system

Abstract

The system according to this embodiment aims to enable even non-technical individuals to intuitively design and implement web pages. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives a photo of a hand-drawn webpage design from the user. The analysis unit analyzes the image uploaded by the reception unit. The generation unit generates design proposals based on the data analyzed by the analysis unit. The generation unit automatically generates HTML / CSS code based on the design generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for non-technical persons to design and implement a web page.

[0005] The system according to the embodiment aims to enable non-technical persons to intuitively design and implement a web page.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives a photo of a hand-drawn webpage design uploaded by the user. The analysis unit analyzes the image uploaded by the reception unit. The generation unit generates design proposals based on the data analyzed by the analysis unit. The generation unit automatically generates HTML / CSS code based on the design generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows even non-technical individuals to intuitively design and implement web pages. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) 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 such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that automatically generates the actual website design and coding by analyzing a photo of a roughly drawn web page design uploaded by a user. This AI agent system allows even non-technical people to intuitively design and immediately implement a website by uploading a photo of their hand-drawn web page design. Specifically, it consists of the following steps: First, the user uploads a photo of their hand-drawn web page design. Next, an image recognition AI analyzes the uploaded image, and a design generation AI generates design proposals based on the analysis data. Finally, a code generation AI automatically generates HTML / CSS code based on the final design. This process reduces the time from design to coding by up to 80%, and reduces the need for expensive designers and developers. In addition, anyone can create a website even without technical skills, and accessibility is improved. The target audience is sole proprietors, freelancers, DIY web designers, and people with creative hobbies, and it solves problems such as barriers to website creation due to lack of technical skills, high design and development costs, and long production times. Leveraging AI technology, even non-technical individuals can easily create websites, significantly reducing costs and time, and providing customizable designs that leverage user creativity. The market size for web design and development is estimated at approximately 2 trillion yen, and if the initial target market is individual business owners and freelancers, it is estimated at approximately 50 billion yen. With the spread of remote work, the acceleration of digital transformation, the evolution of AI technology and improved accessibility, and the increasing web needs of individual business owners and small and medium-sized enterprises, now is the time to enter the market. Let's create a future where everyone can be a designer with this innovative AI agent. With this system, users can upload hand-drawn web page designs as photos, and the AI ​​agent system will automatically analyze, generate designs, and generate HTML / CSS code.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives a photo of a hand-drawn webpage design from the user. For example, the reception unit can receive a photo taken by the user with a smartphone. The reception unit can also receive an image scanned with a scanner. Furthermore, the reception unit can receive a design drawn with a digital pen. The analysis unit analyzes the uploaded image. For example, the analysis unit analyzes the hand-drawn design using image recognition technology. The analysis unit can also identify design elements using pattern matching technology. Furthermore, the analysis unit can analyze the design structure using AI. The generation unit generates design proposals based on the data analyzed by the analysis unit. For example, the generation unit performs template-based generation. The generation unit can also perform automatic generation using AI. Furthermore, the generation unit can generate customizable designs according to the user's requests. The generation unit automatically generates HTML / CSS code based on the generated design. For example, the generation unit generates code based on the framework used. The generation unit can also optimize the code. Furthermore, the generation unit can also generate HTML / CSS code to provide to the user. As a result, the AI ​​agent system according to this embodiment automatically performs analysis, design generation, and HTML / CSS code generation when a user uploads a hand-drawn webpage design as a photograph.

[0030] The reception desk allows users to upload hand-drawn webpage designs as photographs. Users can use their smartphone cameras to photograph their hand-drawn designs and upload the photos to the system. Smartphone cameras have high resolution, allowing them to accurately capture even the fine details of hand-drawn designs. Users can also digitize their hand-drawn designs using a scanner and upload the images. Using a scanner allows for high-precision digitization of hand-drawn designs, enabling more accurate analysis. Furthermore, designs drawn with digital pens can also be uploaded to the reception desk. Digital pens allow for direct drawing on tablets and digital notebooks, and the drawings can be saved as digital data. This allows users to provide hand-drawn designs to the system in various ways, enabling flexible responses. The reception desk centrally manages the uploaded images and prepares them for transmission to the analysis department. It checks the image resolution and format and performs image pre-processing as needed. For example, it performs image noise reduction and contrast adjustment to ensure accurate analysis by the analysis department. This allows the reception desk to efficiently manage designs provided by users and ensure smooth processing of the entire system.

