System

Abstract

An object of the system according to the embodiment is to generate and modify a home page only by a user giving a verbal instruction.SOLUTION: A system includes an instruction analysis part, a generation part, a correction part, and an interface part. The instruction analysis unit analyzes a verbal instruction of a user. The generation unit generates a home page based on the instruction analyzed by the instruction analysis unit. The correction part analyzes a correction instruction to the home page generated by the generation part and reflects the correction. The interface unit provides an interface for the user to verbally issue an instruction.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that creating and editing a homepage requires manual operations by the user, which is inefficient.

[0005] The system according to the embodiment aims to enable a user to generate and modify a homepage simply by issuing verbal instructions. [Means for solving the problem]

[0006] The system according to the embodiment includes an instruction analysis unit, a generation unit, a correction unit, and an interface unit. The instruction analysis unit analyzes verbal instructions from a user. The generation unit generates a homepage based on the instructions analyzed by the instruction analysis unit. The correction unit analyzes correction instructions for the homepage generated by the generation unit and reflects the corrections. The interface unit provides an interface for the user to issue verbal instructions. [Effects of the Invention]

[0007] The system according to the embodiment allows the user to create and modify a homepage simply by giving verbal instructions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automatic homepage generation system according to an embodiment of the present invention is a system that automatically generates an ideal homepage simply by having a user give verbal instructions. In this system, a generation AI instantly analyzes the user's verbal instructions and automatically generates a homepage based on those instructions. The system can also automatically modify the homepage by having the user provide verbal instructions for correction. This allows the automatic homepage generation system to easily create an ideal homepage simply by having the user speak. For example, even a user with no knowledge of web design can create a professional homepage simply by providing verbal instructions. Furthermore, since corrections can be easily made verbally, the homepage can be completed quickly and efficiently.

[0029] An automatic homepage generation system according to an embodiment includes an instruction analysis unit, a generation unit, a correction unit, and an interface unit. The instruction analysis unit analyzes a user's verbal instructions. For example, when a user verbally gives instructions regarding the design or content of a homepage, the generation AI analyzes the instructions. The generation AI converts the user's instructions into text using speech recognition technology and understands the content. The generation AI receives input in the form of prompts containing instructions about what the user wants the generation AI to do, and the generation AI performs analysis based on the prompts. The generation unit generates a homepage based on the instructions analyzed by the instruction analysis unit. For example, if a user instructs the generation AI to "place a large image on the homepage and include a company introduction below it," the generation AI will place a large image on the homepage and insert the company introduction below it in accordance with the instructions. Furthermore, if a user instructs the generation AI to "change the background color to blue," the generation AI will change the background color to blue. The correction unit analyzes correction instructions for the homepage generated by the generation unit and incorporates the corrections. For example, if a user issues an instruction such as "Please make the image on the top page a little smaller," the generation AI analyzes the instruction and modifies it to make the image on the top page smaller. The interface unit provides an interface through which the user can issue verbal instructions. For example, it may provide an interface for inputting voice via a microphone or a voice input interface using a smartphone or tablet. This allows the user to easily issue verbal instructions. As a result, the automatic homepage generation system according to the embodiment automatically generates a homepage based on the user's verbal instructions, and modifications can also be made verbally. For example, the user can easily create their ideal homepage just by speaking. Furthermore, since modifications can also be easily made verbally, the homepage can be completed quickly and efficiently.

[0030] The instruction analysis unit can analyze the tone and speed of the user's voice, estimate the user's urgency and importance, and automatically set the priority of instructions. For example, the generation AI of the instruction analysis unit analyzes the tone and speed of the user's voice to estimate the urgency and importance. For example, it detects a hurried tone or a high voice tone and sets the priority of instructions high. The instruction analysis unit also analyzes the tone and speed of the user's voice to estimate the urgency and importance. For example, it detects a calm tone or a low voice tone and sets the priority of instructions low. The instruction analysis unit also analyzes the tone and speed of the user's voice to estimate the urgency and importance. For example, if the user speaks quickly, it determines that the urgency is high and sets the priority of the instruction high. This makes it possible to set the priority of instructions according to the user's urgency and importance.

[0031] The instruction analysis unit learns the user's past instruction history and predicts the user's preferences and patterns, allowing for more accurate analysis. In the instruction analysis unit, for example, the generation AI learns the user's past instruction history and predicts preferences and patterns. For example, it prioritizes suggesting design elements that have been used frequently in the past. In addition, the instruction analysis unit learns the user's past instruction history and predicts preferences and patterns. For example, it prioritizes suggesting colors and layouts that the user prefers. In addition, the instruction analysis unit learns the user's past instruction history and predicts preferences and patterns. For example, it predicts what the user is likely to specify next based on what the user has specified in the past. This allows for learning the user's preferences and patterns, enabling more accurate analysis.

[0032] The instruction analysis unit can analyze oral instructions in different languages ​​and enable the generation of a multilingual homepage. For example, the generation AI analyzes oral instructions in different languages ​​and generates a multilingual homepage. For example, the generation AI analyzes instructions in English, French, Chinese, etc. and generates a corresponding homepage. The instruction analysis unit also analyzes oral instructions in different languages ​​and generates a multilingual homepage. For example, the generation AI analyzes instructions in Spanish, German, Italian, etc. and generates a corresponding homepage. The instruction analysis unit also analyzes oral instructions in different languages ​​and generates a multilingual homepage. For example, the generation AI analyzes instructions in Japanese, Korean, Russian, etc. and generates a corresponding homepage. This makes it possible to respond to instructions in different languages ​​and generate a multilingual homepage.

