system

Abstract

The system according to this embodiment aims to efficiently migrate websites created with older no-code tools to the latest cloud-based web development platform. [Solution] The system according to the embodiment comprises a reading unit, a conversion unit, and an update unit. The reading unit reads data from a website created with an older no-code tool. The conversion unit converts the data read by the reading unit into a format optimized for the latest cloud-based web construction platform. The update unit updates the data converted by the conversion unit to the new environment.

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 in 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, when migrating a website created with an old no-code tool to the latest cloud-based web construction platform, it is time-consuming and difficult to perform efficiently.

[0005] The system according to the embodiment aims to efficiently migrate a website created with an old no-code tool to the latest cloud-based web construction platform.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, a conversion unit, and an update unit. The reading unit reads data from a website created with an older no-code tool. The conversion unit converts the data read by the reading unit into a format optimized for the latest cloud-based web development platform. The update unit updates the data converted by the conversion unit to the new environment. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently migrate websites created with older no-code tools to the latest cloud-based web development platform. [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] 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 Easy Website Migration Pack, according to an embodiment of the present invention, is a system for migrating websites created with older no-code tools to the latest cloud-based web development platform. This system reads data such as text and images from websites created with older no-code tools, converts the read data into a format optimized for the latest cloud-based web development platform, and updates the converted data to the new environment. For example, the Easy Website Migration Pack uses a data reading unit that supports the format of older tools to accurately acquire data from websites created with older no-code tools. For example, it can read data from websites created with tools such as "Homepage Builder" and the "Bind series." Next, the Easy Website Migration Pack converts the read data into a format optimized for the latest cloud-based web development platform. The conversion unit analyzes the read data and converts it into a format suitable for the latest platform. For example, it can convert HTML code and image files created with older tools into a format compatible with the latest platform. Finally, the Easy Website Migration Pack updates the converted data to the new environment. The update unit uploads the converted data to the latest cloud-based web development platform and builds a website optimized for the new environment. For example, it's possible to build a website with a design and functionality compatible with the latest platforms. This means the "Easy Website Migration Pack" reduces the hassle and cost of migrating from outdated tools to a new environment, providing a user-friendly, up-to-date website. For instance, small businesses and clubs migrating websites created with older no-code tools to a newer environment can significantly reduce the effort and cost involved by using this "Easy Website Migration Pack." Furthermore, it allows for the creation of a website adapted to modern needs without losing the value of the old site. This improves the user experience and contributes to a more comfortable internet environment where more people can access information.This means that the "Easy Website Migration Pack" allows you to migrate websites created with older no-code tools to the latest cloud-based web building platform.

[0029] The Easy Website Migration Pack according to this embodiment comprises a reading unit, a conversion unit, and an update unit. The reading unit reads data from websites created with older no-code tools. The reading unit can read data in a format compatible with older no-code tools, for example. For example, the reading unit can accurately acquire data from websites created with tools such as "Homepage Builder" or the "Bind series." The reading unit can also acquire version information of older tools and select the optimal reading method. For example, the reading unit can acquire version information of older no-code tools and select the corresponding reading algorithm. Furthermore, the reading unit can also be equipped with a function to check data integrity during reading and automatically correct errors. For example, the reading unit checks data integrity during reading and automatically fills in missing data. The conversion unit analyzes the read data and converts it into a format optimized for the latest cloud-based web construction platform. The conversion unit can convert HTML code and image files created with older tools into a format compatible with the latest platform, for example. For example, the conversion unit can convert HTML code created with older tools into a format compatible with the latest platform. Furthermore, the conversion unit can convert image files created with older tools into formats compatible with the latest platforms. In addition, the conversion unit can apply different conversion algorithms depending on the data category. For example, it can apply natural language processing algorithms to text data and image processing algorithms to image data. The update unit uploads the converted data to the latest cloud-based web building platform and constructs a website optimized for the new environment. For example, the update unit can construct a website with a design and functionality compatible with the latest platforms. It can also construct a website with functionality compatible with the latest platforms.As a result, the Easy Website Migration Pack according to this embodiment can migrate websites created with older no-code tools to the latest cloud-based web development platform.

[0030] The reading unit reads data from websites created with older no-code tools. Specifically, the reading unit can read data in a format compatible with older no-code tools. For example, it can accurately retrieve data from websites created with tools such as "Homepage Builder" and the "Bind series." This allows users to easily migrate data from websites created with older tools. The reading unit can also obtain version information of older tools and select the optimal reading method. For example, the reading unit can obtain version information of older no-code tools and select the corresponding reading algorithm. This allows it to accurately read data from websites created with different versions of tools. Furthermore, the reading unit can also have a function to check data integrity during reading and automatically correct errors. For example, the reading unit checks data integrity during reading and automatically fills in missing data. This allows users to migrate data with confidence even if data loss or inconsistencies occur. The reading unit can also utilize parallel processing technology to improve data reading speed and accuracy. For example, it can reduce processing time by reading data simultaneously using multiple threads. Furthermore, the reading unit can also be equipped with a function to monitor the data reading status in real time and notify the user of the progress. This allows the user to understand the data reading status and take action as needed.

[0031] The conversion unit analyzes the read data and converts it into a format optimized for the latest cloud-based web development platform. Specifically, the conversion unit can convert HTML code and image files created with older tools into formats compatible with the latest platform. For example, the conversion unit can convert HTML code created with older tools into a format compatible with the latest platform. It can also convert image files created with older tools into a format compatible with the latest platform. This allows users to migrate data from websites created with older tools to the latest platform. Furthermore, the conversion unit can apply different conversion algorithms depending on the data category. For example, it applies a natural language processing algorithm to text data and an image processing algorithm to image data. This ensures optimal conversion according to the characteristics of each data. The conversion unit can also have a function to detect and automatically correct errors that occur during the data conversion process. For example, it can detect and automatically correct syntax errors in HTML code. This ensures the quality of the converted data. Furthermore, the conversion unit can provide user-customizable conversion settings. For example, users can select specific data formats and conversion algorithms. This allows users to perform data conversion tailored to their needs.