[0031] The analysis unit analyzes the uploaded image. First, it analyzes the hand-drawn design using image recognition technology. This image recognition technology utilizes a neural network based on deep learning to extract features of the hand-drawn design. Specifically, it identifies each element within the design (e.g., buttons, text boxes, image areas, etc.) and recognizes their respective positions and sizes. Next, it identifies the elements of the design using pattern matching technology. Pattern matching technology compares the uploaded design with a predefined template and identifies matching elements. This allows for an understanding of the structure of the hand-drawn design. Furthermore, the analysis unit analyzes the structure of the design using AI. The AI ​​analyzes the overall layout, color scheme, font selection, etc., of the design to understand the design's intent. For example, it analyzes what kind of user interface the hand-drawn design intends and provides information to generate appropriate design proposals based on that. The analysis unit sends these analysis results to the generation unit, providing the data necessary for generating design proposals. This allows the analysis unit to accurately analyze the uploaded hand-drawn design and provide a foundation for the generation unit to generate high-quality design proposals.

[0032] The generation unit generates design proposals based on data analyzed by the analysis unit. First, template-based generation is performed. In template-based generation, pre-prepared design templates are used, and the design is customized based on the data provided by the analysis unit. This allows for the rapid and efficient generation of design proposals. Next, automatic generation is performed using AI. The AI ​​utilizes generative AI and large-scale language models (LLMs) to generate new design proposals based on the user's hand-drawn design. Specifically, the analysis results are input as prompts, and the AI ​​automatically handles the design layout, color scheme, font selection, etc. This allows for the generation of unique design proposals that reflect the user's intentions. Furthermore, the generation unit generates customizable designs according to the user's requests. The user can provide feedback on the generated design proposals and change or add specific elements. The generation unit regenerates the design incorporating the user's feedback to complete the final design proposal. The generation unit automatically generates HTML / CSS code based on the generated design. The code is generated and optimized based on the framework used. For example, it optimizes the code to eliminate redundancy and improve performance. Furthermore, the generation unit generates HTML / CSS code to provide to the user, allowing the user to easily build web pages. This allows the generation unit to generate high-quality design proposals based on the data provided by the analysis unit, and automatically generate HTML / CSS code to provide to the user.

[0033] The reception desk can analyze the user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has frequently used in the past. It can also select the most efficient upload method based on the user's past upload history. Furthermore, the reception desk can analyze the user's past upload history and suggest the optimal upload time. This allows for efficient uploads by selecting the optimal upload method through analysis of the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0034] The reception system can filter uploads based on the user's current projects and areas of interest. For example, the reception system can prioritize uploading designs related to the user's current projects. It can also filter and upload highly relevant designs based on the user's areas of interest. Furthermore, the reception system can upload the most suitable designs according to the progress of the user's projects. This allows for the priority uploading of highly relevant designs by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0035] The reception desk can prioritize uploading highly relevant designs by considering the user's geographical location during the upload process. For example, the reception desk can prioritize uploading designs relevant to a region based on the user's current location. Furthermore, the reception desk can suggest optimal designs considering the user's geographical location. In addition, the reception desk can upload designs that align with local trends based on the user's location. This allows for the priority uploading of region-related designs by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0036] The reception desk can analyze the user's social media activity during the upload process and upload relevant designs. For example, the reception desk can analyze the user's social media activity and prioritize uploading relevant designs. It can also suggest optimal designs considering the user's social media trends. Furthermore, the reception desk can upload highly relevant designs based on the user's social media interests. This allows for the priority uploading of highly relevant designs by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0037] The analysis unit can optimize the analysis algorithm based on the image resolution and quality. For example, the analysis unit can apply a detailed analysis algorithm to high-resolution images. It can also apply noise reduction and correction algorithms to low-resolution images. Furthermore, the analysis unit can select the optimal analysis algorithm according to the image quality. By optimizing the analysis algorithm based on the image resolution and quality, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0038] The analysis unit can apply different analysis methods depending on the design category. For example, the analysis unit can apply a layout analysis method to a layout design. It can also apply a color analysis method to a color design. Furthermore, it can apply a font analysis method to a font design. By applying different analysis methods depending on the design category, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0039] The analysis unit can determine the priority of analysis based on the submission date of the uploaded images. For example, the analysis unit may prioritize the analysis of the most recent images and provide results quickly. The analysis unit may also postpone the analysis of older images. Furthermore, the analysis unit can determine the optimal analysis order based on the submission date. This allows for the rapid provision of analysis results by prioritizing analysis based on the submission date of the uploaded images. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0040] The analysis unit can improve the accuracy of its analysis by referring to relevant design trend data. For example, the analysis unit can refer to the latest design trends and reflect them in the analysis results. The analysis unit can also analyze past design trends to improve the accuracy of its analysis. Furthermore, the analysis unit can select the optimal analysis method based on the trend data. This allows the accuracy of the analysis to be improved by referring to relevant design trend data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0041] The generation unit can adjust the level of detail of the generation based on the importance of the design. For example, the generation unit can perform detailed generation for important design elements. It can also perform simplified generation for less important design elements. Furthermore, the generation unit can select the optimal level of detail of the generation according to the importance of the design. This allows for detailed generation of important design elements by adjusting the level of detail of the generation based on the importance of the design. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0042] The generation unit can apply different generation algorithms depending on the design category. For example, the generation unit can apply a layout generation algorithm to layout designs. It can also apply a color generation algorithm to color designs. Furthermore, it can apply a font generation algorithm to font designs. By applying different generation algorithms depending on the design category, the accuracy of the generation can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0043] The generation unit can determine the generation priority based on the design submission date. For example, the generation unit can prioritize the generation of the most recent design and provide results quickly. The generation unit can also postpone the generation of older designs. Furthermore, the generation unit can determine the optimal generation order based on the submission date. This allows for the rapid provision of generation results by prioritizing generation based on the design submission date. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI.

