system

Abstract

The system according to this embodiment aims to allow users to easily receive a diagnosis and improvement suggestions for their website's homepage. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives a URL of a website from the user. The analysis unit automatically analyzes the top page of the website based on the URL entered by the reception unit. The provision unit provides the user with a diagnostic report and improvement suggestions generated by the analysis unit.

Description

Technical Field

[0006] ,

[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, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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] 2]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to automatically diagnose and propose improvements for the top page of a website, and specialized knowledge is required.

[0005] The system according to the embodiment aims to enable a user to easily receive a diagnosis and improvement suggestions for the top page of a website.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives a URL of a website from the user. The analysis unit automatically analyzes the top page of the website based on the URL entered by the reception unit. The provision unit provides the user with a diagnostic report and improvement suggestions generated by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to easily receive a diagnosis and improvement suggestions for their website's homepage. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The website AI diagnostic system according to an embodiment of the present invention is a system in which artificial intelligence automatically analyzes the homepage of a website and proposes areas for improvement. When a user enters the URL of a website, the AI ​​automatically analyzes the homepage based on that URL. This analysis includes multiple perspectives such as design, usability, SEO, and conversion rate optimization. The AI ​​evaluates the site from these perspectives and generates a detailed diagnostic report and specific improvement suggestions. For example, an image recognition AI analyzes the visual elements of the homepage and generates design evaluations and improvement suggestions. A natural language processing AI analyzes the site's text content and evaluates it from the perspectives of SEO and user engagement. A pattern recognition AI learns the characteristics of successful websites and proposes the optimal structure and layout. A predictive AI predicts user behavior and generates improvement suggestions to increase the conversion rate. A multimodal AI comprehensively analyzes multiple elements such as text, images, and layout to present a comprehensive evaluation and improvement suggestions. This service enables effective website improvement even without specialized knowledge or a large budget. As a result, it is expected that the online presence of small and medium-sized enterprises and freelancers will improve, leading to business growth. This allows the website AI diagnostic system to automatically analyze a website and provide a diagnostic report and improvement suggestions simply by having the user enter the website's URL.

[0029] The website AI diagnostic system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives the URL of a website from the user. The reception unit provides, for example, an interface for the user to enter the URL of a website. The reception unit sends the URL entered by the user to the analysis unit. The analysis unit automatically analyzes the homepage of the website based on the URL entered by the reception unit. The analysis unit analyzes the visual elements of the homepage using, for example, image recognition AI and generates design evaluations and improvement suggestions. The analysis unit analyzes the text content of the site using natural language processing AI and evaluates it from the perspective of SEO and user engagement. The analysis unit learns the characteristics of successful websites using pattern recognition AI and proposes the optimal structure and layout. The analysis unit predicts user behavior using predictive AI and generates improvement suggestions to improve the conversion rate. The analysis unit comprehensively analyzes multiple elements such as text, images, and layout using multimodal AI and presents a comprehensive evaluation and improvement suggestions. The provision unit provides the user with the diagnostic report and improvement suggestions generated by the analysis unit. The service provider, for example, generates a diagnostic report in PDF format and sends it to the user via email. The service provider can also display the diagnostic report through a web interface. Based on the diagnostic report, the service provider presents specific improvement suggestions. As a result, the website AI diagnostic system according to this embodiment can automatically analyze a website and provide a diagnostic report and improvement suggestions simply by the user entering the website's URL.

[0030] The reception desk provides an interface for users to enter website URLs. Specifically, it designs an intuitive and user-friendly input form to allow users to easily enter URLs. For example, it creates a system where users can simply enter a URL in a text box and click a submit button to send the URL to the analysis department. The reception desk also has a function to automatically validate the format of the entered URL and display an error message if it is not in the correct format. This allows users to start the diagnostic process smoothly without entering an incorrect URL. Furthermore, the reception desk also provides a function that allows users to enter multiple URLs at once. This allows users to diagnose multiple websites at once and work efficiently. The reception desk also has a function to save the user's input history and allow them to reuse URLs they have entered in the past. This saves users the trouble of re-entering URLs of websites they frequently diagnose. When the reception desk sends the URL entered by the user to the analysis department, it uses encryption technology to ensure security. This protects user privacy and allows for the secure transmission of data.