[0032] The update unit uploads the converted data to the latest cloud-based web building platform and constructs a website optimized for the new environment. Specifically, the update unit can build websites with designs and functions compatible with the latest platform. For example, the update unit can build websites with designs compatible with the latest platform. It can also build websites with functions compatible with the latest platform. This allows users to easily build websites that utilize the latest technologies. Furthermore, the update unit can also have a function to detect and automatically correct errors that occur during the data upload process. For example, the update unit can detect network errors that occur during upload and automatically retry. This ensures that data is uploaded reliably. The update unit can also provide user-customizable upload settings. For example, users can set the upload order and priority of specific data. This allows users to upload data according to their needs. Furthermore, the update unit can also have a function to automatically check the operation of the website after uploading. For example, the update unit can detect and automatically correct broken links and display errors in the website after uploading. This allows users to publish their websites with confidence.

[0033] The reading unit can read data in a format compatible with older no-code tools. For example, the reading unit can accurately read data in a format compatible with older no-code tools. For instance, the reading unit can accurately retrieve data from websites created with tools such as "Homepage Builder" or the "Bind series." This allows for accurate reading of data in a format compatible with older no-code tools. The format of older no-code tools includes, but is not limited to, specific data formats and structures. Some or all of the processing described above in the reading unit may be performed using AI, or not. For example, the reading unit can input data compatible with the format of older no-code tools into an AI and have the AI ​​perform the data reading.

[0034] The conversion unit can analyze the read data and convert it into a format suitable for the latest platform. For example, the conversion unit can analyze the read data and convert it into a format suitable for the latest platform. For example, the conversion unit can convert HTML code or image files created with older tools into a format compatible with the latest platform. This allows the read data to be converted into a format suitable for the latest platform. A format suitable for the latest platform includes, but is not limited to, specific data formats and structures. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the read data into AI and have the AI ​​perform the data conversion.

[0035] The update unit can upload the converted data to the latest cloud-based web building platform and build a website optimized for the new environment. For example, the update unit can build a website with a design and functionality compatible with the latest platform. This allows the converted data to be provided in an optimized state for the new environment. A website optimized for the new environment includes, but is not limited to, criteria such as performance, compatibility, and usability. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the converted data into AI and have the AI ​​build the website.

[0036] The conversion unit can convert HTML code and image files created with older tools into formats compatible with the latest platforms. For example, the conversion unit can convert HTML code created with older tools into formats compatible with the latest platforms. For example, the conversion unit can convert image files created with older tools into formats compatible with the latest platforms. This allows HTML code and image files created with older tools to be converted into a format suitable for the latest platforms. Formats compatible with the latest platforms include, but are not limited to, specific data formats and structures. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input HTML code and image files created with older tools into AI and have the AI ​​perform the data conversion.

[0037] The update unit can build websites with designs and features compatible with the latest platforms. For example, the update unit can build websites with designs compatible with the latest platforms. For example, the update unit can build websites with features compatible with the latest platforms. This allows for the provision of websites with designs and features compatible with the latest platforms. Designs and features compatible with the latest platforms include, but are not limited to, specific UI / UX elements and feature sets. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can have AI perform the construction of websites with designs and features compatible with the latest platforms.

[0038] The reading unit can acquire version information of older no-code tools and select the optimal reading method. For example, the reading unit can acquire version information of older no-code tools and select a corresponding reading algorithm. For example, the reading unit can select a reading method corresponding to a specific format based on the version information. The reading unit can also select a compatible reading method based on the version information. This allows the optimal reading method to be selected based on the version information of older no-code tools. The version information of older no-code tools includes, for example, a specific version number or release date, but is not limited to such examples. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input version information of older no-code tools into AI and have the AI ​​perform the selection of the optimal reading method.

[0039] The reading unit may be equipped with a function to check data integrity during reading and automatically correct errors. For example, the reading unit can check data integrity during reading and automatically fill in missing data. For example, the reading unit can detect and automatically correct format errors during reading. The reading unit can also detect duplicate data during reading and merge it appropriately. This ensures that accurate data is obtained by checking data integrity and automatically correcting errors. Data integrity includes, but is not limited to, data consistency, completeness, and accuracy. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can have AI perform data integrity checks and error correction.

[0040] The reading unit can prioritize reading highly relevant data based on the user's geographical location information during reading. For example, the reading unit can prioritize reading highly relevant data based on the user's geographical location information. For example, the reading unit prioritizes reading highly relevant data based on the user's geographical location information. Furthermore, the reading unit can prioritize reading region-specific data based on the user's location information. In addition, the reading unit can select and read the most appropriate data considering the user's location information. This allows the reading unit to prioritize reading highly relevant data based on the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS coordinates and address information. Some or all of the above processing in the reading unit may be performed using, for example, AI, or without AI. For example, the reading unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0041] The reading unit can analyze the user's social media activity during reading and read relevant data. For example, the reading unit can analyze the user's social media activity and prioritize reading relevant data. For example, the reading unit can select and read highly relevant data based on the content of social media posts. The reading unit can also read relevant data based on the user's social media follower information. This allows the reading unit to read relevant data based on the user's social media activity. The user's social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's social media activity data into AI and have the AI ​​select relevant data.

[0042] The transformation unit can adjust the level of detail of the transformation based on the importance of the data during the transformation process. For example, the transformation unit can transform important data in detail and less important data in a simplified manner. For example, the transformation unit can adjust the accuracy of the transformation according to the importance of the data. Alternatively, the transformation unit can transform important data with high accuracy and other data with standard accuracy. This allows the level of detail of the transformation to be adjusted according to the importance of the data. Data importance includes, but is not limited to, the frequency of data use and its impact on the business. Some or all of the above processing in the transformation unit may be performed using, for example, AI, or not using AI. For example, the transformation unit can input the data importance into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the transformation.