[0044] The generation unit can adjust the generation order based on the relevance of the designs. For example, the generation unit can prioritize the generation of highly relevant designs and provide results quickly. The generation unit can also postpone the generation of less relevant designs. Furthermore, the generation unit can determine the optimal generation order based on the relevance of the designs. This allows for the priority generation of highly relevant designs by adjusting the generation order based on the relevance of the designs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0045] The generation unit can adjust the level of detail of the generation based on the importance of the code. For example, the generation unit performs detailed generation for important code elements. It can also perform simplified generation for less important code elements. Furthermore, the generation unit can select the optimal level of detail of the generation according to the importance of the code. This allows for detailed generation of important code elements by adjusting the level of detail of the generation based on the importance of the code. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0046] The generation unit can apply different generation algorithms depending on the category of code. For example, it can apply a layout generation algorithm to layout code. It can also apply a style generation algorithm to style code. Furthermore, it can apply a script generation algorithm to script code. By applying different generation algorithms depending on the category of code, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0047] The generation unit can determine the generation priority based on the submission date of the code. For example, the generation unit can prioritize the generation of the most recent code and provide results quickly. The generation unit can also postpone the generation of older code. Furthermore, the generation unit can determine the optimal generation order based on the submission date. This allows for the rapid provision of generation results by prioritizing generation based on the submission date of the code. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0048] The generation unit can adjust the generation order based on the relevance of the code. For example, the generation unit can prioritize the generation of highly relevant code and provide results quickly. The generation unit can also postpone the generation of less relevant code. Furthermore, the generation unit can determine the optimal generation order based on the relevance of the code. This allows for the priority generation of highly relevant code by adjusting the generation order based on the relevance of the code. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0049] The reception unit can select the optimal reception method when receiving feedback by referring to the user's past feedback history. For example, the reception unit may prioritize suggesting feedback methods that the user has frequently used in the past. The reception unit can also select the most efficient feedback method from the user's past feedback history. Furthermore, the reception unit can analyze the user's past feedback history and suggest the optimal time for feedback. In this way, by referring to the user's past feedback history, the reception unit can select the optimal reception method and achieve efficient feedback. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0050] The feedback reception unit can select the optimal method of receiving feedback by considering the user's device information. For example, if the user is using a smartphone, the reception unit can provide a feedback form that is sized to fit the screen. If the user is using a tablet, the reception unit can also provide a feedback form optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible feedback form. This allows the reception unit to provide the optimal feedback reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The reception desk can analyze the user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has frequently used in the past. It can also select the most efficient upload method based on the user's past upload history. Furthermore, the reception desk can analyze the user's past upload history and suggest the optimal upload time. This allows for efficient uploads by selecting the optimal upload method through analysis of the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0053] The reception system can filter uploads based on the user's current projects and areas of interest. For example, the reception system can prioritize uploading designs related to the user's current projects. It can also filter and upload highly relevant designs based on the user's areas of interest. Furthermore, the reception system can upload the most suitable designs according to the progress of the user's projects. This allows for the priority uploading of highly relevant designs by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0054] The reception desk can prioritize uploading highly relevant designs by considering the user's geographical location during the upload process. For example, the reception desk can prioritize uploading designs relevant to a region based on the user's current location. Furthermore, the reception desk can suggest optimal designs considering the user's geographical location. In addition, the reception desk can upload designs that align with local trends based on the user's location. This allows for the priority uploading of region-related designs by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0055] The reception desk can analyze the user's social media activity during the upload process and upload relevant designs. For example, the reception desk can analyze the user's social media activity and prioritize uploading relevant designs. It can also suggest optimal designs considering the user's social media trends. Furthermore, the reception desk can upload highly relevant designs based on the user's social media interests. This allows for the priority uploading of highly relevant designs by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0056] The analysis unit can optimize the analysis algorithm based on the image resolution and quality. For example, the analysis unit can apply a detailed analysis algorithm to high-resolution images. It can also apply noise reduction and correction algorithms to low-resolution images. Furthermore, the analysis unit can select the optimal analysis algorithm according to the image quality. By optimizing the analysis algorithm based on the image resolution and quality, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0057] The analysis unit can apply different analysis methods depending on the design category. For example, the analysis unit can apply a layout analysis method to a layout design. It can also apply a color analysis method to a color design. Furthermore, it can apply a font analysis method to a font design. By applying different analysis methods depending on the design category, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0058] The following briefly describes the processing flow for example form 1.