[0033] The instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to perform more accurate instruction analysis. In the instruction analysis unit, for example, the generation AI analyzes the user's gestures and facial expressions and combines them with verbal instructions to analyze the instructions. For example, it analyzes the hand movements and facial expressions when giving instructions to understand the intention of the instructions. In addition, the instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to analyze the instructions. For example, it analyzes the eye movements and mouth movements when giving instructions to understand the intention of the instructions. In addition, the instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to analyze the instructions. For example, it analyzes the body movements and posture when giving instructions to understand the intention of the instructions. This enables more accurate instruction analysis by analyzing gestures and facial expressions.

[0034] The generation unit can generate multiple design options based on user instructions and allow the user to select from them. For example, the generation unit generates multiple design options using a generation AI based on user instructions. For example, designs with different layouts and color usage can be proposed and the user can select from them. The generation unit also generates multiple design options using a generation AI based on user instructions. For example, designs with different fonts and image placement can be proposed and the user can select from them. The generation unit also generates multiple design options using a generation AI based on user instructions. For example, designs with different themes and styles can be proposed and the user can select from them. This allows the user to select from multiple design options.

[0035] The generation unit automatically generates templates specialized for the user's industry and business model, enabling the provision of more appropriate designs. In the generation unit, for example, the generation AI automatically generates templates specialized for the user's industry and business model. For example, templates for the food and beverage industry and templates for the IT industry are provided. In addition, the generation unit automatically generates templates specialized for the user's industry and business model. For example, templates for the education industry and templates for the medical industry are provided. In addition, the generation unit automatically generates templates specialized for the user's industry and business model. For example, templates for the fashion industry and templates for the real estate industry are provided. This makes it possible to provide templates specialized for industries and business models.

[0036] The generation unit can automatically generate not only a homepage but also related marketing materials and presentation materials based on user instructions. For example, the generation AI automatically generates not only a homepage but also related marketing materials based on user instructions. For example, it generates company introduction materials and product catalogs. The generation unit also automatically generates not only a homepage but also related presentation materials based on user instructions. For example, it generates presentation slides and sales materials. The generation unit also automatically generates not only a homepage but also related marketing materials and presentation materials based on user instructions. For example, it generates event guides and promotional materials. This allows not only a homepage but also related marketing materials and presentation materials to be automatically generated.

[0037] The generation unit also automatically implements SEO measures for the homepage based on user instructions, thereby improving its ranking in search engines. In the generation unit, for example, the generation AI automatically implements SEO measures for the homepage based on user instructions. For example, it optimizes keywords and sets meta tags. In addition, the generation unit automatically implements SEO measures for the homepage based on user instructions. For example, it optimizes internal links and improves page speed. In addition, the generation unit automatically implements SEO measures for the homepage based on user instructions. For example, it optimizes content and acquires external links. In this way, the generation AI automatically implements SEO measures for the homepage and improves its ranking in search engines.

[0038] The correction unit analyzes the user's correction instructions, automatically evaluates the extent of the impact of the correction, and can appropriately correct other related parts. For example, the generation AI in the correction unit analyzes the user's correction instructions and automatically evaluates the extent of the impact of the correction. For example, if an instruction is received to make the image on the top page smaller, the image sizes on other pages are also appropriately adjusted. The correction unit also analyzes the user's correction instructions and automatically evaluates the extent of the impact of the correction. For example, if an instruction is received to change the background color, the background colors on other pages are also appropriately adjusted. The correction unit also analyzes the user's correction instructions and automatically evaluates the extent of the impact of the correction. For example, if an instruction is received to change the font size, the font sizes on other pages are also appropriately adjusted. This allows the extent of the impact of the correction to be automatically evaluated, and other related parts to be appropriately corrected.

[0039] The correction unit can analyze the user's correction instructions and provide a comparative preview before and after the correction, allowing the user to confirm the changes. In the correction unit, for example, the generation AI analyzes the user's correction instructions and provides a comparative preview before and after the correction. For example, a preview before and after changing the image size is displayed so that the user can confirm. In addition, the correction unit analyzes the user's correction instructions and provides a comparative preview before and after the correction. For example, a preview before and after changing the background color is displayed so that the user can confirm. In addition, the correction unit analyzes the user's correction instructions and provides a comparative preview before and after the correction. For example, a preview before and after changing the font size is displayed so that the user can confirm. In this way, a comparative preview before and after the correction is provided, allowing the user to confirm the changes.

[0040] The correction unit can analyze the user's correction instructions and make the corrections applicable to other projects and documents. In the correction unit, for example, the generation AI analyzes the user's correction instructions and applies the corrections to other projects. For example, it reflects a specific design change to multiple projects. In addition, the correction unit analyzes the user's correction instructions and applies the corrections to other documents. For example, it reflects a specific font change to multiple documents. In addition, the correction unit analyzes the user's correction instructions and applies the corrections to other projects and documents. For example, it reflects a specific layout change to multiple projects and documents. This makes it possible to apply the corrections to other projects and documents.

[0041] The correction unit analyzes the user's correction instructions, automatically manages versions of the corrections, and allows easy reversion to past versions. In the correction unit, for example, the generation AI analyzes the user's correction instructions, and automatically manages versions of the corrections. For example, it allows easy reversion to the state before the corrections. In addition, the correction unit analyzes the user's correction instructions, and automatically manages versions of the corrections. For example, it saves the correction history, allowing reversion to past versions. In addition, the correction unit analyzes the user's correction instructions, and automatically manages versions of the corrections. For example, it manages the corrections in chronological order, allowing reversion to any version. In this way, the corrections are automatically managed as versions, and it allows easy reversion to past versions.

[0042] The interface unit can integrate not only voice recognition but also handwriting input and gesture input, thereby providing multiple input methods. The interface unit, for example, integrates not only voice recognition but also handwriting input into the user interface. For example, it allows instructions to be input by handwriting using a tablet. The interface unit can also integrate not only voice recognition but also gesture input into the user interface. For example, it allows instructions to be input by gestures using a camera. The interface unit can also integrate not only voice recognition but also handwriting input and gesture input into the user interface. For example, it allows instructions to be input by handwriting or gestures using a smartphone. This integrates not only voice recognition but also handwriting input and gesture input, thereby providing multiple input methods.

