unit sends the generated code to an API endpoint so that the user can obtain the code through the API. This allows the provider unit to provide the generated code to the user, making it easy for the user to use the code.

[0032] The code generation system includes a preview section that provides a preview function for the generated code. The preview section provides an interface for providing the preview function for the generated code. For example, the preview section provides a real-time preview function, allowing the user to instantly check the content of the generated code. For example, the preview section executes the generated code and displays the result on the screen. The preview section can also provide a static preview function. For example, the preview section displays the HTML structure and CSS styles of the generated code, allowing the user to check the content of the code. In this way, the preview section allows users to check the content of the code in advance by providing a preview function for the generated code.

[0033] The code generation system includes a modification unit for correcting the generated code. The modification unit provides an interface for modifying the generated code. For example, the modification unit may offer a GUI-based modification function, allowing users to intuitively modify the code. For instance, the modification unit may allow users to move code elements or change properties using drag-and-drop operations. The modification unit may also provide a code editor, allowing users to directly edit the code. For example, the modification unit may provide a code editor with syntax highlighting and autocomplete features. This makes it easier for the modification unit to correct parts of the generated code that handle specific data types.

[0034] The code generation system includes a security unit that implements security measures. The security unit provides an interface for implementing security measures for the generated code. For example, the security unit provides encryption functionality to protect sensitive information within the generated code. For instance, it encrypts API keys and passwords within the generated code. The security unit can also provide authentication functionality to control access to the generated code. For example, it performs user authentication, ensuring that only specific users can access the generated code. This allows the security unit to ensure the security of the generated code.

[0035] The loading unit can analyze the user's past loading history and select the optimal loading method during loading. For example, the loading unit can prioritize loading methods that the user has frequently used in the past. For instance, the loading unit can analyze the user's past access logs and propose the most efficient loading method. Furthermore, the loading unit can select the optimal loading method for a specific time period based on the user's past loading history. For example, the loading unit can propose the optimal loading method for a specific time period based on the user's behavioral history. In this way, by analyzing the user's past loading history, the optimal loading method can be provided.

[0036] The loading unit can filter the data based on the user's current project or area of ​​interest during loading. For example, it can prioritize loading only elements related to the project the user is currently working on. For instance, it can analyze data from the user's project management tool to identify relevant elements. The loading unit can also filter and load highly relevant elements based on the user's area of ​​interest. For example, it can analyze the user's browser history and bookmarks to identify elements related to their area of ​​interest. Furthermore, the loading unit can select and load necessary elements according to the progress of the user's project. For example, it can analyze the project's progress and prioritize loading elements needed for a specific phase. This allows for the priority loading of highly relevant elements by filtering based on the user's current project and area of ​​interest.

[0037] The loading unit can prioritize loading elements that are highly relevant based on the user's geographical location information during loading. For example, if the user is in a specific region, the loading unit will prioritize loading elements related to that region. For instance, the loading unit can analyze the user's GPS data to identify elements related to the region. The loading unit can also select and load the most relevant elements based on the user's geographical location information. For example, the loading unit can analyze the user's IP address to identify elements related to the region. Furthermore, if the user is on the move, the loading unit can prioritize loading necessary elements based on their current location. For example, the loading unit can analyze the user's travel history to identify elements related to their destination. In this way, by considering the user's geographical location information, the loading unit can prioritize loading elements that are highly relevant.

[0038] The loading unit can analyze the user's social media activity and load relevant elements during the loading process. For example, the loading unit prioritizes loading relevant elements based on the user's social media activity. For instance, it analyzes the user's social media posts and identifies relevant elements. The loading unit can also load elements related to topics the user has shown interest in on social media. For example, it prioritizes loading elements that the user's followers and friends are interested in. For instance, it analyzes the user's social media network and identifies relevant elements. This allows the loading unit to prioritize loading relevant elements by analyzing the user's social media activity.

[0039] The discrimination unit can improve the accuracy of its discrimination by considering the interrelationships between elements during discrimination. For example, the discrimination unit can analyze the interrelationships between elements and prioritize the discrimination of elements with high relevance. For example, the discrimination unit can analyze the dependencies between elements and identify elements with high relevance. The discrimination unit can also reduce misclassification by considering the interrelationships between elements. For example, the discrimination unit can score the relevance of elements to reduce the risk of misclassification. Furthermore, the discrimination unit can apply algorithms to improve the accuracy of discrimination based on the interrelationships between elements. For example, the discrimination unit can evaluate the relevance of elements and select the optimal discrimination method. In this way, the accuracy of discrimination can be improved by considering the interrelationships between elements.

[0040] The discrimination unit can perform discrimination by considering the attribute information of the element. For example, the discrimination unit performs discrimination based on the attribute information of the element (e.g., size, color, shape, etc.). For example, the discrimination unit analyzes the metadata of the element and identifies the attribute information. The discrimination unit can also analyze the attribute information of the element and select the most appropriate discrimination method. For example, the discrimination unit analyzes the tag information of the element and identifies the attribute information. Furthermore, the discrimination unit can improve the accuracy of discrimination by considering the attribute information of the element. For example, the discrimination unit evaluates the attribute information of the element and selects the optimal discrimination method. In this way, the accuracy of discrimination can be improved by considering the attribute information of the element.

[0041] The discrimination unit can perform discrimination while considering the geographical distribution of elements. For example, the discrimination unit can analyze the geographical distribution of elements and prioritize the discrimination of elements with high relevance. For example, the discrimination unit can analyze the regional distribution of elements and identify elements with high relevance. The discrimination unit can also select the most appropriate discrimination method based on the geographical distribution of elements. For example, the discrimination unit can analyze the country-specific distribution of elements and identify elements with high relevance. Furthermore, the discrimination unit can improve the accuracy of discrimination by considering the geographical distribution of elements. For example, the discrimination unit can evaluate the geographical distribution of elements and select the optimal discrimination method. In this way, the accuracy of discrimination can be improved by considering the geographical distribution of elements.

[0042] The discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the elements during the discrimination process. For example, the discrimination unit can refer to relevant literature for the elements to improve its discrimination accuracy. For example, the discrimination unit can refer to academic papers for the elements to identify highly relevant elements. The discrimination unit can also select the most appropriate discrimination method based on the relevant literature for the elements. For example, the discrimination unit can refer to technical reports for the elements to identify highly relevant elements. The discrimination unit can also apply algorithms to improve the accuracy of its discrimination by considering the relevant literature for the elements. For example, the discrimination unit can evaluate the relevant literature for the elements and select the optimal discrimination method. In this way, the accuracy of discrimination can be improved by referring to relevant literature for the elements.

[0043] The generation unit can adjust the level of detail of the generated code based on the importance of the elements during generation. For example, the generation unit can generate detailed code for important elements. For example, the generation unit can evaluate the importance of elements and generate detailed code. The generation unit can also generate simplified code for less important elements. For example, the generation unit can score the importance of elements and generate simplified code. The generation unit can also select the optimal level of detail of the code based on the importance of the elements. For example, the generation unit can evaluate the importance of elements and select the optimal level of detail of the code. This allows for efficient code generation by adjusting the level of detail of the generated code based on the importance of the elements.