[0043] The conversion unit can apply different conversion algorithms depending on the data category during conversion. For example, the conversion unit can apply a natural language processing algorithm to text data and an image processing algorithm to image data. For example, the conversion unit can select the optimal conversion algorithm according to the data category. The conversion unit can also apply a dedicated conversion algorithm to data in each category. This allows the optimal conversion algorithm to be applied according to the data category. Data categories include, but are not limited to, text data, image data, and metadata. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the data category into AI and have AI select the optimal conversion algorithm.

[0044] The conversion unit can determine the conversion priority based on the data submission date during the conversion process. For example, the conversion unit can prioritize the conversion of the most recent data and postpone the conversion of older data. For example, the conversion unit determines the order of conversion based on the submission date. The conversion unit can also prioritize the conversion of data with a recent submission date. This allows the conversion priority to be determined based on the data submission date. The data submission date includes, but is not limited to, the submission date and time. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the data submission date into AI and have AI perform the determination of the conversion priority.

[0045] The transformation unit can adjust the order of transformations based on the relevance of the data during the transformation process. For example, the transformation unit can prioritize the transformation of highly relevant data and postpone the transformation of less relevant data. For example, the transformation unit determines the order of transformations based on the relevance of the data. The transformation unit can also maintain overall consistency by transforming highly relevant data first. This allows the order of transformations to be adjusted based on the relevance of the data. Data relevance includes, but is not limited to, data correlation and co-occurrence frequency. Some or all of the above processing in the transformation unit may be performed using, for example, AI, or not using AI. For example, the transformation unit can input the data relevances into AI and have AI perform the adjustment of the order of transformations.

[0046] The update unit can analyze the user's past update history during an update to select the optimal update method. For example, the update unit can analyze the user's past update history and select the optimal update method. For example, the update unit can determine the update priority based on the past update history. The update unit can also propose the optimal update procedure based on the user's update history. This allows the system to select the optimal update method based on the user's past update history. The user's past update history includes, but is not limited to, the update date and time and the update content. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can input the user's past update history into AI and have AI select the optimal update method.

[0047] The update unit can customize the update method based on the user's current environment during an update. For example, the update unit can select the optimal update method based on the user's device environment. For example, the update unit can adjust the update method considering the user's network environment. The update unit can also customize the update procedure according to the user's usage environment. This allows the optimal update method to be selected based on the user's current environment. The user's current environment includes, but is not limited to, the device being used and the network environment. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's current environment data into AI and have AI select the optimal update method.

[0048] The update unit can select the optimal update method based on the user's geographical location information during an update. For example, the update unit can select the optimal update method based on the user's geographical location information. For example, the update unit can perform region-specific updates based on geographical location information. The update unit can also select the optimal update method considering the user's location information. This allows the optimal update method to be selected based on the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS coordinates and address information. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input the user's geographical location information into AI and have AI select the optimal update method.

[0049] The update unit can analyze the user's social media activity during an update and propose the optimal update method. For example, the update unit can analyze the user's social media activity and propose the optimal update method. For example, the update unit can propose highly relevant updates based on the content of social media posts. The update unit can also select the optimal update method based on the user's social media follower information. This allows the update unit to propose the optimal update method based on the user's social media activity. The user's social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input the user's social media activity data into AI and have the AI ​​propose the optimal update method.

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

[0051] The old Site Migration Easy Pack can prioritize reading highly relevant data based on the user's geographical location information in its reading unit. For example, if a user is in a specific region, it can prioritize reading data related to that region. It can also prioritize reading region-specific data based on the user's location information. Furthermore, it can select and read the most appropriate data considering the user's location information. This allows for the priority reading of highly relevant data based on the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS coordinates and address information. Some or all of the above processing in the reading unit may be performed using AI or not.

[0052] The old site migration package can adjust the level of detail in the conversion unit based on the importance of the data. For example, important data can be converted in detail, while less important data can be converted simply. The conversion unit can also adjust the accuracy of the conversion according to the importance of the data. For example, important data can be converted with high accuracy, while other data can be converted with standard accuracy. This allows the level of detail in the conversion to be adjusted according to the importance of the data. Data importance includes, but is not limited to, the frequency of data use and its impact on the business. Some or all of the above processing in the conversion unit may be performed using AI or not.

[0053] The old site migration package can analyze a user's past update history in its update section to select the optimal update method. For example, it can determine update priorities based on the user's past update history. The update section can also suggest the optimal update procedure based on the user's update history. This allows for the selection of the optimal update method based on the user's past update history. The user's past update history includes, but is not limited to, the update date and time and the update content. Some or all of the above processing in the update section may be performed using AI or not.

[0054] The old Site Migration Easy Pack allows the update unit to customize the update method based on the user's current environment. For example, it can select the optimal update method based on the user's device environment. The update unit can also adjust the update method considering the user's network environment. Furthermore, it can customize the update procedure according to the user's usage environment. This allows the optimal update method to be selected based on the user's current environment. The user's current environment includes, but is not limited to, the devices and network environment being used. Some or all of the above processing in the update unit may be performed using AI or not.