[0059] Step 1: The reception desk receives a photo of the user's hand-drawn webpage design. For example, the user can upload a photo taken with their smartphone. They can also upload scanned images or designs drawn with a digital pen. Step 2: The analysis unit analyzes the images uploaded by the reception unit. For example, it uses image recognition technology to analyze hand-drawn designs and patterns matching technology and AI to identify the elements and structure of the designs. Step 3: The generation unit generates design proposals based on the data analyzed by the analysis unit. For example, it can generate templates or use AI-based automated generation to create customizable designs according to user requests. Step 4: The generation unit automatically generates HTML / CSS code based on the generated design. For example, it generates code based on the framework used, optimizes the code, and generates HTML / CSS code to be provided to the user.

[0060] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that automatically generates the actual website design and coding by analyzing a photo of a roughly drawn web page design uploaded by a user. This AI agent system allows even non-technical people to intuitively design and immediately implement a website by uploading a photo of their hand-drawn web page design. Specifically, it consists of the following steps: First, the user uploads a photo of their hand-drawn web page design. Next, an image recognition AI analyzes the uploaded image, and a design generation AI generates design proposals based on the analysis data. Finally, a code generation AI automatically generates HTML / CSS code based on the final design. This process reduces the time from design to coding by up to 80%, and reduces the need for expensive designers and developers. In addition, anyone can create a website even without technical skills, and accessibility is improved. The target audience is sole proprietors, freelancers, DIY web designers, and people with creative hobbies, and it solves problems such as barriers to website creation due to lack of technical skills, high design and development costs, and long production times. Leveraging AI technology, even non-technical individuals can easily create websites, significantly reducing costs and time, and providing customizable designs that leverage user creativity. The market size for web design and development is estimated at approximately 2 trillion yen, and if the initial target market is individual business owners and freelancers, it is estimated at approximately 50 billion yen. With the spread of remote work, the acceleration of digital transformation, the evolution of AI technology and improved accessibility, and the increasing web needs of individual business owners and small and medium-sized enterprises, now is the time to enter the market. Let's create a future where everyone can be a designer with this innovative AI agent. With this system, users can upload hand-drawn web page designs as photos, and the AI ​​agent system will automatically analyze, generate designs, and generate HTML / CSS code.

[0061] The AI ​​agent system according to this embodiment comprises a reception unit, an analysis unit, and a generation unit. The reception unit receives a photo of a hand-drawn webpage design from the user. For example, the reception unit can receive a photo taken by the user with a smartphone. The reception unit can also receive an image scanned with a scanner. Furthermore, the reception unit can receive a design drawn with a digital pen. The analysis unit analyzes the uploaded image. For example, the analysis unit analyzes the hand-drawn design using image recognition technology. The analysis unit can also identify design elements using pattern matching technology. Furthermore, the analysis unit can analyze the design structure using AI. The generation unit generates design proposals based on the data analyzed by the analysis unit. For example, the generation unit performs template-based generation. The generation unit can also perform automatic generation using AI. Furthermore, the generation unit can generate customizable designs according to the user's requests. The generation unit automatically generates HTML / CSS code based on the generated design. For example, the generation unit generates code based on the framework used. The generation unit can also optimize the code. Furthermore, the generation unit can also generate HTML / CSS code to provide to the user. As a result, the AI ​​agent system according to this embodiment automatically performs analysis, design generation, and HTML / CSS code generation when a user uploads a hand-drawn webpage design as a photograph.

[0062] The reception desk allows users to upload hand-drawn webpage designs as photographs. Users can use their smartphone cameras to photograph their hand-drawn designs and upload the photos to the system. Smartphone cameras have high resolution, allowing them to accurately capture even the fine details of hand-drawn designs. Users can also digitize their hand-drawn designs using a scanner and upload the images. Using a scanner allows for high-precision digitization of hand-drawn designs, enabling more accurate analysis. Furthermore, designs drawn with digital pens can also be uploaded to the reception desk. Digital pens allow for direct drawing on tablets and digital notebooks, and the drawings can be saved as digital data. This allows users to provide hand-drawn designs to the system in various ways, enabling flexible responses. The reception desk centrally manages the uploaded images and prepares them for transmission to the analysis department. It checks the image resolution and format and performs image pre-processing as needed. For example, it performs image noise reduction and contrast adjustment to ensure accurate analysis by the analysis department. This allows the reception desk to efficiently manage designs provided by users and ensure smooth processing of the entire system.