[0044] The generation unit can apply different generation algorithms depending on the element category during generation. For example, for a button element, the generation unit generates code to track click events. For example, the generation unit analyzes the element category and applies the most suitable generation algorithm. The generation unit can also generate code to track submit events for form elements. For example, the generation unit evaluates the element category and applies the most suitable generation algorithm. The generation unit can also generate code to track click events for link elements. For example, the generation unit evaluates the element category and applies the most suitable generation algorithm. This enables efficient code generation by applying the most suitable generation algorithm according to the element category.

[0045] The generation unit can determine the priority of the code to be generated based on the submission timing of the elements during generation. For example, the generation unit will prioritize generating code for elements with approaching deadlines. For instance, the generation unit will evaluate the submission timing of elements and prioritize generating code for them. The generation unit can also postpone generating code for elements with later submission timings. For example, the generation unit will score the submission timing of elements and postpone generating code for them. Furthermore, the generation unit can determine the optimal code generation priority based on the submission timing of elements. For instance, the generation unit will evaluate the submission timing of elements and determine the optimal code generation priority. This enables efficient code generation by determining the priority of the code to be generated based on the submission timing of elements.

[0046] The generation unit can adjust the order in which it generates code based on the relationships between elements during generation. For example, the generation unit can prioritize generating code for highly relevant elements. For instance, it can evaluate the relationships between elements and prioritize generating code for them. The generation unit can also postpone generating code for less relevant elements. For example, it can score the relationships between elements and postpone generating code for them. Furthermore, the generation unit can determine the optimal order of code generation based on the relationships between elements. For instance, it can evaluate the relationships between elements and determine the optimal order of code generation. By adjusting the order in which code is generated based on the relationships between elements, efficient code generation becomes possible.

[0047] The selection function can select the optimal selection method by referring to the user's past selection history when making a selection. For example, the selection function can automatically display analysis tools that the user has frequently selected in the past as candidates. For example, the selection function can analyze the user's past selection history and suggest the optimal analysis tool. The selection function can also prioritize suggesting selection methods (voice, text, etc.) that the user has used in the past. For example, the selection function can analyze the user's past usage history and suggest the optimal selection method. Furthermore, the selection function can predict and suggest analysis tools to be used during specific time periods based on the user's past selection history. For example, the selection function can analyze the user's past usage history and suggest the optimal analysis tool for a specific time period. In this way, the optimal selection method can be provided by referring to the user's past selection history.

[0048] The selection unit can choose the most suitable analysis tool by considering the user's geographical location information at the time of selection. For example, if the user is in a specific region, the selection unit will prioritize selecting analysis tools related to that region. For example, the selection unit will analyze the user's GPS data to identify analysis tools related to the region. The selection unit can also select the most relevant analysis tool based on the user's geographical location information. For example, the selection unit will analyze the user's IP address to identify analysis tools related to the region. Furthermore, if the user is on the move, the selection unit can prioritize selecting the necessary analysis tools based on their current location. For example, the selection unit will analyze the user's travel history to identify analysis tools related to their destination. In this way, the optimal analysis tool can be provided by considering the user's geographical location information.

[0049] The service provider can select the optimal service delivery method by referring to the user's past delivery history at the time of delivery. For example, the service provider can prioritize delivery methods that the user has frequently used in the past. For example, the service provider can analyze the user's past delivery dates and propose the optimal delivery method. The service provider can also propose the most efficient delivery method based on the user's past delivery history. For example, the service provider can analyze the user's past delivery content and propose the optimal delivery method. Furthermore, the service provider can select the optimal delivery method for a specific time period based on the user's past delivery history. For example, the service provider can analyze the user's past delivery history and propose the optimal delivery method for a specific time period. In this way, the optimal delivery method can be provided by referring to the user's past delivery history.

[0050] The service provider can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, the service provider can analyze the user's device information and propose the optimal display method. Also, if the user is using a tablet, the service provider can provide a display method optimized for the larger screen. For example, the service provider can analyze the user's device information and propose the optimal display method. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. For example, the service provider can analyze the user's device information and propose the optimal display method. In this way, the service provider can provide the optimal delivery method by considering the user's device information.

[0051] The preview function can select the optimal preview method by referring to the user's past preview history during the preview process. For example, the preview function can prioritize preview methods that the user has frequently used in the past. For instance, it can analyze the user's past preview dates and times and suggest the optimal preview method. The preview function can also suggest the most efficient preview method based on the user's past preview history. For example, it can analyze the user's past preview content and suggest the optimal preview method. Furthermore, the preview function can select the optimal preview method for a specific time period based on the user's past preview history. For example, it can analyze the user's past preview history and suggest the optimal preview method for a specific time period. In this way, the optimal preview method can be provided by referring to the user's past preview history.

[0052] The preview section can select the optimal preview method by considering the user's device information during preview. For example, if the user is using a smartphone, the preview section can provide a preview that matches the screen size. For example, the preview section analyzes the user's device information and suggests the optimal preview method. The preview section can also provide a preview optimized for a larger screen if the user is using a tablet. For example, the preview section analyzes the user's device information and suggests the optimal preview method. Furthermore, if the user is using a smartwatch, the preview section can provide a concise and highly visible preview. For example, the preview section analyzes the user's device information and suggests the optimal preview method. In this way, the optimal preview method can be provided by considering the user's device information.

[0053] The correction unit can select the optimal correction method by referring to the user's past correction history during the correction process. For example, the correction unit can prioritize correction methods that the user has frequently used in the past. For example, the correction unit can analyze the user's past correction dates and propose the optimal correction method. The correction unit can also propose the most efficient correction method based on the user's past correction history. For example, the correction unit can analyze the user's past correction content and propose the optimal correction method. Furthermore, the correction unit can select the optimal correction method for a specific time period based on the user's past correction history. For example, the correction unit can analyze the user's past correction history and propose the optimal correction method for a specific time period. In this way, the optimal correction method can be provided by referring to the user's past correction history.

[0054] The correction unit can select the optimal correction method by considering the user's device information during the correction process. For example, if the user is using a smartphone, the correction unit can provide a correction method that matches the screen size. For example, the correction unit can analyze the user's device information and propose the optimal correction method. Furthermore, if the user is using a tablet, the correction unit can also provide a correction method optimized for a larger screen. For example, the correction unit can analyze the user's device information and propose the optimal correction method. Additionally, if the user is using a smartwatch, the correction unit can provide a concise and highly visible correction method. For example, the correction unit can analyze the user's device information and propose the optimal correction method. In this way, by considering the user's device information, the optimal correction method can be provided.