[0031] The analysis department automatically analyzes the homepage of a website based on the URL entered by the reception department. Specifically, it uses image recognition AI to analyze the visual elements of the homepage and generates design evaluations and improvement suggestions. The image recognition AI evaluates the website's color scheme, layout, and image quality, and suggests improvements from the perspectives of visual appeal and usability. It also uses natural language processing AI to analyze the site's text content and evaluate it from the perspectives of SEO and user engagement. The natural language processing AI evaluates the appropriate use of keywords, readability of content, and consistency of information, and suggests improvements for search engine optimization and attracting user interest. Furthermore, it uses pattern recognition AI to learn the characteristics of successful websites and proposes the optimal structure and layout. Based on past data, the pattern recognition AI analyzes commonalities and trends of successful websites and proposes the optimal design and functions that can be applied to the user's website. It uses predictive AI to predict user behavior and generate improvement suggestions to increase the conversion rate. Based on user behavior patterns and past data, the predictive AI predicts future user behavior and proposes specific measures to improve the conversion rate. Using multimodal AI, multiple elements such as text, images, and layout are analyzed in an integrated manner to provide a comprehensive evaluation and improvement suggestions. This allows the analytics department to assess the overall performance of the website and provide comprehensive improvement proposals.

[0032] The service provider will provide users with diagnostic reports and improvement suggestions generated by the analysis department. Specifically, the diagnostic report will be generated in PDF format and sent to the user via email. The PDF report is visually easy to understand and includes detailed analysis results and improvement suggestions. The service provider can also display the diagnostic report through a web interface. Users can check the diagnostic results in real time via a web browser and view detailed information as needed. Based on the diagnostic report, the service provider will present specific improvement suggestions. For example, they will suggest improvements to the website design and content, specific SEO methods, and measures to increase user engagement. Furthermore, the service provider will provide support to users in implementing the suggested improvements. For example, they will provide specific implementation procedures, reference materials, and instructions on how to use tools to ensure that users can proceed with the improvement work smoothly. The service provider will collect feedback from users and continuously improve the accuracy and ease of use of the diagnostic system. This will enable the service provider to provide users with high-quality service and support the improvement of website performance.

[0033] The analysis unit can use image recognition AI to analyze the visual elements of the homepage and generate design evaluations and improvement suggestions. For example, the analysis unit can use image recognition AI to analyze the color balance of the homepage and propose a visually appealing color scheme. The analysis unit can also use image recognition AI to analyze the layout of the homepage and propose a layout that optimizes user eye movement. The analysis unit can use image recognition AI to evaluate the image quality of the homepage and propose the use of high-resolution images. In this way, by using image recognition AI, the visual elements of the homepage can be analyzed in detail, and design evaluations and improvement suggestions can be generated. For example, the image recognition AI can use deep learning algorithms to extract image features and evaluate color balance and layout. The image recognition AI evaluates image resolution and quality and proposes areas for improvement. The image recognition AI proposes a layout that optimizes user eye movement. In this way, the analysis unit can use image recognition AI to analyze the visual elements of the homepage in detail and generate design evaluations and improvement suggestions.

[0034] The analytics department can analyze a website's text content using natural language processing AI and evaluate it from the perspectives of SEO and user engagement. For example, the analytics department can use natural language processing AI to analyze the keyword density of the text and suggest the most suitable keywords for SEO. The analytics department can also use natural language processing AI to evaluate the readability of the text and suggest improvements to enhance user engagement. Furthermore, the analytics department can use natural language processing AI to analyze the tone and style of the text and suggest expressions that match the brand image. This allows for a detailed analysis of a website's text content using natural language processing AI and evaluation from the perspectives of SEO and user engagement. Natural language processing AI analyzes text using techniques such as morphological analysis, grammatical analysis, and semantic analysis. It calculates the keyword density of the text and suggests the most suitable keywords for SEO. It also evaluates the readability of the text and suggests improvements to enhance user engagement. This allows the analytics department to use natural language processing AI to analyze a website's text content in detail and evaluate it from the perspectives of SEO and user engagement.

[0035] The analysis unit can learn the characteristics of successful websites using pattern recognition AI and propose optimal structures and layouts. For example, the analysis unit can learn the placement of navigation menus on successful websites using pattern recognition AI and propose optimal placements. The analysis unit can also learn the content placement on successful websites using pattern recognition AI and propose layouts that enhance user engagement. The analysis unit can also learn the color schemes of successful websites using pattern recognition AI and propose visually appealing color schemes. In this way, by using pattern recognition AI, it is possible to learn the characteristics of successful websites and propose optimal structures and layouts. The pattern recognition AI learns the characteristics of websites using deep learning algorithms, for example. The pattern recognition AI learns characteristics such as the placement of navigation menus, content placement, and color schemes and proposes optimal placements and color schemes. In this way, the analysis unit can learn the characteristics of successful websites using pattern recognition AI and propose optimal structures and layouts.