[0063] The analysis unit analyzes the uploaded image. First, it analyzes the hand-drawn design using image recognition technology. This image recognition technology utilizes a neural network based on deep learning to extract features of the hand-drawn design. Specifically, it identifies each element within the design (e.g., buttons, text boxes, image areas, etc.) and recognizes their respective positions and sizes. Next, it identifies the elements of the design using pattern matching technology. Pattern matching technology compares the uploaded design with a predefined template and identifies matching elements. This allows for an understanding of the structure of the hand-drawn design. Furthermore, the analysis unit analyzes the structure of the design using AI. The AI ​​analyzes the overall layout, color scheme, font selection, etc., of the design to understand the design's intent. For example, it analyzes what kind of user interface the hand-drawn design intends and provides information to generate appropriate design proposals based on that. The analysis unit sends these analysis results to the generation unit, providing the data necessary for generating design proposals. This allows the analysis unit to accurately analyze the uploaded hand-drawn design and provide a foundation for the generation unit to generate high-quality design proposals.

[0064] The generation unit generates design proposals based on data analyzed by the analysis unit. First, template-based generation is performed. In template-based generation, pre-prepared design templates are used, and the design is customized based on the data provided by the analysis unit. This allows for the rapid and efficient generation of design proposals. Next, automatic generation is performed using AI. The AI ​​utilizes generative AI and large-scale language models (LLMs) to generate new design proposals based on the user's hand-drawn design. Specifically, the analysis results are input as prompts, and the AI ​​automatically handles the design layout, color scheme, font selection, etc. This allows for the generation of unique design proposals that reflect the user's intentions. Furthermore, the generation unit generates customizable designs according to the user's requests. The user can provide feedback on the generated design proposals and change or add specific elements. The generation unit regenerates the design incorporating the user's feedback to complete the final design proposal. The generation unit automatically generates HTML / CSS code based on the generated design. The code is generated and optimized based on the framework used. For example, it optimizes the code to eliminate redundancy and improve performance. Furthermore, the generation unit generates HTML / CSS code to provide to the user, allowing the user to easily build web pages. This allows the generation unit to generate high-quality design proposals based on the data provided by the analysis unit, and automatically generate HTML / CSS code to provide to the user.

[0065] The reception desk can estimate the user's emotions and adjust the upload timing based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the upload to give them time to relax. Conversely, if the user is in a hurry, the reception desk can speed up the upload to begin processing quickly. Furthermore, if the user is concentrating, the reception desk can adjust the upload timing to process it at the optimal time. This allows for reduced user stress and optimal upload timing by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0066] The reception desk can analyze the user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has frequently used in the past. It can also select the most efficient upload method based on the user's past upload history. Furthermore, the reception desk can analyze the user's past upload history and suggest the optimal upload time. This allows for efficient uploads by selecting the optimal upload method through analysis of the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0067] The reception system can filter uploads based on the user's current projects and areas of interest. For example, the reception system can prioritize uploading designs related to the user's current projects. It can also filter and upload highly relevant designs based on the user's areas of interest. Furthermore, the reception system can upload the most suitable designs according to the progress of the user's projects. This allows for the priority uploading of highly relevant designs by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0068] The reception desk can estimate the user's emotions and prioritize the designs to upload based on those emotions. For example, if the user is stressed, the reception desk will prioritize uploading simple designs. Conversely, if the user is relaxed, the reception desk may prioritize uploading complex designs. Furthermore, if the user is in a hurry, the reception desk may prioritize uploading designs that can be processed quickly. This allows the system to provide designs that meet the user's needs by prioritizing the designs to be uploaded according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0069] The reception desk can prioritize uploading highly relevant designs by considering the user's geographical location during the upload process. For example, the reception desk can prioritize uploading designs relevant to a region based on the user's current location. Furthermore, the reception desk can suggest optimal designs considering the user's geographical location. In addition, the reception desk can upload designs that align with local trends based on the user's location. This allows for the priority uploading of region-related designs by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0070] The reception desk can analyze the user's social media activity during the upload process and upload relevant designs. For example, the reception desk can analyze the user's social media activity and prioritize uploading relevant designs. It can also suggest optimal designs considering the user's social media trends. Furthermore, the reception desk can upload highly relevant designs based on the user's social media interests. This allows for the priority uploading of highly relevant designs by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0071] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. If the user is in a hurry, the analysis unit can perform a rapid analysis to ensure the minimum necessary accuracy. Furthermore, if the user is stressed, the analysis unit can adjust the accuracy of the analysis to reduce the burden. This allows for the provision of analysis results tailored to the user's needs by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0072] The analysis unit can optimize the analysis algorithm based on the image resolution and quality. For example, the analysis unit can apply a detailed analysis algorithm to high-resolution images. It can also apply noise reduction and correction algorithms to low-resolution images. Furthermore, the analysis unit can select the optimal analysis algorithm according to the image quality. By optimizing the analysis algorithm based on the image resolution and quality, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0073] The analysis unit can apply different analysis methods depending on the design category. For example, the analysis unit can apply a layout analysis method to a layout design. It can also apply a color analysis method to a color design. Furthermore, it can apply a font analysis method to a font design. By applying different analysis methods depending on the design category, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0074] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0075] The analysis unit can determine the priority of analysis based on the submission date of the uploaded images. For example, the analysis unit may prioritize the analysis of the most recent images and provide results quickly. The analysis unit may also postpone the analysis of older images. Furthermore, the analysis unit can determine the optimal analysis order based on the submission date. This allows for the rapid provision of analysis results by prioritizing analysis based on the submission date of the uploaded images. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI.