[0055] The security department can select the optimal security measures by referring to the user's past security history. For example, the security department can prioritize security measures that the user has frequently used in the past. For example, the security department can analyze the user's past security incidents and propose the optimal countermeasures. The security department can also propose the most efficient countermeasures based on the user's past security history. For example, the security department can analyze the user's past countermeasure history and propose the optimal countermeasures. Furthermore, the security department can select the optimal countermeasures for a specific time period based on the user's past security history. For example, the security department can analyze the user's past security history and propose the optimal countermeasures for a specific time period. In this way, the security department can provide the optimal countermeasures by referring to the user's past security history.

[0056] The security department can select the optimal security measures by considering the user's device information. For example, if the user is using a smartphone, the security department can provide security measures optimized for the device. For instance, the security department can analyze the user's device information and propose the most suitable security measures. Furthermore, if the user is using a tablet, the security department can provide security measures optimized for the larger screen. For example, the security department can analyze the user's device information and propose the most suitable security measures. Also, if the user is using a smartwatch, the security department can provide concise and highly visible security measures. For example, the security department can analyze the user's device information and propose the most suitable security measures. In this way, by considering the user's device information, the security department can provide the most optimal security measures.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The code generation system can also include a history analysis unit that analyzes the user's operation history. The history analysis unit records what operations the user has performed in the past and predicts the next operation based on that data. For example, the history analysis unit can identify buttons and links that the user frequently clicks and generate code preferentially for those elements. The history analysis unit can also analyze patterns of past operations the user has performed and predict the next operation. For example, if the history analysis unit finds that the user tends to perform certain operations at certain times of the day, it will generate the most suitable code for those times. In this way, the history analysis unit can improve user convenience by generating optimal code based on the user's operation history.

[0059] The code generation system may also include a feedback collection unit to gather user feedback. The feedback collection unit provides an interface that allows users to provide evaluations and comments on the generated code. For example, the feedback collection unit could provide an evaluation form for users to assess the quality and usability of the generated code. It could also provide a comment field where users can input specific areas for improvement or requests. For instance, the feedback collection unit could enable users to provide detailed feedback on specific parts of the generated code. This allows the feedback collection unit to improve the code generation system based on user feedback, thereby providing higher quality code.

[0060] The code generation system can also include an integration unit that connects with the user's project management tool. This integration unit works with the user's project management tool (e.g., JIRA or Trello) to generate optimal code according to the project's progress. For example, it can retrieve task information from the project management tool and prioritize generating code related to specific tasks. Furthermore, the integration unit can adjust the type and amount of code required according to the project's progress. For instance, it might generate basic code in the early stages of the project and more detailed code in later stages. This allows the integration unit, by working with the project management tool, to generate optimal code according to the project's progress.

[0061] The code generation system may also include an optimization unit that optimizes the generated code by taking into account the user's device information. The optimization unit optimizes the generated code according to the type and performance of the device the user is using. For example, if the user is using a smartphone, the optimization unit will generate code optimized for mobile devices. The optimization unit can also generate code that includes complex processing if the user is using a high-performance desktop PC. For example, the optimization unit will analyze the user's device information and generate the optimal code. The optimization unit can also generate lightweight code if the user is using a low-performance device. For example, the optimization unit will generate the optimal code based on the user's device information. In this way, the optimization unit can provide the optimal code by taking the user's device information into consideration.

[0062] The code generation system can also include a localization unit that localizes the generated code while considering the user's geographical location. The localization unit optimizes the generated code according to the user's region. For example, if the user is in Japan, the localization unit generates code in Japanese. It can also generate code in English if the user is in the United States. For example, the localization unit analyzes the user's geographical location and generates the optimal code. Furthermore, if the user is working on a multilingual project, the localization unit can generate code in multiple languages. For example, the localization unit generates the optimal code based on the user's geographical location. This allows the localization unit to provide optimal code by considering the user's geographical location.

[0063] The code generation system can also include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes the user's activities on social media and generates optimal code based on that data. For example, the social media analysis unit can identify topics and areas of interest that the user frequently posts about and generate related code. It can also analyze topics of interest to the user's followers and friends and generate related code. For example, it can analyze the user's social media network and generate optimal code. Furthermore, the social media analysis unit can improve the code based on user feedback on social media. For example, it can analyze user feedback and generate optimal code. This allows the social media analysis unit to provide optimal code based on the user's social media activity.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The loading section loads the website or application to be analyzed. For example, it can retrieve HTML data from a website using an HTTP request, or load application design files stored locally using a file loading function. It sends an HTTP request to a URL specified by the user and retrieves the HTML data returned as a response. It can also analyze design files uploaded by the user and extract the necessary data. Step 2: The discrimination unit automatically identifies the elements loaded by the loading unit. For example, it uses a machine learning algorithm to analyze the elements on the screen and identify which elements are the target of user interaction. It identifies elements such as buttons, links, and forms, and assigns appropriate tags to each element. It can also classify elements based on specific conditions using rule-based discrimination methods. It automatically identifies elements with specific CSS classes or IDs and identifies those elements as targets for interaction. Step 3: The generation unit generates code based on the elements identified by the discrimination unit. For example, it can generate code such as HTML, CSS, and JavaScript. Based on Google Analytics implementation rules, it generates JavaScript code to track click and scroll events. It can also generate code compatible with SEO and performance analysis tools. Based on SEO analysis tool implementation rules, it generates meta tags and structured data. Based on performance analysis tool implementation rules, it generates code to track page load time and resource usage.

[0066] (Example of form 2) The code generation system according to an embodiment of the present invention is a system that, upon loading a specified website, application, or its design, automatically identifies elements that can be interacted with on the screen and generates code to be embedded in the necessary locations based on the implementation rules of a specified analytics tool. This code generation system operates when a user loads the website, application, or its design to be analyzed into the service. The user can specify the type of analytics tool (e.g., Google Analytics). The system automatically identifies elements that can be interacted with on the screen of the loaded website or application (e.g., buttons, links, forms). AI technology is used for this identification. The AI ​​analyzes the elements on the screen and identifies which elements are targets for user interaction. Subsequently, the system generates code to be embedded in the necessary locations based on the implementation rules of the specified analytics tool. For example, in the case of Google Analytics, code for tracking click events and scroll events is generated. This code is appropriately embedded for each element. Finally, the generated code is provided to the user. By integrating this code into the website or application, the user can observe detailed user behavior. This significantly reduces the amount of code creation required, necessitating only modifications to parts of the code that handle specific data collection methods. This system simplifies user behavior analysis on websites and applications, reducing the burden on staff. For example, it eliminates the need to manually create tracking code for click and scroll events, allowing staff to focus on more critical analytical tasks. Furthermore, because AI automatically identifies elements and generates appropriate code, it's easy to use even for staff unfamiliar with the implementation rules of analytics tools. This system is particularly useful for those using web analytics tools such as Google Analytics. For instance, it allows for easy observation of which buttons or forms are most frequently clicked or used on specific pages of a website.This allows for a detailed understanding of user behavior, which can then be used to improve websites and applications. Furthermore, the code generation system automatically identifies elements of the website or application being analyzed and generates the necessary code, thus reducing the amount of coding work required.