[0036] The analytics unit can use predictive AI to predict user behavior and generate improvement plans to increase conversion rates. For example, the predictive AI can analyze user click patterns and suggest the optimal placement of CTAs (Call to Action). The predictive AI can also predict user dwell time and generate improvement plans to adjust content placement and length. The predictive AI can identify user drop-off points and suggest improvement plans to prevent drop-off. In this way, by using predictive AI, it is possible to predict user behavior and generate improvement plans to increase conversion rates. The predictive AI predicts user behavior using machine learning algorithms, for example. The predictive AI analyzes data such as user click patterns, dwell time, and drop-off points and suggests optimal improvement plans. In this way, the analytics unit can use predictive AI to predict user behavior and generate improvement plans to increase conversion rates.

[0037] The analysis unit can use multimodal AI to comprehensively analyze multiple elements such as text, images, and layout, and present a comprehensive evaluation and improvement plan. For example, the analysis unit can use multimodal AI to analyze the balance between text and images and propose a visually appealing arrangement. The analysis unit can also use multimodal AI to analyze the relationship between layout and user engagement and propose an optimal layout. The analysis unit can also use multimodal AI to analyze the interaction between text, images, and layout and present a comprehensive improvement plan. In this way, by using multimodal AI, it is possible to comprehensively analyze multiple elements such as text, images, and layout, and present a comprehensive evaluation and improvement plan. For example, multimodal AI uses deep learning algorithms to comprehensively analyze data such as text, images, and layout. Multimodal AI evaluates the balance between text and images and the layout, and proposes an optimal improvement plan. In this way, the analysis unit can use multimodal AI to comprehensively analyze multiple elements such as text, images, and layout, and present a comprehensive evaluation and improvement plan.

[0038] The reception desk can analyze the user's past website diagnostic history and select the optimal reception method. For example, the reception desk can automatically display URLs of websites that the user has frequently diagnosed in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest URLs to be used at specific times based on the user's past diagnostic history. In this way, by analyzing the user's past diagnostic history, the optimal reception method can be selected, improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past diagnostic history data into a generating AI and have the generating AI select the optimal reception method.

[0039] The reception desk can filter URLs based on the user's current projects and areas of interest when a URL is entered. For example, the reception desk can prioritize websites related to the user's current projects. The reception desk can also automatically display highly relevant websites as suggestions based on the user's areas of interest. The reception desk can also analyze the user's past search history and suggest relevant websites. This allows for prioritizing highly relevant websites by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0040] The reception desk can prioritize receiving highly relevant websites based on the user's geographical location information when a URL is entered. For example, if the user is in a specific region, the reception desk will prioritize receiving websites related to that region. The reception desk can also suggest websites containing region-specific information based on the user's geographical location information. If the user is traveling, the reception desk can also prioritize receiving websites related to the travel destination. This allows for a diagnosis that takes region-specific information into account by prioritizing highly relevant websites based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant websites.

[0041] The reception unit can analyze the user's social media activity when a URL is entered and accept relevant websites. For example, the reception unit can prioritize websites that the user has shared on social media. The reception unit can also suggest websites of high interest based on the user's social media activity. The reception unit can also prioritize websites shared by accounts that the user follows. In this way, by analyzing the user's social media activity, it is possible to prioritize websites of high interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant websites.

[0042] The service provider can select the optimal display method when providing a diagnostic report by referring to the user's past diagnostic results. For example, the service provider can suggest the optimal display method based on the display method the user has preferred in the past. The service provider can also analyze the user's past diagnostic results and prioritize the display of relevant information. The service provider can also extract specific patterns from the user's past diagnostic history and select the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past diagnostic results, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past diagnostic result data into a generating AI and have the generating AI select the optimal display method.

[0043] The service provider can customize the diagnostic report based on the user's current projects and areas of interest when providing it. For example, the service provider can prioritize displaying information related to the user's current ongoing projects. The service provider can also customize and display highly relevant information based on the user's areas of interest. The service provider can also analyze the user's past search history and provide relevant information. This allows the service provider to provide highly relevant information by customizing the diagnostic report based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization.