[0055] The old site migration package can analyze users' social media activity in its update section and propose the most suitable update method. For example, it can analyze users' social media activity and propose highly relevant updates. The update section can also select highly relevant data based on the content of social media posts and perform updates. Furthermore, it can select the most suitable update method based on the user's social media follower information. This allows the system to propose the most suitable update method based on the user's social media activity. User social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the update section may be performed using AI or not.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The reading unit reads data from websites created with older no-code tools. For example, the reading unit can accurately retrieve data from websites created with tools such as "Homepage Builder" or the "Bind series." The reading unit also retrieves version information of older tools and selects the optimal reading method. Furthermore, the reading unit can also check data integrity during reading and automatically correct errors. Step 2: The conversion unit analyzes the read data and converts it into a format optimized for the latest cloud-based web development platform. For example, the conversion unit can convert HTML code and image files created with older tools into formats compatible with the latest platforms. Furthermore, the conversion unit can apply different conversion algorithms depending on the data category. Step 3: The update team uploads the converted data to the latest cloud-based web building platform and constructs a website optimized for the new environment. For example, the update team can build a website with a design and functionality compatible with the latest platform.

[0058] (Example of form 2) The Easy Website Migration Pack, according to an embodiment of the present invention, is a system for migrating websites created with older no-code tools to the latest cloud-based web development platform. This system reads data such as text and images from websites created with older no-code tools, converts the read data into a format optimized for the latest cloud-based web development platform, and updates the converted data to the new environment. For example, the Easy Website Migration Pack uses a data reading unit that supports the format of older tools to accurately acquire data from websites created with older no-code tools. For example, it can read data from websites created with tools such as "Homepage Builder" and the "Bind series." Next, the Easy Website Migration Pack converts the read data into a format optimized for the latest cloud-based web development platform. The conversion unit analyzes the read data and converts it into a format suitable for the latest platform. For example, it can convert HTML code and image files created with older tools into a format compatible with the latest platform. Finally, the Easy Website Migration Pack updates the converted data to the new environment. The update unit uploads the converted data to the latest cloud-based web development platform and builds a website optimized for the new environment. For example, it's possible to build a website with a design and functionality compatible with the latest platforms. This means the "Easy Website Migration Pack" reduces the hassle and cost of migrating from outdated tools to a new environment, providing a user-friendly, up-to-date website. For instance, small businesses and clubs migrating websites created with older no-code tools to a newer environment can significantly reduce the effort and cost involved by using this "Easy Website Migration Pack." Furthermore, it allows for the creation of a website adapted to modern needs without losing the value of the old site. This improves the user experience and contributes to a more comfortable internet environment where more people can access information.This means that the "Easy Website Migration Pack" allows you to migrate websites created with older no-code tools to the latest cloud-based web building platform.

[0059] The Easy Website Migration Pack according to this embodiment comprises a reading unit, a conversion unit, and an update unit. The reading unit reads data from websites created with older no-code tools. The reading unit can read data in a format compatible with older no-code tools, for example. For example, the reading unit can accurately acquire data from websites created with tools such as "Homepage Builder" or the "Bind series." The reading unit can also acquire version information of older tools and select the optimal reading method. For example, the reading unit can acquire version information of older no-code tools and select the corresponding reading algorithm. Furthermore, the reading unit can also be equipped with a function to check data integrity during reading and automatically correct errors. For example, the reading unit checks data integrity during reading and automatically fills in missing data. The conversion unit analyzes the read data and converts it into a format optimized for the latest cloud-based web construction platform. The conversion unit can convert HTML code and image files created with older tools into a format compatible with the latest platform, for example. For example, the conversion unit can convert HTML code created with older tools into a format compatible with the latest platform. Furthermore, the conversion unit can convert image files created with older tools into formats compatible with the latest platforms. In addition, the conversion unit can apply different conversion algorithms depending on the data category. For example, it can apply natural language processing algorithms to text data and image processing algorithms to image data. The update unit uploads the converted data to the latest cloud-based web building platform and constructs a website optimized for the new environment. For example, the update unit can construct a website with a design and functionality compatible with the latest platforms. It can also construct a website with functionality compatible with the latest platforms.As a result, the Easy Website Migration Pack according to this embodiment can migrate websites created with older no-code tools to the latest cloud-based web development platform.

[0060] The reading unit reads data from websites created with older no-code tools. Specifically, the reading unit can read data in a format compatible with older no-code tools. For example, it can accurately retrieve data from websites created with tools such as "Homepage Builder" and the "Bind series." This allows users to easily migrate data from websites created with older tools. The reading unit can also obtain version information of older tools and select the optimal reading method. For example, the reading unit can obtain version information of older no-code tools and select the corresponding reading algorithm. This allows it to accurately read data from websites created with different versions of tools. Furthermore, the reading unit can also have a function to check data integrity during reading and automatically correct errors. For example, the reading unit checks data integrity during reading and automatically fills in missing data. This allows users to migrate data with confidence even if data loss or inconsistencies occur. The reading unit can also utilize parallel processing technology to improve data reading speed and accuracy. For example, it can reduce processing time by reading data simultaneously using multiple threads. Furthermore, the reading unit can also be equipped with a function to monitor the data reading status in real time and notify the user of the progress. This allows the user to understand the data reading status and take action as needed.

[0061] The conversion unit analyzes the read data and converts it into a format optimized for the latest cloud-based web development platform. Specifically, the conversion unit can convert HTML code and image files created with older tools into formats compatible with the latest platform. For example, the conversion unit can convert HTML code created with older tools into a format compatible with the latest platform. It can also convert image files created with older tools into a format compatible with the latest platform. This allows users to migrate data from websites created with older tools to the latest platform. Furthermore, the conversion unit can apply different conversion algorithms depending on the data category. For example, it applies a natural language processing algorithm to text data and an image processing algorithm to image data. This ensures optimal conversion according to the characteristics of each data. The conversion unit can also have a function to detect and automatically correct errors that occur during the data conversion process. For example, it can detect and automatically correct syntax errors in HTML code. This ensures the quality of the converted data. Furthermore, the conversion unit can provide user-customizable conversion settings. For example, users can select specific data formats and conversion algorithms. This allows users to perform data conversion tailored to their needs.