[0067] The code generation system according to the embodiment comprises a reading unit, a discrimination unit, and a generation unit. The reading unit reads the website or application to be analyzed. The reading unit can, for example, obtain HTML data from the website using an HTTP request. The reading unit can also read application design files stored locally using a file reading function. For example, the reading unit sends an HTTP request to a URL specified by the user and obtains HTML data returned as a response. The reading unit can also analyze design files uploaded by the user and extract the necessary data. The discrimination unit automatically discriminates the elements read by the reading unit. The discrimination unit analyzes the elements on the screen using, for example, a machine learning algorithm and identifies which elements are the target of user interaction. For example, the discrimination unit identifies elements such as buttons, links, and forms and assigns appropriate tags to each element. The discrimination unit can also classify elements based on specific conditions using a rule-based discrimination method. For example, the discrimination unit automatically identifies elements with specific CSS classes or IDs and identifies those elements as targets for interaction. The generation unit generates code based on the elements identified by the discrimination unit. The generation unit can generate code such as HTML, CSS, and JavaScript. For example, it can generate JavaScript code to track click and scroll events based on Google Analytics implementation rules. It can also generate code compatible with SEO and performance analysis tools. For example, it can generate meta tags and structured data based on SEO analysis tool implementation rules. It can also generate code to track page load time and resource usage based on performance analysis tool implementation rules. As a result, the code generation system according to the embodiment reduces the amount of code creation work by automatically identifying elements of the website or application being analyzed and generating the code.

[0068] The code generation system includes a specification section for specifying the type of analytics tool. This specification section provides an interface for the user to specify the type of analytics tool. For example, the specification section might allow the user to select an analytics tool using a dropdown menu or radio buttons. For instance, it might offer options such as Google Analytics, SEO analytics tools, and performance analytics tools. The specification section can also allow the user to specify a custom analytics tool. For example, it might provide a text field for the user to input settings for a custom analytics tool. This allows the specification section to generate code corresponding to the appropriate analytics tool based on the user's specified choice.

[0069] The code generation system includes a provider unit that provides the generated code. The provider unit provides an interface for providing the generated code to the user. For example, the provider unit generates a download link so that the user can download the generated code. For example, the provider unit compresses the generated code as a ZIP file and provides a download link. The provider unit can also provide the generated code via an API. For example, the provider unit sends the generated code to an API endpoint so that the user can obtain the code through the API. This allows the provider unit to provide the generated code to the user, making it easy for the user to use the code.

[0070] The code generation system includes a preview section that provides a preview function for the generated code. The preview section provides an interface for providing the preview function for the generated code. For example, the preview section provides a real-time preview function, allowing the user to instantly check the content of the generated code. For example, the preview section executes the generated code and displays the result on the screen. The preview section can also provide a static preview function. For example, the preview section displays the HTML structure and CSS styles of the generated code, allowing the user to check the content of the code. In this way, the preview section allows users to check the content of the code in advance by providing a preview function for the generated code.

[0071] The code generation system includes a modification unit for correcting the generated code. The modification unit provides an interface for modifying the generated code. For example, the modification unit may offer a GUI-based modification function, allowing users to intuitively modify the code. For instance, the modification unit may allow users to move code elements or change properties using drag-and-drop operations. The modification unit may also provide a code editor, allowing users to directly edit the code. For example, the modification unit may provide a code editor with syntax highlighting and autocomplete features. This makes it easier for the modification unit to correct parts of the generated code that handle specific data types.

[0072] The code generation system includes a security unit that implements security measures. The security unit provides an interface for implementing security measures for the generated code. For example, the security unit provides encryption functionality to protect sensitive information within the generated code. For instance, it encrypts API keys and passwords within the generated code. The security unit can also provide authentication functionality to control access to the generated code. For example, it performs user authentication, ensuring that only specific users can access the generated code. This allows the security unit to ensure the security of the generated code.

[0073] The reading unit can estimate the user's emotions and adjust the reading timing based on the estimated emotions. For example, if the user is stressed, the reading unit will speed up the reading to minimize waiting time. For example, the reading unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Also, if the user is relaxed, the reading unit can slow down the reading and display detailed reading information. For example, the reading unit can record the user's voice and estimate their emotions using voice analysis technology. Also, if the user is in a hurry, the reading unit can start reading immediately and display the progress in real time. For example, the reading unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the reading timing to be adjusted according to the user's emotions, thereby reducing user stress. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0074] The loading unit can analyze the user's past loading history and select the optimal loading method during loading. For example, the loading unit can prioritize loading methods that the user has frequently used in the past. For instance, the loading unit can analyze the user's past access logs and propose the most efficient loading method. Furthermore, the loading unit can select the optimal loading method for a specific time period based on the user's past loading history. For example, the loading unit can propose the optimal loading method for a specific time period based on the user's behavioral history. In this way, by analyzing the user's past loading history, the optimal loading method can be provided.

[0075] The loading unit can filter the data based on the user's current project or area of ​​interest during loading. For example, it can prioritize loading only elements related to the project the user is currently working on. For instance, it can analyze data from the user's project management tool to identify relevant elements. The loading unit can also filter and load highly relevant elements based on the user's area of ​​interest. For example, it can analyze the user's browser history and bookmarks to identify elements related to their area of ​​interest. Furthermore, the loading unit can select and load necessary elements according to the progress of the user's project. For example, it can analyze the project's progress and prioritize loading elements needed for a specific phase. This allows for the priority loading of highly relevant elements by filtering based on the user's current project and area of ​​interest.

[0076] The reading unit can estimate the user's emotions and determine the priority of elements to read based on the estimated emotions. For example, if the user is stressed, the reading unit will prioritize reading important elements. For example, the reading unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Also, if the user is relaxed, the reading unit can prioritize reading detailed elements. For example, the reading unit may record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the reading unit can prioritize reading the most necessary elements. For example, the reading unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for reading tailored to the user's needs by determining the priority of elements to read according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0077] The loading unit can prioritize loading elements that are highly relevant based on the user's geographical location information during loading. For example, if the user is in a specific region, the loading unit will prioritize loading elements related to that region. For instance, the loading unit can analyze the user's GPS data to identify elements related to the region. The loading unit can also select and load the most relevant elements based on the user's geographical location information. For example, the loading unit can analyze the user's IP address to identify elements related to the region. Furthermore, if the user is on the move, the loading unit can prioritize loading necessary elements based on their current location. For example, the loading unit can analyze the user's travel history to identify elements related to their destination. In this way, by considering the user's geographical location information, the loading unit can prioritize loading elements that are highly relevant.

[0078] The loading unit can analyze the user's social media activity and load relevant elements during the loading process. For example, the loading unit prioritizes loading relevant elements based on the user's social media activity. For instance, it analyzes the user's social media posts and identifies relevant elements. The loading unit can also load elements related to topics the user has shown interest in on social media. For example, it prioritizes loading elements that the user's followers and friends are interested in. For instance, it analyzes the user's social media network and identifies relevant elements. This allows the loading unit to prioritize loading relevant elements by analyzing the user's social media activity.