[0044] The service provider can prioritize providing highly relevant reports based on the user's geographical location when delivering diagnostic reports. For example, if the user is in a specific region, the service provider will prioritize displaying information related to that region. The service provider can also provide reports that include region-specific information based on the user's geographical location. If the user is traveling, the service provider can also prioritize providing information related to their travel destination. This allows for diagnostics that take region-specific information into account by prioritizing the provision of highly relevant reports based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant reports.

[0045] The service provider can analyze the user's social media activity and provide relevant reports when providing diagnostic reports. For example, the service provider can provide relevant reports based on information shared by the user on social media. The service provider can also prioritize displaying information of high interest to the user based on their social media activity. The service provider can also provide relevant reports based on information shared by accounts that the user follows. This allows the service provider to prioritize providing information of high interest by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI select relevant reports.

[0046] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0047] The analysis unit can improve the accuracy of the diagnosis by referring to the user's past diagnosis results. For example, based on data from websites the user has previously diagnosed, the analysis unit can learn similar patterns and generate more accurate improvement suggestions. It can also analyze the effectiveness of improvements the user has made in the past and prioritize suggesting successful improvements. It can also extract specific trends from the user's past diagnosis results and provide optimal improvement suggestions. In this way, by referring to the user's past diagnosis results, the accuracy of the diagnosis can be improved and more effective improvement suggestions can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past diagnosis result data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0048] The analysis unit can customize the diagnosis based on the user's current projects and areas of interest. For example, it can prioritize diagnosing websites related to the user's current projects. It can also provide highly relevant improvement suggestions based on the user's areas of interest. It can also analyze the user's past search history and diagnose relevant websites. This allows for the provision of highly relevant improvement suggestions by customizing the diagnosis based on the user's current projects and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the diagnostic customization.

[0049] The analysis unit can perform diagnoses based on the user's geographical location information. For example, if the user is in a specific region, it will prioritize diagnosing websites related to that region. Based on the user's geographical location information, it can also provide improvement suggestions that include region-specific information. If the user is traveling, it can also diagnosing websites related to their travel destination. This allows for the provision of improvement suggestions that take region-specific information into account by performing diagnoses based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the diagnosis.

[0050] The analysis unit can analyze a user's social media activity and diagnose relevant websites. For example, it can prioritize diagnosing websites that the user has shared on social media. It can also diagnose websites of high interest based on the user's social media activity. It can also diagnose websites shared by accounts that the user follows. This allows for the prioritization of websites of high interest by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the diagnosis.

[0051] The service provider can select the optimal display method by referring to the user's past diagnostic results. For example, it can suggest the optimal display method based on the display method the user has preferred in the past. It can also analyze the user's past diagnostic results and prioritize the display of relevant information. It can also extract specific patterns from the user's past diagnostic history and select the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past diagnostic results, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past diagnostic result data into a generating AI and have the generating AI select the optimal display method.

[0052] The service provider can customize the diagnostic report based on the user's current projects and areas of interest. For example, it can prioritize displaying information related to the user's current projects. It can also customize and display highly relevant information based on the user's areas of interest. It can also analyze the user's past search history and provide relevant information. This allows the service provider to provide highly relevant information by customizing the diagnostic report based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization.

[0053] The service provider can prioritize providing highly relevant reports based on the user's geographical location. For example, if the user is in a specific region, it will prioritize displaying information related to that region. It can also provide reports that include region-specific information based on the user's geographical location. If the user is traveling, it can prioritize providing information related to their travel destination. This allows for diagnosis that takes region-specific information into account by prioritizing the provision of highly relevant reports based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant reports.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The reception unit receives the website URL from the user. The reception unit provides an interface for the user to enter the website URL and sends the entered URL to the analysis unit. Step 2: The analysis unit automatically analyzes the website's homepage based on the URL entered by the reception unit. The analysis unit uses image recognition AI, natural language processing AI, pattern recognition AI, predictive AI, and multimodal AI to comprehensively analyze visual elements, text content, characteristics of successful websites, user behavior, and multiple other factors to generate a comprehensive evaluation and improvement suggestions. Step 3: The service provider provides the user with the diagnostic report and improvement suggestions generated by the analysis unit. The service provider can generate the diagnostic report in PDF format and send it to the user via email or display it through a web interface. They will also provide specific improvement suggestions based on the diagnostic report.