[0076] The analysis unit can improve the accuracy of its analysis by referring to relevant design trend data. For example, the analysis unit can refer to the latest design trends and reflect them in the analysis results. The analysis unit can also analyze past design trends to improve the accuracy of its analysis. Furthermore, the analysis unit can select the optimal analysis method based on the trend data. This allows the accuracy of the analysis to be improved by referring to relevant design trend data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI.

[0077] The generation unit can estimate the user's emotions and adjust the style of the design it generates based on those emotions. For example, if the user is relaxed, the generation unit can generate a design with soft colors. If the user is in a hurry, it can also generate a simple and intuitive design. Furthermore, if the user is excited, it can generate a visually stimulating design. This allows for the provision of designs that meet user needs by adjusting the style of the generated design according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0078] The generation unit can adjust the level of detail of the generation based on the importance of the design. For example, the generation unit can perform detailed generation for important design elements. It can also perform simplified generation for less important design elements. Furthermore, the generation unit can select the optimal level of detail of the generation according to the importance of the design. This allows for detailed generation of important design elements by adjusting the level of detail of the generation based on the importance of the design. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0079] The generation unit can apply different generation algorithms depending on the design category. For example, the generation unit can apply a layout generation algorithm to layout designs. It can also apply a color generation algorithm to color designs. Furthermore, it can apply a font generation algorithm to font designs. By applying different generation algorithms depending on the design category, the accuracy of the generation can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0080] The generation unit can estimate the user's emotions and adjust the length of the generated design based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise design. If the user is relaxed, the generation unit can also generate a longer design with detailed explanations. Furthermore, if the user is excited, the generation unit can generate a design with visually stimulating effects. By adjusting the length of the generated design according to the user's emotions, it is possible to provide a design that meets the user's needs. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0081] The generation unit can determine the generation priority based on the design submission date. For example, the generation unit can prioritize the generation of the most recent design and provide results quickly. The generation unit can also postpone the generation of older designs. Furthermore, the generation unit can determine the optimal generation order based on the submission date. This allows for the rapid provision of generation results by prioritizing generation based on the design submission date. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI.

[0082] The generation unit can adjust the generation order based on the relevance of the designs. For example, the generation unit can prioritize the generation of highly relevant designs and provide results quickly. The generation unit can also postpone the generation of less relevant designs. Furthermore, the generation unit can determine the optimal generation order based on the relevance of the designs. This allows for the priority generation of highly relevant designs by adjusting the generation order based on the relevance of the designs. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0083] The generation unit can estimate the user's emotions and adjust the style of the generated HTML / CSS code based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate HTML / CSS code with soft colors. If the user is in a hurry, the generation unit can also generate simple and intuitive HTML / CSS code. Furthermore, if the user is excited, the generation unit can generate visually stimulating HTML / CSS code. This allows for the provision of code tailored to the user's needs by adjusting the style of the generated HTML / CSS code according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can adjust the level of detail of the generation based on the importance of the code. For example, the generation unit performs detailed generation for important code elements. It can also perform simplified generation for less important code elements. Furthermore, the generation unit can select the optimal level of detail of the generation according to the importance of the code. This allows for detailed generation of important code elements by adjusting the level of detail of the generation based on the importance of the code. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0085] The generation unit can apply different generation algorithms depending on the category of code. For example, it can apply a layout generation algorithm to layout code. It can also apply a style generation algorithm to style code. Furthermore, it can apply a script generation algorithm to script code. By applying different generation algorithms depending on the category of code, the accuracy of generation can be improved. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI.

[0086] The generation unit can estimate the user's emotions and adjust the length of the generated HTML / CSS code based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise HTML / CSS code. If the user is relaxed, the generation unit can also generate longer HTML / CSS code with detailed explanations. Furthermore, if the user is excited, the generation unit can generate HTML / CSS code with visually stimulating effects. This allows for the provision of code tailored to the user's needs by adjusting the length of the generated HTML / CSS code according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The generation unit can determine the generation priority based on the submission date of the code. For example, the generation unit can prioritize the generation of the most recent code and provide results quickly. The generation unit can also postpone the generation of older code. Furthermore, the generation unit can determine the optimal generation order based on the submission date. This allows for the rapid provision of generation results by prioritizing generation based on the submission date of the code. Some or all of the above processing in the generation unit may be performed using AI, for example, or without using AI.