[0079] The discrimination unit can estimate the user's emotions and adjust the discrimination criteria based on the estimated emotions. For example, if the user is stressed, the discrimination unit can make a judgment using simple criteria. For example, the discrimination unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The discrimination unit can also make a judgment using detailed criteria if the user is relaxed. For example, the discrimination unit can record the user's voice and estimate the emotion using voice analysis technology. The discrimination unit can also set criteria for quick judgment if the user is in a hurry. For example, the discrimination unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for discrimination tailored to the user's needs by adjusting the discrimination criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0080] The discrimination unit can improve the accuracy of its discrimination by considering the interrelationships between elements during discrimination. For example, the discrimination unit can analyze the interrelationships between elements and prioritize the discrimination of elements with high relevance. For example, the discrimination unit can analyze the dependencies between elements and identify elements with high relevance. The discrimination unit can also reduce misclassification by considering the interrelationships between elements. For example, the discrimination unit can score the relevance of elements to reduce the risk of misclassification. Furthermore, the discrimination unit can apply algorithms to improve the accuracy of discrimination based on the interrelationships between elements. For example, the discrimination unit can evaluate the relevance of elements and select the optimal discrimination method. In this way, the accuracy of discrimination can be improved by considering the interrelationships between elements.

[0081] The discrimination unit can perform discrimination by considering the attribute information of the element. For example, the discrimination unit performs discrimination based on the attribute information of the element (e.g., size, color, shape, etc.). For example, the discrimination unit analyzes the metadata of the element and identifies the attribute information. The discrimination unit can also analyze the attribute information of the element and select the most appropriate discrimination method. For example, the discrimination unit analyzes the tag information of the element and identifies the attribute information. Furthermore, the discrimination unit can improve the accuracy of discrimination by considering the attribute information of the element. For example, the discrimination unit evaluates the attribute information of the element and selects the optimal discrimination method. In this way, the accuracy of discrimination can be improved by considering the attribute information of the element.

[0082] The discrimination unit can estimate the user's emotions and adjust the display order of the discrimination results based on the estimated emotions. For example, if the user is feeling stressed, the discrimination unit will prioritize displaying important discrimination results. For example, the discrimination unit may capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The discrimination unit can also prioritize displaying detailed discrimination results if the user is relaxed. For example, the discrimination unit may record the user's voice and estimate the emotion using voice analysis technology. The discrimination unit can also prioritize displaying the most necessary discrimination results if the user is in a hurry. For example, the discrimination unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for display tailored to the user's needs by adjusting the display order of discrimination results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0083] The discrimination unit can perform discrimination while considering the geographical distribution of elements. For example, the discrimination unit can analyze the geographical distribution of elements and prioritize the discrimination of elements with high relevance. For example, the discrimination unit can analyze the regional distribution of elements and identify elements with high relevance. The discrimination unit can also select the most appropriate discrimination method based on the geographical distribution of elements. For example, the discrimination unit can analyze the country-specific distribution of elements and identify elements with high relevance. Furthermore, the discrimination unit can improve the accuracy of discrimination by considering the geographical distribution of elements. For example, the discrimination unit can evaluate the geographical distribution of elements and select the optimal discrimination method. In this way, the accuracy of discrimination can be improved by considering the geographical distribution of elements.

[0084] The discrimination unit can improve the accuracy of its discrimination by referring to relevant literature for the elements during the discrimination process. For example, the discrimination unit can refer to relevant literature for the elements to improve its discrimination accuracy. For example, the discrimination unit can refer to academic papers for the elements to identify highly relevant elements. The discrimination unit can also select the most appropriate discrimination method based on the relevant literature for the elements. For example, the discrimination unit can refer to technical reports for the elements to identify highly relevant elements. The discrimination unit can also apply algorithms to improve the accuracy of its discrimination by considering the relevant literature for the elements. For example, the discrimination unit can evaluate the relevant literature for the elements and select the optimal discrimination method. In this way, the accuracy of discrimination can be improved by referring to relevant literature for the elements.

[0085] The generation unit can estimate the user's emotions and adjust the way the generated code is expressed based on the estimated emotions. For example, if the user is stressed, the generation unit will generate simple and easy-to-understand code. For example, the generation unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also generate code with detailed comments if the user is relaxed. For example, the generation unit may record the user's voice and estimate their emotions using voice analysis technology. The generation unit can also generate code that can be quickly implemented if the user is in a hurry. For example, the generation unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the generation unit to provide code that is easy for the user to understand by adjusting the way the generated code is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0086] The generation unit can adjust the level of detail of the generated code based on the importance of the elements during generation. For example, the generation unit can generate detailed code for important elements. For example, the generation unit can evaluate the importance of elements and generate detailed code. The generation unit can also generate simplified code for less important elements. For example, the generation unit can score the importance of elements and generate simplified code. The generation unit can also select the optimal level of detail of the code based on the importance of the elements. For example, the generation unit can evaluate the importance of elements and select the optimal level of detail of the code. This allows for efficient code generation by adjusting the level of detail of the generated code based on the importance of the elements.

[0087] The generation unit can apply different generation algorithms depending on the element category during generation. For example, for a button element, the generation unit generates code to track click events. For example, the generation unit analyzes the element category and applies the most suitable generation algorithm. The generation unit can also generate code to track submit events for form elements. For example, the generation unit evaluates the element category and applies the most suitable generation algorithm. The generation unit can also generate code to track click events for link elements. For example, the generation unit evaluates the element category and applies the most suitable generation algorithm. This enables efficient code generation by applying the most suitable generation algorithm according to the element category.

[0088] The generation unit can estimate the user's emotions and adjust the length of the generated code based on the estimated emotions. For example, if the user is stressed, the generation unit will generate short, concise code. For example, the generation unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The generation unit can also generate longer code with more detailed explanations if the user is relaxed. For example, the generation unit may record the user's voice and estimate their emotions using voice analysis technology. The generation unit can also generate short, quickly implementable code if the user is in a hurry. For example, the generation unit may collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the length of the generated code according to the user's emotions, it is possible to provide code that is easy for the user to understand. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0089] The generation unit can determine the priority of the code to be generated based on the submission timing of the elements during generation. For example, the generation unit will prioritize generating code for elements with approaching deadlines. For instance, the generation unit will evaluate the submission timing of elements and prioritize generating code for them. The generation unit can also postpone generating code for elements with later submission timings. For example, the generation unit will score the submission timing of elements and postpone generating code for them. Furthermore, the generation unit can determine the optimal code generation priority based on the submission timing of elements. For instance, the generation unit will evaluate the submission timing of elements and determine the optimal code generation priority. This enables efficient code generation by determining the priority of the code to be generated based on the submission timing of elements.

[0090] The generation unit can adjust the order in which it generates code based on the relationships between elements during generation. For example, the generation unit can prioritize generating code for highly relevant elements. For instance, it can evaluate the relationships between elements and prioritize generating code for them. The generation unit can also postpone generating code for less relevant elements. For example, it can score the relationships between elements and postpone generating code for them. Furthermore, the generation unit can determine the optimal order of code generation based on the relationships between elements. For instance, it can evaluate the relationships between elements and determine the optimal order of code generation. By adjusting the order in which code is generated based on the relationships between elements, efficient code generation becomes possible.