[0043] The interface unit is equipped with an AI assistant and can provide real-time support when the user gives instructions. The interface unit, for example, has an AI assistant in the user interface and provides real-time support when the user gives instructions. For example, it checks the content of the instructions and makes appropriate suggestions. The interface unit also has an AI assistant in the user interface and provides real-time support when the user gives instructions. For example, it provides guidance when the user is unsure. The interface unit also has an AI assistant in the user interface and provides real-time support when the user gives instructions. For example, it provides an immediate answer when the user asks a question. This makes it possible to provide real-time support when the user gives instructions.

[0044] The interface unit can be made compatible with devices such as smart speakers or smart watches, enabling use in a wider variety of environments. For example, the interface unit makes the user interface compatible with smart speakers, enabling voice instructions to be issued. For example, instructions can be issued using Amazon Echo or Google Home. The interface unit can also make the user interface compatible with smart watches, enabling instructions to be issued from the wrist. For example, instructions can be issued using Apple Watch or Samsung Galaxy Watch. The interface unit can also make the user interface compatible with devices such as smart speakers or smart watches, enabling use in a wider variety of environments. For example, instructions can be issued at home or on the go. This makes the interface compatible with devices such as smart speakers or smart watches, enabling use in a wider variety of environments.

[0045] The interface unit can incorporate AR technology to enable the user to give instructions visually. The interface unit, for example, incorporates AR technology into the user interface to enable the user to give instructions visually. For example, instructions are given using AR glasses. The interface unit can also incorporate AR technology into the user interface to enable the user to give instructions visually. For example, instructions are given using an AR display using a smartphone camera. The interface unit can also incorporate AR technology into the user interface to enable the user to give instructions visually. For example, instructions are given using an AR display using a tablet. In this way, AR technology is incorporated to enable the user to give instructions visually.

[0046] The preview function can add a real-time editing function, allowing the user to directly edit while viewing the preview. The preview function can add, for example, a real-time editing function, allowing the user to directly edit while viewing the preview. For example, editing text or images by drag and drop. The preview function can also add a real-time editing function, allowing the user to directly edit while viewing the preview. For example, changing the background color or font size in real time. The preview function can also add a real-time editing function, allowing the user to directly edit while viewing the preview. For example, adjusting the layout or placement in real time. This allows the user to directly edit while viewing the preview.

[0047] The preview function adds an automatic evaluation function using AI, allowing you to evaluate the quality of a homepage's design and content. The preview function, for example, adds an automatic evaluation function using AI to evaluate the quality of a homepage's design and content. For example, it evaluates the consistency of the design and usability. The preview function also adds an automatic evaluation function using AI to evaluate the quality of a homepage's design and content. For example, it evaluates the appropriateness of the content and SEO measures. The preview function also adds an automatic evaluation function using AI to evaluate the quality of a homepage's design and content. For example, it evaluates accessibility and performance. This allows you to evaluate the quality of a homepage's design and content.

[0048] The preview function may incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the preview function may incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the homepage may be viewed using a VR headset. The preview function may also incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the preview function may provide navigation and interaction within the virtual space. The preview function may also incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the preview function may display the homepage content using 3D models and animations. This may incorporate VR technology to allow the user to experience the homepage in a virtual space.

[0049] The preview feature may add a social sharing feature, allowing users to share the preview with other users and get feedback. For example, the preview feature may add a social sharing feature, allowing users to share the preview with other users, for example, by sending a preview link via social media or email. The preview feature may also add a social sharing feature, allowing users to share the preview with other users, for example, by selecting a platform to share it on and collecting feedback. The preview feature may also add a social sharing feature, allowing users to share the preview with other users, for example, by receiving comments and ratings in real time, allowing users to share the preview with other users and get feedback.

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

[0051] The instruction analysis unit can learn the user's past instruction history and predict the user's preferences and patterns, allowing for more accurate analysis. For example, it can prioritize suggestions of design elements that have been frequently used in the past. It can also prioritize suggestions of colors and layouts that the user prefers. Furthermore, it can predict what the user is likely to specify next based on the instructions the user has given in the past. This allows for more accurate analysis by learning the user's preferences and patterns.

[0052] The instruction analysis unit can analyze verbal instructions in different languages ​​and generate a multilingual homepage. For example, it can analyze instructions in English, French, Chinese, etc. and generate a corresponding homepage. It can also analyze instructions in Spanish, German, Italian, etc. and generate a corresponding homepage. It can also analyze instructions in Japanese, Korean, Russian, etc. and generate a multilingual homepage. This makes it possible to respond to instructions in different languages ​​and generate a multilingual homepage.

[0053] The instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to perform more accurate instruction analysis. For example, it can analyze hand movements and facial expressions when giving instructions to understand the intention of the instruction. It can also analyze eye movements and mouth movements to understand the intention of the instruction. It can also analyze body movements and posture to understand the intention of the instruction. This allows for more accurate instruction analysis by analyzing gestures and facial expressions.

[0054] The generation unit can generate multiple design options based on user instructions and allow the user to select from them. For example, it can propose designs with different layouts and color usage and allow the user to select from them. It can also propose designs with different fonts and image placement and allow the user to select from them. It can also propose designs with different themes and styles and allow the user to select from them. This allows the user to select from multiple design options.

[0055] The generation unit can automatically generate templates specialized for the user's industry or business model, providing more appropriate designs. For example, templates for the food and beverage industry or the IT industry can be provided. Templates for the education industry or the medical industry can also be provided. Furthermore, templates for the fashion industry or the real estate industry can also be provided. This makes it possible to provide templates specialized for industries and business models.