[0088] The generation unit can adjust the generation order based on the relevance of the code. For example, the generation unit can prioritize the generation of highly relevant code and provide results quickly. The generation unit can also postpone the generation of less relevant code. Furthermore, the generation unit can determine the optimal generation order based on the relevance of the code. This allows for the priority generation of highly relevant code by adjusting the generation order based on the relevance of the code. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI.

[0089] The reception system can estimate the user's emotions and adjust the feedback process based on those emotions. For example, if the user is stressed, the reception system can provide a simple feedback form and minimize the input steps. If the user is relaxed, the reception system can also provide detailed feedback options and suggest a customizable feedback method. Furthermore, if the user is in a hurry, the reception system can prioritize voice feedback and receive feedback quickly. This reduces user stress and ensures optimal feedback by adjusting the feedback process according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The reception unit can select the optimal reception method when receiving feedback by referring to the user's past feedback history. For example, the reception unit may prioritize suggesting feedback methods that the user has frequently used in the past. The reception unit can also select the most efficient feedback method from the user's past feedback history. Furthermore, the reception unit can analyze the user's past feedback history and suggest the optimal time for feedback. In this way, by referring to the user's past feedback history, the reception unit can select the optimal reception method and achieve efficient feedback. Some or all of the above processing in the reception unit may be performed using AI, for example, or without using AI.

[0091] The reception desk can estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, the reception desk will prioritize important feedback. If the user is relaxed, the reception desk can also prioritize detailed feedback. Furthermore, if the user is in a hurry, the reception desk can prioritize feedback that can be processed quickly. This allows for prioritizing important feedback based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0092] The feedback reception unit can select the optimal method of receiving feedback by considering the user's device information. For example, if the user is using a smartphone, the reception unit can provide a feedback form that is sized to fit the screen. If the user is using a tablet, the reception unit can also provide a feedback form optimized for a larger screen. Furthermore, if the user is using a smartwatch, the reception unit can provide a concise and highly visible feedback form. This allows the reception unit to provide the optimal feedback reception method by considering the user's device information. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI.

[0093] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0094] The reception desk can estimate the user's emotions and adjust the upload timing based on the estimated emotions. For example, if the user is stressed, the reception desk can delay the upload to give them time to relax. Conversely, if the user is in a hurry, the reception desk can speed up the upload to begin processing quickly. Furthermore, if the user is concentrating, the reception desk can adjust the upload timing to process it at the optimal time. This allows for reduced user stress and optimal upload timing by adjusting the upload timing according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0095] The reception desk can analyze the user's past upload history and select the optimal upload method. For example, the reception desk may prioritize suggesting upload methods that the user has frequently used in the past. It can also select the most efficient upload method based on the user's past upload history. Furthermore, the reception desk can analyze the user's past upload history and suggest the optimal upload time. This allows for efficient uploads by selecting the optimal upload method through analysis of the user's past upload history. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0096] The reception system can filter uploads based on the user's current projects and areas of interest. For example, the reception system can prioritize uploading designs related to the user's current projects. It can also filter and upload highly relevant designs based on the user's areas of interest. Furthermore, the reception system can upload the most suitable designs according to the progress of the user's projects. This allows for the priority uploading of highly relevant designs by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI.

[0097] The reception desk can estimate the user's emotions and prioritize the designs to upload based on those emotions. For example, if the user is stressed, the reception desk will prioritize uploading simple designs. Conversely, if the user is relaxed, the reception desk may prioritize uploading complex designs. Furthermore, if the user is in a hurry, the reception desk may prioritize uploading designs that can be processed quickly. This allows the system to provide designs that meet the user's needs by prioritizing the designs to be uploaded according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0098] The reception desk can prioritize uploading highly relevant designs by considering the user's geographical location during the upload process. For example, the reception desk can prioritize uploading designs relevant to a region based on the user's current location. Furthermore, the reception desk can suggest optimal designs considering the user's geographical location. In addition, the reception desk can upload designs that align with local trends based on the user's location. This allows for the priority uploading of region-related designs by considering the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0099] The reception desk can analyze the user's social media activity during the upload process and upload relevant designs. For example, the reception desk can analyze the user's social media activity and prioritize uploading relevant designs. It can also suggest optimal designs considering the user's social media trends. Furthermore, the reception desk can upload highly relevant designs based on the user's social media interests. This allows for the priority uploading of highly relevant designs by analyzing the user's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI.

[0100] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis to improve accuracy. If the user is in a hurry, the analysis unit can perform a rapid analysis to ensure the minimum necessary accuracy. Furthermore, if the user is stressed, the analysis unit can adjust the accuracy of the analysis to reduce the burden. This allows for the provision of analysis results tailored to the user's needs by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0101] The analysis unit can optimize the analysis algorithm based on the image resolution and quality. For example, the analysis unit can apply a detailed analysis algorithm to high-resolution images. It can also apply noise reduction and correction algorithms to low-resolution images. Furthermore, the analysis unit can select the optimal analysis algorithm according to the image quality. By optimizing the analysis algorithm based on the image resolution and quality, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0102] The analysis unit can apply different analysis methods depending on the design category. For example, the analysis unit can apply a layout analysis method to a layout design. It can also apply a color analysis method to a color design. Furthermore, it can apply a font analysis method to a font design. By applying different analysis methods depending on the design category, the accuracy of the analysis can be improved. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI.