[0062] The update unit uploads the converted data to the latest cloud-based web building platform and constructs a website optimized for the new environment. Specifically, the update unit can build websites with designs and functions compatible with the latest platform. For example, the update unit can build websites with designs compatible with the latest platform. It can also build websites with functions compatible with the latest platform. This allows users to easily build websites that utilize the latest technologies. Furthermore, the update unit can also have a function to detect and automatically correct errors that occur during the data upload process. For example, the update unit can detect network errors that occur during upload and automatically retry. This ensures that data is uploaded reliably. The update unit can also provide user-customizable upload settings. For example, users can set the upload order and priority of specific data. This allows users to upload data according to their needs. Furthermore, the update unit can also have a function to automatically check the operation of the website after uploading. For example, the update unit can detect and automatically correct broken links and display errors in the website after uploading. This allows users to publish their websites with confidence.

[0063] The reading unit can read data in a format compatible with older no-code tools. For example, the reading unit can accurately read data in a format compatible with older no-code tools. For instance, the reading unit can accurately retrieve data from websites created with tools such as "Homepage Builder" or the "Bind series." This allows for accurate reading of data in a format compatible with older no-code tools. The format of older no-code tools includes, but is not limited to, specific data formats and structures. Some or all of the processing described above in the reading unit may be performed using AI, or not. For example, the reading unit can input data compatible with the format of older no-code tools into an AI and have the AI ​​perform the data reading.

[0064] The conversion unit can analyze the read data and convert it into a format suitable for the latest platform. For example, the conversion unit can analyze the read data and convert it into a format suitable for the latest platform. For example, the conversion unit can convert HTML code or image files created with older tools into a format compatible with the latest platform. This allows the read data to be converted into a format suitable for the latest platform. A format suitable for the latest platform includes, but is not limited to, specific data formats and structures. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the read data into AI and have the AI ​​perform the data conversion.

[0065] The update unit can upload the converted data to the latest cloud-based web building platform and build a website optimized for the new environment. For example, the update unit can build a website with a design and functionality compatible with the latest platform. This allows the converted data to be provided in an optimized state for the new environment. A website optimized for the new environment includes, but is not limited to, criteria such as performance, compatibility, and usability. Some or all of the above processing in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the converted data into AI and have the AI ​​build the website.

[0066] The conversion unit can convert HTML code and image files created with older tools into formats compatible with the latest platforms. For example, the conversion unit can convert HTML code created with older tools into formats compatible with the latest platforms. For example, the conversion unit can convert image files created with older tools into formats compatible with the latest platforms. This allows HTML code and image files created with older tools to be converted into a format suitable for the latest platforms. Formats compatible with the latest platforms include, but are not limited to, specific data formats and structures. Some or all of the above-described processes in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input HTML code and image files created with older tools into AI and have the AI ​​perform the data conversion.

[0067] The update unit can build websites with designs and features compatible with the latest platforms. For example, the update unit can build websites with designs compatible with the latest platforms. For example, the update unit can build websites with features compatible with the latest platforms. This allows for the provision of websites with designs and features compatible with the latest platforms. Designs and features compatible with the latest platforms include, but are not limited to, specific UI / UX elements and feature sets. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can have AI perform the construction of websites with designs and features compatible with the latest platforms.

[0068] The reading unit can estimate the user's emotions and adjust the timing of data reading based on the estimated emotions. For example, if the user is stressed, the reading unit can read quickly to reduce the user's burden. Conversely, if the user is relaxed, the reading unit can read slowly to obtain detailed data. Furthermore, if the user is in a hurry, the reading unit can prioritize reading important data. This allows the timing of data reading to be adjusted according to the user's emotions. User 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. Some or all of the above processing in the reading unit may be performed using AI, or not using AI. For example, the reading unit can input user emotion data into an AI and have the AI ​​adjust the timing of data reading.

[0069] The reading unit can acquire version information of older no-code tools and select the optimal reading method. For example, the reading unit can acquire version information of older no-code tools and select a corresponding reading algorithm. For example, the reading unit can select a reading method corresponding to a specific format based on the version information. The reading unit can also select a compatible reading method based on the version information. This allows the optimal reading method to be selected based on the version information of older no-code tools. The version information of older no-code tools includes, for example, a specific version number or release date, but is not limited to such examples. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input version information of older no-code tools into AI and have the AI ​​perform the selection of the optimal reading method.

[0070] The reading unit may be equipped with a function to check data integrity during reading and automatically correct errors. For example, the reading unit can check data integrity during reading and automatically fill in missing data. For example, the reading unit can detect and automatically correct format errors during reading. The reading unit can also detect duplicate data during reading and merge it appropriately. This ensures that accurate data is obtained by checking data integrity and automatically correcting errors. Data integrity includes, but is not limited to, data consistency, completeness, and accuracy. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can have AI perform data integrity checks and error correction.

[0071] The reading unit can estimate the user's emotions and determine the priority of data to read based on the estimated emotions. For example, if the user is stressed, the reading unit can prioritize reading important data. If the user is relaxed, the reading unit can read all data equally. Furthermore, if the user is in a hurry, the reading unit can prioritize reading only the minimum necessary data. This allows the data to be prioritized according to the user's emotions. The estimation of the user's emotions 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. Some or all of the above processing in the reading unit may be performed using AI, or not using AI. For example, the reading unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[0072] The reading unit can prioritize reading highly relevant data based on the user's geographical location information during reading. For example, the reading unit can prioritize reading highly relevant data based on the user's geographical location information. For example, the reading unit prioritizes reading highly relevant data based on the user's geographical location information. Furthermore, the reading unit can prioritize reading region-specific data based on the user's location information. In addition, the reading unit can select and read the most appropriate data considering the user's location information. This allows the reading unit to prioritize reading highly relevant data based on the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS coordinates and address information. Some or all of the above processing in the reading unit may be performed using, for example, AI, or without AI. For example, the reading unit can input the user's geographical location information into AI and have the AI ​​select highly relevant data.