[0056] (Example of form 2) The website AI diagnostic system according to an embodiment of the present invention is a system in which artificial intelligence automatically analyzes the homepage of a website and proposes areas for improvement. When a user enters the URL of a website, the AI ​​automatically analyzes the homepage based on that URL. This analysis includes multiple perspectives such as design, usability, SEO, and conversion rate optimization. The AI ​​evaluates the site from these perspectives and generates a detailed diagnostic report and specific improvement suggestions. For example, an image recognition AI analyzes the visual elements of the homepage and generates design evaluations and improvement suggestions. A natural language processing AI analyzes the site's text content and evaluates it from the perspectives of SEO and user engagement. A pattern recognition AI learns the characteristics of successful websites and proposes the optimal structure and layout. A predictive AI predicts user behavior and generates improvement suggestions to increase the conversion rate. A multimodal AI comprehensively analyzes multiple elements such as text, images, and layout to present a comprehensive evaluation and improvement suggestions. This service enables effective website improvement even without specialized knowledge or a large budget. As a result, it is expected that the online presence of small and medium-sized enterprises and freelancers will improve, leading to business growth. This allows the website AI diagnostic system to automatically analyze a website and provide a diagnostic report and improvement suggestions simply by having the user enter the website's URL.

[0057] The website AI diagnostic system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives the URL of a website from the user. The reception unit provides, for example, an interface for the user to enter the URL of a website. The reception unit sends the URL entered by the user to the analysis unit. The analysis unit automatically analyzes the homepage of the website based on the URL entered by the reception unit. The analysis unit analyzes the visual elements of the homepage using, for example, image recognition AI and generates design evaluations and improvement suggestions. The analysis unit analyzes the text content of the site using natural language processing AI and evaluates it from the perspective of SEO and user engagement. The analysis unit learns the characteristics of successful websites using pattern recognition AI and proposes the optimal structure and layout. The analysis unit predicts user behavior using predictive AI and generates improvement suggestions to improve the conversion rate. The analysis unit comprehensively analyzes multiple elements such as text, images, and layout using multimodal AI and presents a comprehensive evaluation and improvement suggestions. The provision unit provides the user with the diagnostic report and improvement suggestions generated by the analysis unit. The service provider, for example, generates a diagnostic report in PDF format and sends it to the user via email. The service provider can also display the diagnostic report through a web interface. Based on the diagnostic report, the service provider presents specific improvement suggestions. As a result, the website AI diagnostic system according to this embodiment can automatically analyze a website and provide a diagnostic report and improvement suggestions simply by the user entering the website's URL.

[0058] The reception desk provides an interface for users to enter website URLs. Specifically, it designs an intuitive and user-friendly input form to allow users to easily enter URLs. For example, it creates a system where users can simply enter a URL in a text box and click a submit button to send the URL to the analysis department. The reception desk also has a function to automatically validate the format of the entered URL and display an error message if it is not in the correct format. This allows users to start the diagnostic process smoothly without entering an incorrect URL. Furthermore, the reception desk also provides a function that allows users to enter multiple URLs at once. This allows users to diagnose multiple websites at once and work efficiently. The reception desk also has a function to save the user's input history and allow them to reuse URLs they have entered in the past. This saves users the trouble of re-entering URLs of websites they frequently diagnose. When the reception desk sends the URL entered by the user to the analysis department, it uses encryption technology to ensure security. This protects user privacy and allows for the secure transmission of data.

[0059] The analysis department automatically analyzes the homepage of a website based on the URL entered by the reception department. Specifically, it uses image recognition AI to analyze the visual elements of the homepage and generates design evaluations and improvement suggestions. The image recognition AI evaluates the website's color scheme, layout, and image quality, and suggests improvements from the perspectives of visual appeal and usability. It also uses natural language processing AI to analyze the site's text content and evaluate it from the perspectives of SEO and user engagement. The natural language processing AI evaluates the appropriate use of keywords, readability of content, and consistency of information, and suggests improvements for search engine optimization and attracting user interest. Furthermore, it uses pattern recognition AI to learn the characteristics of successful websites and proposes the optimal structure and layout. Based on past data, the pattern recognition AI analyzes commonalities and trends of successful websites and proposes the optimal design and functions that can be applied to the user's website. It uses predictive AI to predict user behavior and generate improvement suggestions to increase the conversion rate. Based on user behavior patterns and past data, the predictive AI predicts future user behavior and proposes specific measures to improve the conversion rate. Using multimodal AI, multiple elements such as text, images, and layout are analyzed in an integrated manner to provide a comprehensive evaluation and improvement suggestions. This allows the analytics department to assess the overall performance of the website and provide comprehensive improvement proposals.