[0056] The generation unit can automatically generate not only homepages but also related marketing materials and presentation materials based on user instructions. For example, it can generate company introduction materials and product catalogs. It can also generate presentation slides and sales materials. It can also generate event guides and promotional materials. This allows the automatic generation of not only homepages but also related marketing materials and presentation materials.

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

[0058] Step 1: The instruction analysis unit analyzes the user's verbal instructions. For example, when a user gives verbal instructions about the design or content of a homepage, the generation AI analyzes those instructions. The generation AI uses voice recognition technology to convert the user's instructions into text and understands their content. The input to the generation AI is a prompt containing instructions about what the user wants the generation AI to do, and the generation AI performs analysis based on that prompt. Step 2: The generation unit generates a homepage based on the instructions analyzed by the instruction analysis unit. For example, if the user instructs, "Place a large image on the top page and insert a company introduction below," the generation AI will follow the instructions and place a large image on the top page, inserting the company introduction below it. Also, if the user instructs, "Change the background color to blue," the generation AI will change the background color to blue. Step 3: The correction unit analyzes the correction instructions for the homepage generated by the generation unit and reflects the corrections. For example, if a user gives an instruction such as "Please make the image on the top page a little smaller," the generation AI analyzes the instruction and makes a correction to make the image on the top page smaller. Step 4: The interface unit provides an interface for the user to give verbal instructions. For example, it provides an interface for inputting voice through a microphone or a voice input interface using a smartphone or tablet. This allows the user to easily give verbal instructions.

[0059] (Example 2) The automatic homepage generation system according to an embodiment of the present invention is a system that automatically generates an ideal homepage simply by having a user give verbal instructions. In this system, a generation AI instantly analyzes the user's verbal instructions and automatically generates a homepage based on those instructions. The system can also automatically modify the homepage by having the user provide verbal instructions for correction. This allows the automatic homepage generation system to easily create an ideal homepage simply by having the user speak. For example, even a user with no knowledge of web design can create a professional homepage simply by providing verbal instructions. Furthermore, since corrections can be easily made verbally, the homepage can be completed quickly and efficiently.

[0060] An automatic homepage generation system according to an embodiment includes an instruction analysis unit, a generation unit, a correction unit, and an interface unit. The instruction analysis unit analyzes a user's verbal instructions. For example, when a user verbally gives instructions regarding the design or content of a homepage, the generation AI analyzes the instructions. The generation AI converts the user's instructions into text using speech recognition technology and understands the content. The generation AI receives input in the form of prompts containing instructions about what the user wants the generation AI to do, and the generation AI performs analysis based on the prompts. The generation unit generates a homepage based on the instructions analyzed by the instruction analysis unit. For example, if a user instructs the generation AI to "place a large image on the homepage and include a company introduction below it," the generation AI will place a large image on the homepage and insert the company introduction below it in accordance with the instructions. Furthermore, if a user instructs the generation AI to "change the background color to blue," the generation AI will change the background color to blue. The correction unit analyzes correction instructions for the homepage generated by the generation unit and incorporates the corrections. For example, if a user issues an instruction such as "Please make the image on the top page a little smaller," the generation AI analyzes the instruction and modifies it to make the image on the top page smaller. The interface unit provides an interface through which the user can issue verbal instructions. For example, it may provide an interface for inputting voice via a microphone or a voice input interface using a smartphone or tablet. This allows the user to easily issue verbal instructions. As a result, the automatic homepage generation system according to the embodiment automatically generates a homepage based on the user's verbal instructions, and modifications can also be made verbally. For example, the user can easily create their ideal homepage just by speaking. Furthermore, since modifications can also be easily made verbally, the homepage can be completed quickly and efficiently.

[0061] The instruction analysis unit can analyze the tone and speed of the user's voice, estimate the user's urgency and importance, and automatically set the priority of instructions. For example, the generation AI of the instruction analysis unit analyzes the tone and speed of the user's voice to estimate the urgency and importance. For example, it detects a hurried tone or a high voice tone and sets the priority of instructions high. The instruction analysis unit also analyzes the tone and speed of the user's voice to estimate the urgency and importance. For example, it detects a calm tone or a low voice tone and sets the priority of instructions low. The instruction analysis unit also analyzes the tone and speed of the user's voice to estimate the urgency and importance. For example, if the user speaks quickly, it determines that the urgency is high and sets the priority of the instruction high. This makes it possible to set the priority of instructions according to the user's urgency and importance.

[0062] The instruction analysis unit learns the user's past instruction history and predicts the user's preferences and patterns, allowing for more accurate analysis. In the instruction analysis unit, for example, the generation AI learns the user's past instruction history and predicts preferences and patterns. For example, it prioritizes suggesting design elements that have been used frequently in the past. In addition, the instruction analysis unit learns the user's past instruction history and predicts preferences and patterns. For example, it prioritizes suggesting colors and layouts that the user prefers. In addition, the instruction analysis unit learns the user's past instruction history and predicts preferences and patterns. For example, it predicts what the user is likely to specify next based on what the user has specified in the past. This allows for learning the user's preferences and patterns, enabling more accurate analysis.

[0063] The instruction analysis unit uses the emotion estimation function to analyze the user's emotional state, and if the user is feeling stressed or dissatisfied, the generation AI can provide appropriate feedback and suggestions. The instruction analysis unit, for example, uses the emotion estimation function to analyze the user's emotional state. For example, if the user is feeling stressed or dissatisfied, the generation AI makes suggestions to help them relax. The instruction analysis unit also uses the emotion estimation function to analyze the user's emotional state. For example, if the user is feeling dissatisfied, the generation AI suggests areas for improvement. The instruction analysis unit also uses the emotion estimation function to analyze the user's emotional state. For example, if the user is feeling stressed, the generation AI makes suggestions to help them relax. This makes it possible to provide feedback and suggestions according to the user's emotional state.