[0103] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0104] The following briefly describes the processing flow for example form 2.

[0105] Step 1: The reception desk receives a photo of the user's hand-drawn webpage design. For example, the user can upload a photo taken with their smartphone. They can also upload scanned images or designs drawn with a digital pen. Step 2: The analysis unit analyzes the images uploaded by the reception unit. For example, it uses image recognition technology to analyze hand-drawn designs and patterns matching technology and AI to identify the elements and structure of the designs. Step 3: The generation unit generates design proposals based on the data analyzed by the analysis unit. For example, it can generate templates or use AI-based automated generation to create customizable designs according to user requests. Step 4: The generation unit automatically generates HTML / CSS code based on the generated design. For example, it generates code based on the framework used, optimizes the code, and generates HTML / CSS code to be provided to the user.

[0106] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0107] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0108] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0109] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing users to upload photos taken with their smartphones or images scanned with a scanner. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes hand-drawn designs using image recognition technology and pattern matching technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates design proposals based on the analyzed data and automatically generates HTML / CSS code. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0110] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0111] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0112] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0113] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0114] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0115] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0116] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0117] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0118] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0119] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0120] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0121] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0123] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0124] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to upload photos taken with a smartphone or images scanned with a scanner. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes hand-drawn designs using image recognition technology and pattern matching technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates design proposals based on the analyzed data and automatically generates HTML / CSS code. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0126] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0127] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing users to upload photos taken with a smartphone or images scanned with a scanner. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes hand-drawn designs using image recognition technology and pattern matching technology. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, which generates design proposals based on the analyzed data and automatically generates HTML / CSS code. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0142] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0143] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0150] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the reception unit, analysis unit, and generation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing users to upload photos taken with a smartphone or images scanned with a scanner. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes hand-drawn designs using image recognition technology and pattern matching technology. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which generates design proposals based on the analyzed data and automatically generates HTML / CSS code. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various changes are possible.

[0159] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0160] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0161] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0162] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0163] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0164] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0165] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0166] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0167] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0168] 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.

[0169] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0170] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0171] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0172] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0173] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0174] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0175] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0176] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0177] (Note 1) A reception area where users upload hand-drawn webpage designs as photos, An analysis unit analyzes the images uploaded by the reception unit, A generation unit generates design proposals based on the data analyzed by the aforementioned analysis unit, The system includes a generation unit that automatically generates HTML / CSS code based on the design generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and adjusts the upload timing based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When uploading, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates user sentiment and determines the priority of designs to upload based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is During the upload process, the system prioritizes uploading designs that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is During the upload process, the system analyzes the user's social media activity and uploads relevant designs. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, Optimize the analysis algorithm based on image resolution and quality. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, Apply different analysis methods depending on the design category. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The priority of analysis is determined based on the submission date of the uploaded images. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Referencing relevant design trend data improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is It estimates the user's emotions and adjusts the design style generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is Adjust the level of detail generated based on the importance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Apply different generative algorithms depending on the design category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is It estimates the user's emotions and adjusts the length of the generated design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is Prioritize generation based on the design submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Adjust the generation order based on the relevance of the design. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is It estimates the user's emotions and adjusts the style of the generated HTML / CSS code based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is Adjust the level of detail generated based on the importance of the code. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is Apply different generation algorithms depending on the category of the code. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's emotions and adjusts the length of the generated HTML / CSS code based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is The generation priority is determined based on the code submission date. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is Adjust the generation order based on the relevance of the code. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned reception unit is It estimates the user's emotions and adjusts the feedback collection method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned reception unit is When receiving feedback, the system will refer to the user's past feedback history to select the most suitable method for receiving it. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned reception unit is It estimates the user's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned reception unit is When receiving feedback, the system selects the most suitable method of receiving it, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0178] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area where users upload hand-drawn webpage designs as photos, An analysis unit analyzes the images uploaded by the reception unit, A generation unit generates design proposals based on the data analyzed by the aforementioned analysis unit, The system includes a generation unit that automatically generates HTML / CSS code based on the design generated by the generation unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and adjusts the upload timing based on those emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past upload history and select the optimal upload method. The system according to feature 1.

4. The aforementioned reception unit is When uploading, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates user sentiment and determines the priority of designs to upload based on the estimated user sentiment. The system according to feature 1.

6. The aforementioned reception unit is During the upload process, the system prioritizes uploading designs that are highly relevant to the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is During the upload process, the system analyzes the user's social media activity and uploads relevant designs. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

Images