[0091] The selection unit can estimate the user's emotions and adjust the method of selecting the analysis tool based on the estimated emotions. For example, if the user is stressed, the selection unit can provide a simple interface and minimize the steps required to select the analysis tool. For example, the selection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is relaxed, the selection unit can also provide detailed selection options and suggest a customizable selection method. For example, the selection unit can record the user's voice and estimate their emotions using voice analysis technology. If the user is in a hurry, the selection unit can prioritize voice input to allow for quick selection of the analysis tool. For example, the selection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly system by adjusting the method of selecting the analysis tool according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0092] The selection function can select the optimal selection method by referring to the user's past selection history when making a selection. For example, the selection function can automatically display analysis tools that the user has frequently selected in the past as candidates. For example, the selection function can analyze the user's past selection history and suggest the optimal analysis tool. The selection function can also prioritize suggesting selection methods (voice, text, etc.) that the user has used in the past. For example, the selection function can analyze the user's past usage history and suggest the optimal selection method. Furthermore, the selection function can predict and suggest analysis tools to be used during specific time periods based on the user's past selection history. For example, the selection function can analyze the user's past usage history and suggest the optimal analysis tool for a specific time period. In this way, the optimal selection method can be provided by referring to the user's past selection history.

[0093] The system can estimate the user's emotions and prioritize analysis tools based on those emotions. For example, if the user is stressed, the system will prioritize important analysis tools. For instance, it might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is relaxed, the system can prioritize detailed analysis tools. For example, it might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the system can prioritize the most necessary analysis tools. For example, it might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly system by prioritizing analysis tools according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The selection unit can choose the most suitable analysis tool by considering the user's geographical location information at the time of selection. For example, if the user is in a specific region, the selection unit will prioritize selecting analysis tools related to that region. For example, the selection unit will analyze the user's GPS data to identify analysis tools related to the region. The selection unit can also select the most relevant analysis tool based on the user's geographical location information. For example, the selection unit will analyze the user's IP address to identify analysis tools related to the region. Furthermore, if the user is on the move, the selection unit can prioritize selecting the necessary analysis tools based on their current location. For example, the selection unit will analyze the user's travel history to identify analysis tools related to their destination. In this way, the optimal analysis tool can be provided by considering the user's geographical location information.

[0095] The service provider can estimate the user's emotions and adjust the display method of the code based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display method. For example, the service provider can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The service provider can also provide a display method that includes detailed information if the user is relaxed. For example, the service provider can record the user's voice and estimate the emotion using voice analysis technology. The service provider can also provide a concise display method if the user is in a hurry. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for a display that is easy for the user to understand by adjusting the display method of the code according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The service provider can select the optimal service delivery method by referring to the user's past delivery history at the time of delivery. For example, the service provider can prioritize delivery methods that the user has frequently used in the past. For example, the service provider can analyze the user's past delivery dates and propose the optimal delivery method. The service provider can also propose the most efficient delivery method based on the user's past delivery history. For example, the service provider can analyze the user's past delivery content and propose the optimal delivery method. Furthermore, the service provider can select the optimal delivery method for a specific time period based on the user's past delivery history. For example, the service provider can analyze the user's past delivery history and propose the optimal delivery method for a specific time period. In this way, the optimal delivery method can be provided by referring to the user's past delivery history.

[0097] The service provider can estimate the user's emotions and determine the priority of the codes to provide based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize providing important codes. For example, the service provider may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also prioritize providing detailed codes if the user is relaxed. For example, the service provider may record the user's voice and estimate their emotions using voice analysis technology. The service provider can also prioritize providing the most necessary codes if the user is in a hurry. For example, the service provider may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the service provider to provide a user-friendly system by prioritizing the codes to provide according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The service provider can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, the service provider can analyze the user's device information and propose the optimal display method. Also, if the user is using a tablet, the service provider can provide a display method optimized for the larger screen. For example, the service provider can analyze the user's device information and propose the optimal display method. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. For example, the service provider can analyze the user's device information and propose the optimal display method. In this way, the service provider can provide the optimal delivery method by considering the user's device information.

[0099] The preview unit can estimate the user's emotions and adjust the preview display method based on the estimated emotions. For example, if the user is stressed, the preview unit can provide a simple and highly visible preview. For example, the preview unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The preview unit can also provide a preview with detailed information if the user is relaxed. For example, the preview unit can record the user's voice and estimate their emotions using voice analysis technology. The preview unit can also provide a concise preview if the user is in a hurry. For example, the preview unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly display by adjusting the preview display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0100] The preview function can select the optimal preview method by referring to the user's past preview history during the preview process. For example, the preview function can prioritize preview methods that the user has frequently used in the past. For instance, it can analyze the user's past preview dates and times and suggest the optimal preview method. The preview function can also suggest the most efficient preview method based on the user's past preview history. For example, it can analyze the user's past preview content and suggest the optimal preview method. Furthermore, the preview function can select the optimal preview method for a specific time period based on the user's past preview history. For example, it can analyze the user's past preview history and suggest the optimal preview method for a specific time period. In this way, the optimal preview method can be provided by referring to the user's past preview history.

[0101] The preview unit can estimate the user's emotions and prioritize previews based on those emotions. For example, if the user is stressed, the preview unit will prioritize displaying important previews. For instance, the preview unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The preview unit can also prioritize displaying detailed previews if the user is relaxed. For example, the preview unit might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the preview unit can prioritize displaying the most necessary previews. For example, the preview unit might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly system by prioritizing previews according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The preview section can select the optimal preview method by considering the user's device information during preview. For example, if the user is using a smartphone, the preview section can provide a preview that matches the screen size. For example, the preview section analyzes the user's device information and suggests the optimal preview method. The preview section can also provide a preview optimized for a larger screen if the user is using a tablet. For example, the preview section analyzes the user's device information and suggests the optimal preview method. Furthermore, if the user is using a smartwatch, the preview section can provide a concise and highly visible preview. For example, the preview section analyzes the user's device information and suggests the optimal preview method. In this way, the optimal preview method can be provided by considering the user's device information.

[0103] The correction unit can estimate the user's emotions and adjust the correction method based on the estimated emotions. For example, if the user is stressed, the correction unit can provide a simple correction method. For example, the correction unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. The correction unit can also provide more detailed correction options if the user is relaxed. For example, the correction unit can record the user's voice and estimate the emotion using voice analysis technology. The correction unit can also provide a quick correction method if the user is in a hurry. For example, the correction unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate the emotion using an emotion estimation algorithm. This allows for a user-friendly system by adjusting the correction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The correction unit can select the optimal correction method by referring to the user's past correction history during the correction process. For example, the correction unit can prioritize correction methods that the user has frequently used in the past. For example, the correction unit can analyze the user's past correction dates and propose the optimal correction method. The correction unit can also propose the most efficient correction method based on the user's past correction history. For example, the correction unit can analyze the user's past correction content and propose the optimal correction method. Furthermore, the correction unit can select the optimal correction method for a specific time period based on the user's past correction history. For example, the correction unit can analyze the user's past correction history and propose the optimal correction method for a specific time period. In this way, the optimal correction method can be provided by referring to the user's past correction history.