[0060] The service provider will provide users with diagnostic reports and improvement suggestions generated by the analysis department. Specifically, the diagnostic report will be generated in PDF format and sent to the user via email. The PDF report is visually easy to understand and includes detailed analysis results and improvement suggestions. The service provider can also display the diagnostic report through a web interface. Users can check the diagnostic results in real time via a web browser and view detailed information as needed. Based on the diagnostic report, the service provider will present specific improvement suggestions. For example, they will suggest improvements to the website design and content, specific SEO methods, and measures to increase user engagement. Furthermore, the service provider will provide support to users in implementing the suggested improvements. For example, they will provide specific implementation procedures, reference materials, and instructions on how to use tools to ensure that users can proceed with the improvement work smoothly. The service provider will collect feedback from users and continuously improve the accuracy and ease of use of the diagnostic system. This will enable the service provider to provide users with high-quality service and support the improvement of website performance.

[0061] The analysis unit can use image recognition AI to analyze the visual elements of the homepage and generate design evaluations and improvement suggestions. For example, the analysis unit can use image recognition AI to analyze the color balance of the homepage and propose a visually appealing color scheme. The analysis unit can also use image recognition AI to analyze the layout of the homepage and propose a layout that optimizes user eye movement. The analysis unit can use image recognition AI to evaluate the image quality of the homepage and propose the use of high-resolution images. In this way, by using image recognition AI, the visual elements of the homepage can be analyzed in detail, and design evaluations and improvement suggestions can be generated. For example, the image recognition AI can use deep learning algorithms to extract image features and evaluate color balance and layout. The image recognition AI evaluates image resolution and quality and proposes areas for improvement. The image recognition AI proposes a layout that optimizes user eye movement. In this way, the analysis unit can use image recognition AI to analyze the visual elements of the homepage in detail and generate design evaluations and improvement suggestions.

[0062] The analytics department can analyze a website's text content using natural language processing AI and evaluate it from the perspectives of SEO and user engagement. For example, the analytics department can use natural language processing AI to analyze the keyword density of the text and suggest the most suitable keywords for SEO. The analytics department can also use natural language processing AI to evaluate the readability of the text and suggest improvements to enhance user engagement. Furthermore, the analytics department can use natural language processing AI to analyze the tone and style of the text and suggest expressions that match the brand image. This allows for a detailed analysis of a website's text content using natural language processing AI and evaluation from the perspectives of SEO and user engagement. Natural language processing AI analyzes text using techniques such as morphological analysis, grammatical analysis, and semantic analysis. It calculates the keyword density of the text and suggests the most suitable keywords for SEO. It also evaluates the readability of the text and suggests improvements to enhance user engagement. This allows the analytics department to use natural language processing AI to analyze a website's text content in detail and evaluate it from the perspectives of SEO and user engagement.

[0063] The analysis unit can learn the characteristics of successful websites using pattern recognition AI and propose optimal structures and layouts. For example, the analysis unit can learn the placement of navigation menus on successful websites using pattern recognition AI and propose optimal placements. The analysis unit can also learn the content placement on successful websites using pattern recognition AI and propose layouts that enhance user engagement. The analysis unit can also learn the color schemes of successful websites using pattern recognition AI and propose visually appealing color schemes. In this way, by using pattern recognition AI, it is possible to learn the characteristics of successful websites and propose optimal structures and layouts. The pattern recognition AI learns the characteristics of websites using deep learning algorithms, for example. The pattern recognition AI learns characteristics such as the placement of navigation menus, content placement, and color schemes and proposes optimal placements and color schemes. In this way, the analysis unit can learn the characteristics of successful websites using pattern recognition AI and propose optimal structures and layouts.

[0064] The analytics unit can use predictive AI to predict user behavior and generate improvement plans to increase conversion rates. For example, the predictive AI can analyze user click patterns and suggest the optimal placement of CTAs (Call to Action). The predictive AI can also predict user dwell time and generate improvement plans to adjust content placement and length. The predictive AI can identify user drop-off points and suggest improvement plans to prevent drop-off. In this way, by using predictive AI, it is possible to predict user behavior and generate improvement plans to increase conversion rates. The predictive AI predicts user behavior using machine learning algorithms, for example. The predictive AI analyzes data such as user click patterns, dwell time, and drop-off points and suggests optimal improvement plans. In this way, the analytics unit can use predictive AI to predict user behavior and generate improvement plans to increase conversion rates.