[0064] The instruction analysis unit can analyze oral instructions in different languages ​​and enable the generation of a multilingual homepage. For example, the generation AI analyzes oral instructions in different languages ​​and generates a multilingual homepage. For example, the generation AI analyzes instructions in English, French, Chinese, etc. and generates a corresponding homepage. The instruction analysis unit also analyzes oral instructions in different languages ​​and generates a multilingual homepage. For example, the generation AI analyzes instructions in Spanish, German, Italian, etc. and generates a corresponding homepage. The instruction analysis unit also analyzes oral instructions in different languages ​​and generates a multilingual homepage. For example, the generation AI analyzes instructions in Japanese, Korean, Russian, etc. and generates a corresponding homepage. This makes it possible to respond to instructions in different languages ​​and generate a multilingual homepage.

[0065] The instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to perform more accurate instruction analysis. In the instruction analysis unit, for example, the generation AI analyzes the user's gestures and facial expressions and combines them with verbal instructions to analyze the instructions. For example, it analyzes the hand movements and facial expressions when giving instructions to understand the intention of the instructions. In addition, the instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to analyze the instructions. For example, it analyzes the eye movements and mouth movements when giving instructions to understand the intention of the instructions. In addition, the instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to analyze the instructions. For example, it analyzes the body movements and posture when giving instructions to understand the intention of the instructions. This enables more accurate instruction analysis by analyzing gestures and facial expressions.

[0066] The instruction analysis unit uses the emotion estimation function to analyze the emotion of the user when giving instructions in real time, and can make suggestions to elicit positive emotions. The instruction analysis unit, for example, uses the emotion estimation function to analyze the emotion of the user when giving instructions in real time. For example, it makes suggestions to elicit positive emotions. The instruction analysis unit also uses the emotion estimation function to analyze the emotion of the user when giving instructions in real time. For example, it makes suggestions to allow the user to give instructions in a relaxed manner. The instruction analysis unit also uses the emotion estimation function to analyze the emotion of the user when giving instructions in real time. For example, it makes suggestions to allow the user to enjoy giving instructions. This makes it possible to make suggestions according to the user's emotions, and to elicit positive emotions.

[0067] The generation unit can generate multiple design options based on user instructions and allow the user to select from them. For example, the generation unit generates multiple design options using a generation AI based on user instructions. For example, designs with different layouts and color usage can be proposed and the user can select from them. The generation unit also generates multiple design options using a generation AI based on user instructions. For example, designs with different fonts and image placement can be proposed and the user can select from them. The generation unit also generates multiple design options using a generation AI based on user instructions. For example, designs with different themes and styles can be proposed and the user can select from them. This allows the user to select from multiple design options.

[0068] The generation unit automatically generates templates specialized for the user's industry and business model, enabling the provision of more appropriate designs. In the generation unit, for example, the generation AI automatically generates templates specialized for the user's industry and business model. For example, templates for the food and beverage industry and templates for the IT industry are provided. In addition, the generation unit automatically generates templates specialized for the user's industry and business model. For example, templates for the education industry and templates for the medical industry are provided. In addition, the generation unit automatically generates templates specialized for the user's industry and business model. For example, templates for the fashion industry and templates for the real estate industry are provided. This makes it possible to provide templates specialized for industries and business models.

[0069] The generation unit can use the emotion estimation function to automatically adjust design elements to match the user's emotions. The generation unit, for example, uses the emotion estimation function to automatically adjust design elements to match the user's emotions. For example, bright colors and soft fonts are used to elicit positive emotions. The generation unit also uses the emotion estimation function to automatically adjust design elements to match the user's emotions. For example, calm colors and a simple layout are used to elicit relaxed emotions. The generation unit also uses the emotion estimation function to automatically adjust design elements to match the user's emotions. For example, colorful colors and playful fonts are used to elicit fun. In this way, the design elements can be automatically adjusted to match the user's emotions.

[0070] The generation unit can automatically generate not only a homepage but also related marketing materials and presentation materials based on user instructions. For example, the generation AI automatically generates not only a homepage but also related marketing materials based on user instructions. For example, it generates company introduction materials and product catalogs. The generation unit also automatically generates not only a homepage but also related presentation materials based on user instructions. For example, it generates presentation slides and sales materials. The generation unit also automatically generates not only a homepage but also related marketing materials and presentation materials based on user instructions. For example, it generates event guides and promotional materials. This allows not only a homepage but also related marketing materials and presentation materials to be automatically generated.

[0071] The generation unit also automatically implements SEO measures for the homepage based on user instructions, thereby improving its ranking in search engines. In the generation unit, for example, the generation AI automatically implements SEO measures for the homepage based on user instructions. For example, it optimizes keywords and sets meta tags. In addition, the generation unit automatically implements SEO measures for the homepage based on user instructions. For example, it optimizes internal links and improves page speed. In addition, the generation unit automatically implements SEO measures for the homepage based on user instructions. For example, it optimizes content and acquires external links. In this way, the generation AI automatically implements SEO measures for the homepage and improves its ranking in search engines.

[0072] The generation unit can use the emotion estimation function to dynamically change the homepage content based on the user's emotion, thereby increasing visitor engagement. The generation unit, for example, uses the emotion estimation function to dynamically change the homepage content based on the user's emotion. For example, bright colors and soft fonts are used to elicit positive emotions. The generation unit also uses the emotion estimation function to dynamically change the homepage content based on the user's emotion. For example, calm colors and a simple layout are used to elicit relaxed emotions. The generation unit also uses the emotion estimation function to dynamically change the homepage content based on the user's emotion. For example, colorful colors and playful fonts are used to elicit fun. In this way, the homepage content can be dynamically changed based on the user's emotion, thereby increasing visitor engagement.