[0073] The reading unit can analyze the user's social media activity during reading and read relevant data. For example, the reading unit can analyze the user's social media activity and prioritize reading relevant data. For example, the reading unit can select and read highly relevant data based on the content of social media posts. The reading unit can also read relevant data based on the user's social media follower information. This allows the reading unit to read relevant data based on the user's social media activity. The user's social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input the user's social media activity data into AI and have the AI ​​select relevant data.

[0074] The transformation unit can estimate the user's emotions and adjust the transformation's expression based on the estimated emotions. For example, if the user is stressed, the transformation unit can perform the transformation using a simple expression. If the user is relaxed, the transformation unit can perform the transformation using a detailed expression. Furthermore, if the user is in a hurry, the transformation unit can perform the transformation quickly. This allows the transformation's expression to be adjusted according to the user's emotions. The estimation of the user's emotions is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the transformation unit may be performed using AI, for example, or without AI. For example, the transformation unit can input user emotion data into an AI and have the AI ​​adjust the transformation's expression.

[0075] The transformation unit can adjust the level of detail of the transformation based on the importance of the data during the transformation process. For example, the transformation unit can transform important data in detail and less important data in a simplified manner. For example, the transformation unit can adjust the accuracy of the transformation according to the importance of the data. Alternatively, the transformation unit can transform important data with high accuracy and other data with standard accuracy. This allows the level of detail of the transformation to be adjusted according to the importance of the data. Data importance includes, but is not limited to, the frequency of data use and its impact on the business. Some or all of the above processing in the transformation unit may be performed using, for example, AI, or not using AI. For example, the transformation unit can input the data importance into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the transformation.

[0076] The conversion unit can apply different conversion algorithms depending on the data category during conversion. For example, the conversion unit can apply a natural language processing algorithm to text data and an image processing algorithm to image data. For example, the conversion unit can select the optimal conversion algorithm according to the data category. The conversion unit can also apply a dedicated conversion algorithm to data in each category. This allows the optimal conversion algorithm to be applied according to the data category. Data categories include, but are not limited to, text data, image data, and metadata. Some or all of the above processing in the conversion unit may be performed using AI, for example, or without AI. For example, the conversion unit can input the data category into AI and have AI select the optimal conversion algorithm.

[0077] The transformation unit can estimate the user's emotions and adjust the length of the transformation based on the estimated emotions. For example, if the user is in a hurry, the transformation unit can perform a short, concise transformation. If the user is relaxed, the transformation unit can perform a longer transformation that includes detailed explanations. Furthermore, if the user is excited, the transformation unit can perform a transformation with visually stimulating effects. This allows the length of the transformation to be adjusted according to the user's emotions. The estimation of the user's emotions 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. Some or all of the above processing in the transformation unit may be performed using AI, for example, or not using AI. For example, the transformation unit can input user emotion data into AI and have the AI ​​adjust the length of the transformation.

[0078] The conversion unit can determine the conversion priority based on the data submission date during the conversion process. For example, the conversion unit can prioritize the conversion of the most recent data and postpone the conversion of older data. For example, the conversion unit determines the order of conversion based on the submission date. The conversion unit can also prioritize the conversion of data with a recent submission date. This allows the conversion priority to be determined based on the data submission date. The data submission date includes, but is not limited to, the submission date and time. Some or all of the above processing in the conversion unit may be performed using, for example, AI, or not using AI. For example, the conversion unit can input the data submission date into AI and have AI perform the determination of the conversion priority.

[0079] The transformation unit can adjust the order of transformations based on the relevance of the data during the transformation process. For example, the transformation unit can prioritize the transformation of highly relevant data and postpone the transformation of less relevant data. For example, the transformation unit determines the order of transformations based on the relevance of the data. The transformation unit can also maintain overall consistency by transforming highly relevant data first. This allows the order of transformations to be adjusted based on the relevance of the data. Data relevance includes, but is not limited to, data correlation and co-occurrence frequency. Some or all of the above processing in the transformation unit may be performed using, for example, AI, or not using AI. For example, the transformation unit can input the data relevances into AI and have AI perform the adjustment of the order of transformations.

[0080] The update unit can estimate the user's emotions and adjust the update method based on the estimated emotions. For example, if the user is stressed, the update unit can perform a rapid update. If the user is relaxed, the update unit can perform a detailed update. Furthermore, if the user is in a hurry, the update unit can prioritize updating important parts. This allows the update method to be adjusted according to the user's emotions. User 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. Some or all of the above processing in the update unit may be performed using AI, or not using AI. For example, the update unit can input user emotion data into AI and have the AI ​​adjust the update method.

[0081] The update unit can analyze the user's past update history during an update to select the optimal update method. For example, the update unit can analyze the user's past update history and select the optimal update method. For example, the update unit can determine the update priority based on the past update history. The update unit can also propose the optimal update procedure based on the user's update history. This allows the system to select the optimal update method based on the user's past update history. The user's past update history includes, but is not limited to, the update date and time and the update content. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or not. For example, the update unit can input the user's past update history into AI and have AI select the optimal update method.

[0082] The update unit can customize the update method based on the user's current environment during an update. For example, the update unit can select the optimal update method based on the user's device environment. For example, the update unit can adjust the update method considering the user's network environment. The update unit can also customize the update procedure according to the user's usage environment. This allows the optimal update method to be selected based on the user's current environment. The user's current environment includes, but is not limited to, the device being used and the network environment. Some or all of the above-described processes in the update unit may be performed using AI, for example, or without AI. For example, the update unit can input the user's current environment data into AI and have AI select the optimal update method.

[0083] The update unit can estimate the user's emotions and determine update priorities based on those emotions. For example, if the user is stressed, the update unit will prioritize updating important parts. If the user is relaxed, the update unit can perform a complete update. Furthermore, if the user is in a hurry, the update unit can perform a rapid update. This allows the update priority to be determined according to the user's emotions. User 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. Some or all of the above processing in the update unit may be performed using AI or not. For example, the update unit can input user emotion data into an AI and have the AI ​​determine the update priority.