[0105] The editing unit can estimate the user's emotions and determine the priority of corrections based on the estimated emotions. For example, if the user is stressed, the editing unit will prioritize important corrections. For example, the editing unit may capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The editing unit can also prioritize detailed corrections if the user is relaxed. For example, the editing unit may record the user's voice and estimate their emotions using voice analysis technology. The editing unit can also prioritize the most necessary corrections if the user is in a hurry. For example, the editing unit may collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly system by determining the priority of corrections according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0106] The correction unit can select the optimal correction method by considering the user's device information during the correction process. For example, if the user is using a smartphone, the correction unit can provide a correction method that matches the screen size. For example, the correction unit can analyze the user's device information and propose the optimal correction method. Furthermore, if the user is using a tablet, the correction unit can also provide a correction method optimized for a larger screen. For example, the correction unit can analyze the user's device information and propose the optimal correction method. Additionally, if the user is using a smartwatch, the correction unit can provide a concise and highly visible correction method. For example, the correction unit can analyze the user's device information and propose the optimal correction method. In this way, by considering the user's device information, the optimal correction method can be provided.

[0107] The security department can estimate the user's emotions and adjust security measures based on those emotions. For example, if the user is stressed, the security department can provide simple and quick security measures. For instance, it can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The security department can also provide more detailed security measures if the user is relaxed. For example, it can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the security department can provide security measures that can be implemented quickly. For example, it can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly system by adjusting security measures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The security department can select the optimal security measures by referring to the user's past security history. For example, the security department can prioritize security measures that the user has frequently used in the past. For example, the security department can analyze the user's past security incidents and propose the optimal countermeasures. The security department can also propose the most efficient countermeasures based on the user's past security history. For example, the security department can analyze the user's past countermeasure history and propose the optimal countermeasures. Furthermore, the security department can select the optimal countermeasures for a specific time period based on the user's past security history. For example, the security department can analyze the user's past security history and propose the optimal countermeasures for a specific time period. In this way, the security department can provide the optimal countermeasures by referring to the user's past security history.

[0109] The security department can estimate the user's emotions and prioritize security measures based on those estimates. For example, if the user is stressed, the security department will prioritize critical security measures. For instance, the security department might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Similarly, if the user is relaxed, the security department can prioritize more detailed security measures. For example, the security department might record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the security department can prioritize the most necessary security measures. For example, the security department might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a user-friendly system by prioritizing security measures according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0110] The security department can select the optimal security measures by considering the user's device information. For example, if the user is using a smartphone, the security department can provide security measures optimized for the device. For instance, the security department can analyze the user's device information and propose the most suitable security measures. Furthermore, if the user is using a tablet, the security department can provide security measures optimized for the larger screen. For example, the security department can analyze the user's device information and propose the most suitable security measures. Also, if the user is using a smartwatch, the security department can provide concise and highly visible security measures. For example, the security department can analyze the user's device information and propose the most suitable security measures. In this way, by considering the user's device information, the security department can provide the most optimal security measures. === Hard Collateral 1-1 === Each of the multiple elements described above, including the reading unit, discrimination unit, generation unit, specification unit, provision unit, preview unit, modification unit, and security unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reading unit is implemented by the control unit 46A of the smart device 14 and obtains HTML data from a website using an HTTP request. The discrimination unit is implemented by the identification processing unit 290 of the data processing device 12 and analyzes elements on the screen using a machine learning algorithm. The generation unit is implemented by the identification processing unit 290 of the data processing device 12 and generates code based on the implementation rules of Google Analytics. The specification unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to specify the type of analysis tool. The provision unit is implemented by the control unit 46A of the smart device 14 and provides an interface for providing the generated code to the user. The preview unit is implemented by the control unit 46A of the smart device 14 and provides a preview function for the generated code. The modification unit is implemented by the control unit 46A of the smart device 14 and provides an interface for modifying the generated code. The security unit is implemented by the specific processing unit 290 of the data processing device 12 and performs security measures for the generated code. === Hard Collateral 1-2 === Each of the multiple elements described above, including the reading unit, discrimination unit, generation unit, specification unit, provision unit, preview unit, modification unit, and security unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the smart glasses 214 and obtains HTML data from a website using an HTTP request. The discrimination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes elements on the screen using a machine learning algorithm. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates code based on the implementation rules of Google Analytics. The specification unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to specify the type of analysis tool. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for providing the generated code to the user. The preview unit is implemented by the control unit 46A of the smart glasses 214 and provides a preview function for the generated code. The modification unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for modifying the generated code. The security unit is implemented by the specific processing unit 290 of the data processing device 12 and implements security measures for the generated code. === Hard Collateral 1-3 === Each of the multiple elements described above, including the reading unit, discrimination unit, generation unit, specification unit, provision unit, preview unit, modification unit, and security unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the headset terminal 314 and obtains HTML data from a website using an HTTP request. The discrimination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes elements on the screen using a machine learning algorithm. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates code based on the implementation rules of Google Analytics. The specification unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to specify the type of analysis tool. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for providing the generated code to the user. The preview unit is implemented by the control unit 46A of the headset terminal 314 and provides a preview function for the generated code. The modification unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for modifying the generated code. The security unit is implemented by the specific processing unit 290 of the data processing device 12 and implements security measures for the generated code. === Hard Collateral 1-4 === Each of the multiple elements described above, including the reading unit, discrimination unit, generation unit, specification unit, provision unit, preview unit, modification unit, and security unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the robot 414 and obtains HTML data from a website using an HTTP request. The discrimination unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes elements on the screen using a machine learning algorithm. The generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates code based on the implementation rules of Google Analytics. The specification unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to specify the type of analysis tool. The provision unit is implemented by the control unit 46A of the robot 414 and provides an interface for providing the generated code to the user. The preview unit is implemented by the control unit 46A of the robot 414 and provides a preview function for the generated code. The modification unit is implemented by the control unit 46A of the robot 414 and provides an interface for modifying the generated code. The security unit is implemented by the specific processing unit 290 of the data processing device 12 and performs security measures for the generated code.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The code generation system can also include a history analysis unit that analyzes the user's operation history. The history analysis unit records what operations the user has performed in the past and predicts the next operation based on that data. For example, the history analysis unit can identify buttons and links that the user frequently clicks and generate code preferentially for those elements. The history analysis unit can also analyze patterns of past operations the user has performed and predict the next operation. For example, if the history analysis unit finds that the user tends to perform certain operations at certain times of the day, it will generate the most suitable code for those times. In this way, the history analysis unit can improve user convenience by generating optimal code based on the user's operation history.