[0073] The correction unit analyzes the user's correction instructions, automatically evaluates the extent of the impact of the correction, and can appropriately correct other related parts. For example, the generation AI in the correction unit analyzes the user's correction instructions and automatically evaluates the extent of the impact of the correction. For example, if an instruction is received to make the image on the top page smaller, the image sizes on other pages are also appropriately adjusted. The correction unit also analyzes the user's correction instructions and automatically evaluates the extent of the impact of the correction. For example, if an instruction is received to change the background color, the background colors on other pages are also appropriately adjusted. The correction unit also analyzes the user's correction instructions and automatically evaluates the extent of the impact of the correction. For example, if an instruction is received to change the font size, the font sizes on other pages are also appropriately adjusted. This allows the extent of the impact of the correction to be automatically evaluated, and other related parts to be appropriately corrected.

[0074] The correction unit can analyze the user's correction instructions and provide a comparative preview before and after the correction, allowing the user to confirm the changes. In the correction unit, for example, the generation AI analyzes the user's correction instructions and provides a comparative preview before and after the correction. For example, a preview before and after changing the image size is displayed so that the user can confirm. In addition, the correction unit analyzes the user's correction instructions and provides a comparative preview before and after the correction. For example, a preview before and after changing the background color is displayed so that the user can confirm. In addition, the correction unit analyzes the user's correction instructions and provides a comparative preview before and after the correction. For example, a preview before and after changing the font size is displayed so that the user can confirm. In this way, a comparative preview before and after the correction is provided, allowing the user to confirm the changes.

[0075] The correction unit uses the emotion estimation function to analyze the user's emotion regarding the correction instruction and provides positive feedback, thereby improving user satisfaction. The correction unit, for example, uses the emotion estimation function to analyze the user's emotion regarding the correction instruction. For example, positive feedback is provided to improve user satisfaction. The correction unit also uses the emotion estimation function to analyze the user's emotion regarding the correction instruction. For example, if the user is satisfied, the generation AI provides a compliment. The correction unit also uses the emotion estimation function to analyze the user's emotion regarding the correction instruction. For example, if the user is dissatisfied, the generation AI suggests areas for improvement. In this way, the user's emotion regarding the correction instruction is analyzed and positive feedback is provided, thereby improving user satisfaction.

[0076] The correction unit can analyze the user's correction instructions and make the corrections applicable to other projects and documents. In the correction unit, for example, the generation AI analyzes the user's correction instructions and applies the corrections to other projects. For example, it reflects a specific design change to multiple projects. In addition, the correction unit analyzes the user's correction instructions and applies the corrections to other documents. For example, it reflects a specific font change to multiple documents. In addition, the correction unit analyzes the user's correction instructions and applies the corrections to other projects and documents. For example, it reflects a specific layout change to multiple projects and documents. This makes it possible to apply the corrections to other projects and documents.

[0077] The correction unit analyzes the user's correction instructions, automatically manages versions of the corrections, and allows easy reversion to past versions. In the correction unit, for example, the generation AI analyzes the user's correction instructions, and automatically manages versions of the corrections. For example, it allows easy reversion to the state before the corrections. In addition, the correction unit analyzes the user's correction instructions, and automatically manages versions of the corrections. For example, it saves the correction history, allowing reversion to past versions. In addition, the correction unit analyzes the user's correction instructions, and automatically manages versions of the corrections. For example, it manages the corrections in chronological order, allowing reversion to any version. In this way, the corrections are automatically managed as versions, and it allows easy reversion to past versions.

[0078] The correction unit uses the emotion estimation function to analyze the user's emotions regarding the correction instructions in real time, and can make suggestions to smoothly proceed with the correction process. The correction unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the correction instructions in real time. For example, it makes suggestions to smoothly proceed with the correction process. The correction unit also uses the emotion estimation function to analyze the user's emotions regarding the correction instructions in real time. For example, if the user is feeling stressed, the generation AI makes suggestions to relax. The correction unit also uses the emotion estimation function to analyze the user's emotions regarding the correction instructions in real time. For example, if the user is feeling dissatisfied, the generation AI suggests areas for improvement. In this way, the user's emotions regarding the correction instructions can be analyzed in real time, and suggestions can be made to smoothly proceed with the correction process.

[0079] The interface unit can integrate not only voice recognition but also handwriting input and gesture input, thereby providing multiple input methods. The interface unit, for example, integrates not only voice recognition but also handwriting input into the user interface. For example, it allows instructions to be input by handwriting using a tablet. The interface unit can also integrate not only voice recognition but also gesture input into the user interface. For example, it allows instructions to be input by gestures using a camera. The interface unit can also integrate not only voice recognition but also handwriting input and gesture input into the user interface. For example, it allows instructions to be input by handwriting or gestures using a smartphone. This integrates not only voice recognition but also handwriting input and gesture input, thereby providing multiple input methods.

[0080] The interface unit is equipped with an AI assistant and can provide real-time support when the user gives instructions. The interface unit, for example, has an AI assistant in the user interface and provides real-time support when the user gives instructions. For example, it checks the content of the instructions and makes appropriate suggestions. The interface unit also has an AI assistant in the user interface and provides real-time support when the user gives instructions. For example, it provides guidance when the user is unsure. The interface unit also has an AI assistant in the user interface and provides real-time support when the user gives instructions. For example, it provides an immediate answer when the user asks a question. This makes it possible to provide real-time support when the user gives instructions.

[0081] The interface unit uses the emotion estimation function to enable the user interface to dynamically change the design and functions of the interface depending on the emotional state of the user. For example, the interface unit uses the emotion estimation function to enable the user interface to dynamically change the design depending on the emotional state of the user. For example, bright colors and soft fonts are used to elicit positive emotions. The interface unit also uses the emotion estimation function to enable the user interface to dynamically change the functions depending on the emotional state of the user. For example, a simple layout is provided to elicit relaxed emotions. The interface unit also uses the emotion estimation function to enable the user interface to dynamically change the design and functions depending on the emotional state of the user. For example, colorful colors and playful functions are provided to elicit enjoyment. This allows the design and functions of the interface to dynamically change depending on the emotional state of the user.