[0065] The analysis unit can use multimodal AI to comprehensively analyze multiple elements such as text, images, and layout, and present a comprehensive evaluation and improvement plan. For example, the analysis unit can use multimodal AI to analyze the balance between text and images and propose a visually appealing arrangement. The analysis unit can also use multimodal AI to analyze the relationship between layout and user engagement and propose an optimal layout. The analysis unit can also use multimodal AI to analyze the interaction between text, images, and layout and present a comprehensive improvement plan. In this way, by using multimodal AI, it is possible to comprehensively analyze multiple elements such as text, images, and layout, and present a comprehensive evaluation and improvement plan. For example, multimodal AI uses deep learning algorithms to comprehensively analyze data such as text, images, and layout. Multimodal AI evaluates the balance between text and images and the layout, and proposes an optimal improvement plan. In this way, the analysis unit can use multimodal AI to comprehensively analyze multiple elements such as text, images, and layout, and present a comprehensive evaluation and improvement plan.

[0066] The reception desk can estimate the user's emotions and adjust the timing of URL input based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick URL entry. This reduces user stress and optimizes the input process by adjusting the timing of URL input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0067] The reception desk can analyze the user's past website diagnostic history and select the optimal reception method. For example, the reception desk can automatically display URLs of websites that the user has frequently diagnosed in the past as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest URLs to be used at specific times based on the user's past diagnostic history. In this way, by analyzing the user's past diagnostic history, the optimal reception method can be selected, improving user convenience. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past diagnostic history data into a generating AI and have the generating AI select the optimal reception method.

[0068] The reception desk can filter URLs based on the user's current projects and areas of interest when a URL is entered. For example, the reception desk can prioritize websites related to the user's current projects. The reception desk can also automatically display highly relevant websites as suggestions based on the user's areas of interest. The reception desk can also analyze the user's past search history and suggest relevant websites. This allows for prioritizing highly relevant websites by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the filtering.

[0069] The reception desk can estimate the user's emotions and prioritize the URLs to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize URLs of high importance. If the user is relaxed, the reception desk may also prioritize URLs that require a detailed diagnosis. If the user is in a hurry, the reception desk may also prioritize URLs that can be diagnosed quickly. This allows for diagnosis tailored to the user's needs by prioritizing URLs based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0070] The reception desk can prioritize receiving highly relevant websites based on the user's geographical location information when a URL is entered. For example, if the user is in a specific region, the reception desk will prioritize receiving websites related to that region. The reception desk can also suggest websites containing region-specific information based on the user's geographical location information. If the user is traveling, the reception desk can also prioritize receiving websites related to the travel destination. This allows for a diagnosis that takes region-specific information into account by prioritizing highly relevant websites based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's geographical location information data into a generating AI and have the generating AI select highly relevant websites.

[0071] The reception unit can analyze the user's social media activity when a URL is entered and accept relevant websites. For example, the reception unit can prioritize websites that the user has shared on social media. The reception unit can also suggest websites of high interest based on the user's social media activity. The reception unit can also prioritize websites shared by accounts that the user follows. In this way, by analyzing the user's social media activity, it is possible to prioritize websites of high interest. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI and have the generating AI select relevant websites.

[0072] The service provider can estimate the user's emotions and adjust how the diagnostic report is displayed based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. By adjusting how the diagnostic report is displayed based on the user's emotions, the service provider can provide the optimal display method for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0073] The service provider can select the optimal display method when providing a diagnostic report by referring to the user's past diagnostic results. For example, the service provider can suggest the optimal display method based on the display method the user has preferred in the past. The service provider can also analyze the user's past diagnostic results and prioritize the display of relevant information. The service provider can also extract specific patterns from the user's past diagnostic history and select the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past diagnostic results, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past diagnostic result data into a generating AI and have the generating AI select the optimal display method.

[0074] The service provider can customize the diagnostic report based on the user's current projects and areas of interest when providing it. For example, the service provider can prioritize displaying information related to the user's current ongoing projects. The service provider can also customize and display highly relevant information based on the user's areas of interest. The service provider can also analyze the user's past search history and provide relevant information. This allows the service provider to provide highly relevant information by customizing the diagnostic report based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization.