[0113] The code generation system may also include a feedback collection unit to gather user feedback. The feedback collection unit provides an interface that allows users to provide evaluations and comments on the generated code. For example, the feedback collection unit could provide an evaluation form for users to assess the quality and usability of the generated code. It could also provide a comment field where users can input specific areas for improvement or requests. For instance, the feedback collection unit could enable users to provide detailed feedback on specific parts of the generated code. This allows the feedback collection unit to improve the code generation system based on user feedback, thereby providing higher quality code.

[0114] The code generation system can further estimate the user's emotions and adjust the style of the generated code based on those emotions. For example, if the user is stressed, it can generate simple and easy-to-read code. For instance, the generation unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is relaxed, it can also generate code that includes detailed comments and explanations. For example, the generation unit might record the user's voice and estimate their emotions using voice analysis technology. If the user is in a hurry, it can also generate short code that can be quickly implemented. For example, the generation unit might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the style of the generated code according to the user's emotions, the system can provide code that is easy for the user to understand.

[0115] The code generation system can also include an integration unit that connects with the user's project management tool. This integration unit works with the user's project management tool (e.g., JIRA or Trello) to generate optimal code according to the project's progress. For example, it can retrieve task information from the project management tool and prioritize generating code related to specific tasks. Furthermore, the integration unit can adjust the type and amount of code required according to the project's progress. For instance, it might generate basic code in the early stages of the project and more detailed code in later stages. This allows the integration unit, by working with the project management tool, to generate optimal code according to the project's progress.

[0116] The code generation system can further estimate the user's emotions and adjust the error handling of the generated code based on those estimated emotions. For example, if the user is stressed, the error handling can be simplified and the error messages clearer. For instance, the generation unit could capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is relaxed, the system can perform more detailed error handling and include specific solutions in the error messages. For example, the generation unit could record the user's voice and estimate their emotions using voice analysis technology. If the user is in a hurry, the system can minimize error handling and allow for quick error resolution. For example, the generation unit could collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the error handling of the generated code according to the user's emotions, the system can provide user-friendly code.

[0117] The code generation system may also include an optimization unit that optimizes the generated code by taking into account the user's device information. The optimization unit optimizes the generated code according to the type and performance of the device the user is using. For example, if the user is using a smartphone, the optimization unit will generate code optimized for mobile devices. The optimization unit can also generate code that includes complex processing if the user is using a high-performance desktop PC. For example, the optimization unit will analyze the user's device information and generate the optimal code. The optimization unit can also generate lightweight code if the user is using a low-performance device. For example, the optimization unit will generate the optimal code based on the user's device information. In this way, the optimization unit can provide the optimal code by taking the user's device information into consideration.

[0118] The code generation system can further estimate the user's emotions and adjust the testing method of the generated code based on those estimated emotions. For example, if the user is stressed, a simple testing method can be provided. For instance, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is relaxed, a more detailed testing method can be provided. For example, the generation unit can record the user's voice and estimate their emotions using voice analysis technology. If the user is in a hurry, a method for rapid testing can be provided. For example, the generation unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the testing method of the generated code according to the user's emotions, a user-friendly system can be provided.

[0119] The code generation system can also include a localization unit that localizes the generated code while considering the user's geographical location. The localization unit optimizes the generated code according to the user's region. For example, if the user is in Japan, the localization unit generates code in Japanese. It can also generate code in English if the user is in the United States. For example, the localization unit analyzes the user's geographical location and generates the optimal code. Furthermore, if the user is working on a multilingual project, the localization unit can generate code in multiple languages. For example, the localization unit generates the optimal code based on the user's geographical location. This allows the localization unit to provide optimal code by considering the user's geographical location.

[0120] The code generation system can further estimate the user's emotions and adjust the code documentation based on those emotions. For example, if the user is stressed, it can generate concise and to-the-point documentation. For instance, the generation unit might capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the user is relaxed, it can also generate documentation with detailed explanations. For example, the generation unit might record the user's voice and estimate their emotions using voice analysis technology. If the user is in a hurry, it can generate concise documentation that can be quickly understood. For example, the generation unit might collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. By adjusting the code documentation according to the user's emotions, the system can provide documentation that is easy for the user to understand.

[0121] The code generation system can also include a social media analysis unit that analyzes the user's social media activity. The social media analysis unit analyzes the user's activities on social media and generates optimal code based on that data. For example, the social media analysis unit can identify topics and areas of interest that the user frequently posts about and generate related code. It can also analyze topics of interest to the user's followers and friends and generate related code. For example, it can analyze the user's social media network and generate optimal code. Furthermore, the social media analysis unit can improve the code based on user feedback on social media. For example, it can analyze user feedback and generate optimal code. This allows the social media analysis unit to provide optimal code based on the user's social media activity.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The loading section loads the website or application to be analyzed. For example, it can retrieve HTML data from a website using an HTTP request, or load application design files stored locally using a file loading function. It sends an HTTP request to a URL specified by the user and retrieves the HTML data returned as a response. It can also analyze design files uploaded by the user and extract the necessary data. Step 2: The discrimination unit automatically identifies the elements loaded by the loading unit. For example, it uses a machine learning algorithm to analyze the elements on the screen and identify which elements are the target of user interaction. It identifies elements such as buttons, links, and forms, and assigns appropriate tags to each element. It can also classify elements based on specific conditions using rule-based discrimination methods. It automatically identifies elements with specific CSS classes or IDs and identifies those elements as targets for interaction. Step 3: The generation unit generates code based on the elements identified by the discrimination unit. For example, it can generate code such as HTML, CSS, and JavaScript. Based on Google Analytics implementation rules, it generates JavaScript code to track click and scroll events. It can also generate code compatible with SEO and performance analysis tools. Based on SEO analysis tool implementation rules, it generates meta tags and structured data. Based on performance analysis tool implementation rules, it generates code to track page load time and resource usage.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (for example, still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or Naive Bayes, and can perform a variety of operations, but is not limited to these examples. Furthermore, AI may also be an AI agent. Also, when the operations described above are performed by AI, the operations may be performed partially or entirely by AI, but is not limited to these examples. Additionally, operations performed by AI, including generative AI, may be replaced by rule-based operations, and rule-based operations may be replaced by operations performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 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.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, 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 specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A loading unit that loads the website or application to be analyzed, A discrimination unit that automatically identifies the elements read by the aforementioned reading unit, A generation unit that generates a code based on the elements determined by the discrimination unit, Equipped with A system characterized by the following features.

2. It includes a section for specifying the type of analysis tool. The system according to feature 1.

3. It includes a provisioning unit that provides the generated code. The system according to feature 1.

4. It includes a preview section that provides a preview function for the generated code. The system according to feature 1.

5. It includes a correction section for modifying the generated code. The system according to feature 1.

6. The company has a security department that handles security measures. The system according to feature 1.

7. The aforementioned reading unit, It estimates the user's emotions and adjusts the loading timing based on those emotions. The system according to feature 1.

8. The aforementioned reading unit, During loading, the system analyzes the user's past loading history and selects the appropriate loading method. The system according to feature 1.

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