[0082] The interface unit can be made compatible with devices such as smart speakers or smart watches, enabling use in a wider variety of environments. For example, the interface unit makes the user interface compatible with smart speakers, enabling voice instructions to be issued. For example, instructions can be issued using Amazon Echo or Google Home. The interface unit can also make the user interface compatible with smart watches, enabling instructions to be issued from the wrist. For example, instructions can be issued using Apple Watch or Samsung Galaxy Watch. The interface unit can also make the user interface compatible with devices such as smart speakers or smart watches, enabling use in a wider variety of environments. For example, instructions can be issued at home or on the go. This makes the interface compatible with devices such as smart speakers or smart watches, enabling use in a wider variety of environments.

[0083] The interface unit can incorporate AR technology to enable the user to give instructions visually. The interface unit, for example, incorporates AR technology into the user interface to enable the user to give instructions visually. For example, instructions are given using AR glasses. The interface unit can also incorporate AR technology into the user interface to enable the user to give instructions visually. For example, instructions are given using an AR display using a smartphone camera. The interface unit can also incorporate AR technology into the user interface to enable the user to give instructions visually. For example, instructions are given using an AR display using a tablet. In this way, AR technology is incorporated to enable the user to give instructions visually.

[0084] The interface unit can use the emotion estimation function to enable the user interface to optimize the interaction method based on the user's emotion. For example, the interface unit uses the emotion estimation function to enable the user interface to optimize the interaction method based on the user's emotion. For example, bright colors and soft fonts are used to elicit positive emotions. The interface unit also uses the emotion estimation function to enable the user interface to optimize the interaction method based on the user's emotion. For example, a simple layout is provided to elicit relaxed emotions. The interface unit also uses the emotion estimation function to enable the user interface to optimize the interaction method based on the user's emotion. For example, colorful colors and playful functions are provided to elicit enjoyment. In this way, the interaction method can be optimized based on the user's emotion.

[0085] The preview function can add a real-time editing function, allowing the user to directly edit while viewing the preview. The preview function can add, for example, a real-time editing function, allowing the user to directly edit while viewing the preview. For example, editing text or images by drag and drop. The preview function can also add a real-time editing function, allowing the user to directly edit while viewing the preview. For example, changing the background color or font size in real time. The preview function can also add a real-time editing function, allowing the user to directly edit while viewing the preview. For example, adjusting the layout or placement in real time. This allows the user to directly edit while viewing the preview.

[0086] The preview function adds an automatic evaluation function using AI, allowing you to evaluate the quality of a homepage's design and content. The preview function, for example, adds an automatic evaluation function using AI to evaluate the quality of a homepage's design and content. For example, it evaluates the consistency of the design and usability. The preview function also adds an automatic evaluation function using AI to evaluate the quality of a homepage's design and content. For example, it evaluates the appropriateness of the content and SEO measures. The preview function also adds an automatic evaluation function using AI to evaluate the quality of a homepage's design and content. For example, it evaluates accessibility and performance. This allows you to evaluate the quality of a homepage's design and content.

[0087] The preview function uses the emotion estimation function to analyze the emotional response of the user when they view the preview and can optimize the preview content. For example, the preview function uses the emotion estimation function to analyze the emotional response of the user when they view the preview. For example, the design and content can be adjusted to elicit positive emotions. The preview function also uses the emotion estimation function to analyze the emotional response of the user when they view the preview. For example, if the user is dissatisfied, the generation AI can suggest areas for improvement. The preview function also uses the emotion estimation function to analyze the emotional response of the user when they view the preview. For example, if the user is satisfied, the generation AI can provide a compliment. This allows the preview content to be optimized by analyzing the emotional response of the user when they view the preview.

[0088] The preview function may incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the preview function may incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the homepage may be viewed using a VR headset. The preview function may also incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the preview function may provide navigation and interaction within the virtual space. The preview function may also incorporate VR technology to allow the user to experience the homepage in a virtual space. For example, the preview function may display the homepage content using 3D models and animations. This may incorporate VR technology to allow the user to experience the homepage in a virtual space.

[0089] The preview feature may add a social sharing feature, allowing users to share the preview with other users and get feedback. For example, the preview feature may add a social sharing feature, allowing users to share the preview with other users, for example, by sending a preview link via social media or email. The preview feature may also add a social sharing feature, allowing users to share the preview with other users, for example, by selecting a platform to share it on and collecting feedback. The preview feature may also add a social sharing feature, allowing users to share the preview with other users, for example, by receiving comments and ratings in real time, allowing users to share the preview with other users and get feedback.

[0090] The preview function uses the emotion estimation function to analyze the emotions of users when they view the preview in real time, and can dynamically change the preview content. For example, the preview function uses the emotion estimation function to analyze the emotions of users when they view the preview in real time. For example, the design and content can be dynamically changed to elicit positive emotions. The preview function also uses the emotion estimation function to analyze the emotions of users when they view the preview in real time. For example, if the user is dissatisfied, the generation AI will suggest areas for improvement. The preview function also uses the emotion estimation function to analyze the emotions of users when they view the preview in real time. For example, if the user is satisfied, the generation AI will provide a compliment. This allows the preview content to be dynamically changed, and the emotions of users when they view the preview to be analyzed in real time.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The instruction analysis unit can analyze the tone and speed of the user's voice, estimate the user's urgency and importance, and automatically set the priority of instructions. For example, it can detect a hurried tone or a high voice tone and set the priority of instructions high. It can also detect a calm tone or a low voice tone and set the priority of instructions low. Furthermore, it can determine that the user speaks quickly and set the priority of instructions high. This makes it possible to set the priority of instructions according to the user's urgency and importance.