[0084] The update unit can select the optimal update method based on the user's geographical location information during an update. For example, the update unit can select the optimal update method based on the user's geographical location information. For example, the update unit can perform region-specific updates based on geographical location information. The update unit can also select the optimal update method considering the user's location information. This allows the optimal update method to be selected based on the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS coordinates and address information. Some or all of the above-described processes in the update unit may be performed using, for example, AI, or without AI. For example, the update unit can input the user's geographical location information into AI and have AI select the optimal update method.

[0085] The update unit can analyze the user's social media activity during an update and propose the optimal update method. For example, the update unit can analyze the user's social media activity and propose the optimal update method. For example, the update unit can propose highly relevant updates based on the content of social media posts. The update unit can also select the optimal update method based on the user's social media follower information. This allows the update unit to propose the optimal update method based on the user's social media activity. The user's social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. Some or all of the above processing in the update unit may be performed using, for example, AI, or not using AI. For example, the update unit can input the user's social media activity data into AI and have the AI ​​propose the optimal update method.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The old site migration package can estimate the user's emotions and adjust the data reading method based on those emotions. For example, if the user is stressed, the reading unit will read the data quickly to reduce the user's burden. If the user is relaxed, the reading unit can read slowly to obtain detailed data. Furthermore, if the user is in a hurry, the reading unit can prioritize reading important data. In this way, the data reading method can be adjusted according to the user's emotions. Emotion estimation may be performed using an emotion engine or generative AI. For example, an emotion engine can estimate emotions from the user's facial expressions and voice and adjust the reading method based on the results.

[0088] The old Site Migration Easy Pack can prioritize reading highly relevant data based on the user's geographical location information in its reading unit. For example, if a user is in a specific region, it can prioritize reading data related to that region. It can also prioritize reading region-specific data based on the user's location information. Furthermore, it can select and read the most appropriate data considering the user's location information. This allows for the priority reading of highly relevant data based on the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS coordinates and address information. Some or all of the above processing in the reading unit may be performed using AI or not.

[0089] The old site migration package can estimate the user's emotions in its translation section and adjust the translation's expression based on the estimated emotions. For example, if the user is stressed, the translation section will use a simple expression. If the user is relaxed, it can use a more detailed expression. Furthermore, if the user is in a hurry, it can perform a quick translation. This allows the translation's expression to be adjusted according to the user's emotions. Emotion estimation may be performed using an emotion engine or generative AI. For example, an emotion engine can estimate emotions from the user's facial expressions and voice, and adjust the translation's expression based on the results.

[0090] The old site migration package can adjust the level of detail in the conversion unit based on the importance of the data. For example, important data can be converted in detail, while less important data can be converted simply. The conversion unit can also adjust the accuracy of the conversion according to the importance of the data. For example, important data can be converted with high accuracy, while other data can be converted with standard accuracy. This allows the level of detail in the conversion to be adjusted according to the importance of the data. Data importance includes, but is not limited to, the frequency of data use and its impact on the business. Some or all of the above processing in the conversion unit may be performed using AI or not.

[0091] The old site migration package can estimate the user's emotions in the translation section and adjust the length of the translation based on the estimated emotions. For example, if the user is in a hurry, a short, to-the-point translation will be performed. If the user is relaxed, a longer translation including detailed explanations can be performed. Furthermore, if the user is excited, a translation with visually stimulating effects can be added. In this way, the length of the translation can be adjusted according to the user's emotions. Emotion estimation may be performed using an emotion engine or generative AI. For example, an emotion engine can estimate emotions from the user's facial expressions and voice and adjust the length of the translation based on the results.

[0092] The old site migration package can analyze a user's past update history in its update section to select the optimal update method. For example, it can determine update priorities based on the user's past update history. The update section can also suggest the optimal update procedure based on the user's update history. This allows for the selection of the optimal update method based on the user's past update history. The user's past update history includes, but is not limited to, the update date and time and the update content. Some or all of the above processing in the update section may be performed using AI or not.

[0093] The old site migration package can estimate the user's emotions in the update section and adjust the update method based on the estimated emotions. For example, if the user is stressed, the update section will perform the update quickly. If the user is relaxed, a detailed update can be performed. Furthermore, if the user is in a hurry, important parts can be prioritized in the update. In this way, the update method can be adjusted according to the user's emotions. Emotion estimation may be performed using an emotion engine or generative AI. For example, an emotion engine can estimate emotions from the user's facial expressions and voice and adjust the update method based on the results.

[0094] The old Site Migration Easy Pack allows the update unit to customize the update method based on the user's current environment. For example, it can select the optimal update method based on the user's device environment. The update unit can also adjust the update method considering the user's network environment. Furthermore, it can customize the update procedure according to the user's usage environment. This allows the optimal update method to be selected based on the user's current environment. The user's current environment includes, but is not limited to, the devices and network environment being used. Some or all of the above processing in the update unit may be performed using AI or not.

[0095] The old Site Migration Easy Pack can estimate the user's emotions in the update section and determine update priorities based on those emotions. For example, if the user is stressed, important parts can be prioritized for updates. If the user is relaxed, a full update can be performed. Furthermore, if the user is in a hurry, updates can be performed quickly. This allows for the prioritization of updates according to the user's emotions. Emotion estimation may be performed using an emotion engine or generative AI. For example, an emotion engine can estimate emotions from the user's facial expressions and voice, and determine update priorities based on the results.