[0093] The instruction analysis unit can learn the user's past instruction history and predict the user's preferences and patterns, allowing for more accurate analysis. For example, it can prioritize suggestions of design elements that have been frequently used in the past. It can also prioritize suggestions of colors and layouts that the user prefers. Furthermore, it can predict what the user is likely to specify next based on the instructions the user has given in the past. This allows for more accurate analysis by learning the user's preferences and patterns.

[0094] The instruction analysis unit uses the emotion estimation function to analyze the user's emotional state, and if the user is feeling stressed or dissatisfied, the generation AI can provide appropriate feedback and suggestions. For example, if the user is feeling stressed or dissatisfied, the generation AI will make suggestions to help them relax. Also, if the user is feeling dissatisfied, the generation AI can suggest areas for improvement. Furthermore, if the user is feeling stressed, the generation AI can also make suggestions to help them relax. This makes it possible to provide feedback and suggestions according to the user's emotional state.

[0095] The instruction analysis unit can analyze verbal instructions in different languages ​​and generate a multilingual homepage. For example, it can analyze instructions in English, French, Chinese, etc. and generate a corresponding homepage. It can also analyze instructions in Spanish, German, Italian, etc. and generate a corresponding homepage. It can also analyze instructions in Japanese, Korean, Russian, etc. and generate a multilingual homepage. This makes it possible to respond to instructions in different languages ​​and generate a multilingual homepage.

[0096] The instruction analysis unit analyzes the user's gestures and facial expressions and combines them with verbal instructions to perform more accurate instruction analysis. For example, it can analyze hand movements and facial expressions when giving instructions to understand the intention of the instruction. It can also analyze eye movements and mouth movements to understand the intention of the instruction. It can also analyze body movements and posture to understand the intention of the instruction. This allows for more accurate instruction analysis by analyzing gestures and facial expressions.

[0097] The instruction analysis unit can use the emotion estimation function to analyze the emotion of the user when giving instructions in real time and make suggestions to elicit positive emotions. For example, suggestions can be made to allow the user to give instructions in a relaxed manner. Suggestions can also be made to allow the user to enjoy giving instructions. Furthermore, it is possible to analyze the emotion of the user when giving instructions in real time and make suggestions to elicit positive emotions. This makes it possible to make suggestions based on the user's emotions and elicit positive emotions.

[0098] The generation unit can generate multiple design options based on user instructions and allow the user to select from them. For example, it can propose designs with different layouts and color usage and allow the user to select from them. It can also propose designs with different fonts and image placement and allow the user to select from them. It can also propose designs with different themes and styles and allow the user to select from them. This allows the user to select from multiple design options.

[0099] The generation unit can automatically generate templates specialized for the user's industry or business model, providing more appropriate designs. For example, templates for the food and beverage industry or the IT industry can be provided. Templates for the education industry or the medical industry can also be provided. Furthermore, templates for the fashion industry or the real estate industry can also be provided. This makes it possible to provide templates specialized for industries and business models.

[0100] The generator can use the emotion estimation function to automatically adjust design elements to match the user's emotions. For example, bright colors and soft fonts can be used to elicit positive emotions. Alternatively, calm colors and simple layouts can be used to elicit relaxed emotions. Furthermore, colorful colors and playful fonts can be used to elicit fun. This allows the generator to automatically adjust design elements to match the user's emotions.

[0101] The generation unit can automatically generate not only homepages but also related marketing materials and presentation materials based on user instructions. For example, it can generate company introduction materials and product catalogs. It can also generate presentation slides and sales materials. It can also generate event guides and promotional materials. This allows the automatic generation of not only homepages but also related marketing materials and presentation materials.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The instruction analysis unit analyzes the user's verbal instructions. For example, when a user gives verbal instructions about the design or content of a homepage, the generation AI analyzes those instructions. The generation AI uses voice recognition technology to convert the user's instructions into text and understands their content. The input to the generation AI is a prompt containing instructions about what the user wants the generation AI to do, and the generation AI performs analysis based on that prompt. Step 2: The generation unit generates a homepage based on the instructions analyzed by the instruction analysis unit. For example, if the user instructs, "Place a large image on the top page and insert a company introduction below," the generation AI will follow the instructions and place a large image on the top page, inserting the company introduction below it. Also, if the user instructs, "Change the background color to blue," the generation AI will change the background color to blue. Step 3: The correction unit analyzes the correction instructions for the homepage generated by the generation unit and reflects the corrections. For example, if a user gives an instruction such as "Please make the image on the top page a little smaller," the generation AI analyzes the instruction and makes a correction to make the image on the top page smaller. Step 4: The interface unit provides an interface for the user to give verbal instructions. For example, it provides an interface for inputting voice through a microphone or a voice input interface using a smartphone or tablet. This allows the user to easily give verbal instructions.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an instruction analysis unit that analyzes a user's verbal instruction; a generation unit that generates a homepage based on the instructions analyzed by the instruction analysis unit; a correction unit that analyzes correction instructions for the homepage generated by the generation unit and reflects the corrections; an interface unit that provides an interface for a user to give verbal instructions; A system characterized by:

2. The instruction analysis unit The verbal instructions in different languages ​​are analyzed to enable the creation of a multilingual homepage.

2. The system of claim 1.

3. The generation unit generating a plurality of design options based on the user's instructions and allowing the user to select from the options; 2. The system of claim 1.

4. The correction unit The correction instruction of the user is analyzed, the extent of the influence of the correction is automatically evaluated, and the other related parts are also appropriately corrected.

2. The system of claim 1.

5. The interface unit In addition to voice recognition, handwriting input and gesture input are also integrated to provide multiple input methods.

2. The system of claim 1.

6. The instruction analysis unit The AI ​​analyzes the user's emotional state, and if the user feels stressed or dissatisfied, the AI ​​provides appropriate feedback and suggestions.

2. The system of claim 1.

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