[0096] The old site migration package can analyze users' social media activity in its update section and propose the most suitable update method. For example, it can analyze users' social media activity and propose highly relevant updates. The update section can also select highly relevant data based on the content of social media posts and perform updates. Furthermore, it can select the most suitable update method based on the user's social media follower information. This allows the system to propose the most suitable update method based on the user's social media activity. User social media activity includes, but is not limited to, posts, the number of likes, and the number of followers. Some or all of the above processing in the update section may be performed using AI or not.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The reading unit reads data from websites created with older no-code tools. For example, the reading unit can accurately retrieve data from websites created with tools such as "Homepage Builder" or the "Bind series." The reading unit also retrieves version information of older tools and selects the optimal reading method. Furthermore, the reading unit can also check data integrity during reading and automatically correct errors. Step 2: The conversion unit analyzes the read data and converts it into a format optimized for the latest cloud-based web development platform. For example, the conversion unit can convert HTML code and image files created with older tools into formats compatible with the latest platforms. Furthermore, the conversion unit can apply different conversion algorithms depending on the data category. Step 3: The update team uploads the converted data to the latest cloud-based web building platform and constructs a website optimized for the new environment. For example, the update team can build a website with a design and functionality compatible with the latest platform.

[0099] 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.

[0100] 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 text generation AI, image generation AI, and multimodal generation AI. 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 with 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 from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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 various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts 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.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0101] 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.

[0102] Each of the multiple elements described above, including the reading unit, conversion unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit is implemented by the control unit 46A of the smart device 14 and accurately retrieves data from a website created with an older no-code tool. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the read data into a format optimized for the latest cloud-based web construction platform. The update unit is implemented by the control unit 46A of the smart device 14 and uploads the converted data to the new environment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0116] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0118] Each of the multiple elements described above, including the reading unit, conversion unit, and update 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 accurately acquires data from a website created with an older no-code tool. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the read data into a format optimized for the latest cloud-based web development platform. The update unit is implemented by the control unit 46A of the smart glasses 214 and uploads the converted data to the new environment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the reading unit, conversion unit, and update 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 accurately acquires data from websites created with older no-code tools. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the read data into a format optimized for the latest cloud-based web construction platform. The update unit is implemented by the control unit 46A of the headset terminal 314 and uploads the converted data to the new environment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the reading unit, conversion unit, and update 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 accurately acquires data from a website created with an older no-code tool. The conversion unit is implemented by the specific processing unit 290 of the data processing unit 12 and converts the read data into a format optimized for the latest cloud-based web construction platform. The update unit is implemented by the control unit 46A of the robot 414 and uploads the converted data to the new environment. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A reading unit that reads data from websites created with older no-code tools, A conversion unit that converts the data read by the aforementioned reading unit into a format optimized for the latest cloud-based web construction platform, The system includes an update unit that updates the data converted by the conversion unit to a new environment. A system characterized by the following features. (Note 2) The reading unit is Read data in a format compatible with older no-code tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) The conversion unit is The system analyzes the read data and converts it to a format suitable for the latest platform. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update section is, The converted data is uploaded to a state-of-the-art cloud-based web building platform to construct a website optimized for the new environment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The conversion unit is Convert HTML code and image files created with older tools to formats compatible with the latest platforms. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned update section is, Build a website with a design and functionality that is compatible with the latest platforms. The system described in Appendix 1, characterized by the features described herein. (Note 7) The reading unit is It estimates the user's emotions and adjusts the timing of data reading based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The reading unit is Retrieve version information from older no-code tools and select the optimal method for reading it. The system described in Appendix 1, characterized by the features described herein. (Note 9) The reading unit is It has a function to check the integrity of the data during reading and to automatically correct errors. The system described in Appendix 1, characterized by the features described herein. (Note 10) The reading unit is It estimates the user's emotions and determines the priority of data to read based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The reading unit is When reading data, the system prioritizes reading the most relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The reading unit is Analyze the user's social media activity during reading and extract relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The conversion unit is It estimates the user's emotions and adjusts the way the transformation is expressed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The conversion unit is Adjust the level of detail of the conversion based on the importance of the data during the conversion process. The system described in Appendix 1, characterized by the features described herein. (Note 15) The conversion unit is Apply different conversion algorithms depending on the data category during conversion. The system described in Appendix 1, characterized by the features described herein. (Note 16) The conversion unit is It estimates the user's emotions and adjusts the length of the conversion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The conversion unit is During the conversion process, the conversion priority is determined based on the data submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The conversion unit is The order of conversions is adjusted based on the relevance of the data during the conversion process. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update section is, It estimates user sentiment and adjusts the update method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update section is, During updates, the system analyzes the user's past update history to select the optimal update method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update section is, Customize the update process based on the user's current environment during updates. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update section is, It estimates user sentiment and determines update priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update section is, During updates, the system selects the optimal update method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned update section is, The system analyzes users' social media activity during updates and suggests the optimal update method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0171] 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 reading unit that reads data from websites created with older no-code tools, A conversion unit that converts the data read by the aforementioned reading unit into a format optimized for the latest cloud-based web construction platform, The system includes an update unit that updates the data converted by the conversion unit to a new environment. A system characterized by the following features.

2. The reading unit is Read data in a format compatible with older no-code tools. The system according to feature 1.

3. The conversion unit is The system analyzes the read data and converts it to a format suitable for the latest platform. The system according to feature 1.

4. The aforementioned update section is, The converted data is uploaded to a state-of-the-art cloud-based web building platform to construct a website optimized for the new environment. The system according to feature 1.

5. The conversion unit is Convert HTML code and image files created with older tools to formats compatible with the latest platforms. The system according to feature 1.

6. The aforementioned update section is, Build a website with a design and functionality that is compatible with the latest platforms. The system according to feature 1.

7. The reading unit is It estimates the user's emotions and adjusts the timing of data reading based on the estimated user emotions. The system according to feature 1.

8. The reading unit is Retrieve version information from older no-code tools and select the best method for reading it. The system according to feature 1.

Images