[0075] The service provider can estimate the user's emotions and prioritize diagnostic reports based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize displaying information of high importance. If the user is relaxed, the service provider may also prioritize displaying detailed information. If the user is in a hurry, the service provider may also prioritize displaying information that can be quickly viewed. In this way, by prioritizing diagnostic reports based on the user's emotions, information tailored to the user's needs 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0076] The service provider can prioritize providing highly relevant reports based on the user's geographical location when delivering diagnostic reports. For example, if the user is in a specific region, the service provider will prioritize displaying information related to that region. The service provider can also provide reports that include region-specific information based on the user's geographical location. If the user is traveling, the service provider can also prioritize providing information related to their travel destination. This allows for diagnostics that take region-specific information into account by prioritizing the provision of highly relevant reports based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or not. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant reports.

[0077] The service provider can analyze the user's social media activity and provide relevant reports when providing diagnostic reports. For example, the service provider can provide relevant reports based on information shared by the user on social media. The service provider can also prioritize displaying information of high interest to the user based on their social media activity. The service provider can also provide relevant reports based on information shared by accounts that the user follows. This allows the service provider to prioritize providing information of high interest by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI select relevant reports.

[0078] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0079] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can perform a simple diagnosis and provide basic improvement suggestions. If the user is relaxed, the analysis unit can perform a detailed diagnosis and provide more specific improvement suggestions. If the user is in a hurry, the analysis unit can perform a rapid diagnosis and provide immediately actionable improvement suggestions. This allows for a diagnosis tailored to the user's needs by adjusting the depth of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The analysis unit can improve the accuracy of the diagnosis by referring to the user's past diagnosis results. For example, based on data from websites the user has previously diagnosed, the analysis unit can learn similar patterns and generate more accurate improvement suggestions. It can also analyze the effectiveness of improvements the user has made in the past and prioritize suggesting successful improvements. It can also extract specific trends from the user's past diagnosis results and provide optimal improvement suggestions. In this way, by referring to the user's past diagnosis results, the accuracy of the diagnosis can be improved and more effective improvement suggestions can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past diagnosis result data into a generating AI and have the generating AI perform the task of improving the accuracy of the diagnosis.

[0081] The analysis unit can customize the diagnosis based on the user's current projects and areas of interest. For example, it can prioritize diagnosing websites related to the user's current projects. It can also provide highly relevant improvement suggestions based on the user's areas of interest. It can also analyze the user's past search history and diagnose relevant websites. This allows for the provision of highly relevant improvement suggestions by customizing the diagnosis based on the user's current projects and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the diagnostic customization.

[0082] The analysis unit can perform diagnoses based on the user's geographical location information. For example, if the user is in a specific region, it will prioritize diagnosing websites related to that region. Based on the user's geographical location information, it can also provide improvement suggestions that include region-specific information. If the user is traveling, it can also diagnosing websites related to their travel destination. This allows for the provision of improvement suggestions that take region-specific information into account by performing diagnoses based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform the diagnosis.

[0083] The analysis unit can analyze a user's social media activity and diagnose relevant websites. For example, it can prioritize diagnosing websites that the user has shared on social media. It can also diagnose websites of high interest based on the user's social media activity. It can also diagnose websites shared by accounts that the user follows. This allows for the prioritization of websites of high interest by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the diagnosis.

[0084] The service provider can estimate the user's emotions and adjust how the diagnostic report is displayed based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. If the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting how the diagnostic report is displayed based on the user's emotions, the optimal display method can be provided for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0085] The service provider can select the optimal display method by referring to the user's past diagnostic results. For example, it can suggest the optimal display method based on the display method the user has preferred in the past. It can also analyze the user's past diagnostic results and prioritize the display of relevant information. It can also extract specific patterns from the user's past diagnostic history and select the optimal display method. This allows the service provider to select the optimal display method by referring to the user's past diagnostic results, thereby improving user convenience. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past diagnostic result data into a generating AI and have the generating AI select the optimal display method.

[0086] The service provider can customize the diagnostic report based on the user's current projects and areas of interest. For example, it can prioritize displaying information related to the user's current projects. It can also customize and display highly relevant information based on the user's areas of interest. It can also analyze the user's past search history and provide relevant information. This allows the service provider to provide highly relevant information by customizing the diagnostic report based on the user's current projects and areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input data on the user's current projects and areas of interest into a generating AI and have the generating AI perform the customization.

[0087] The service provider can estimate the user's emotions and prioritize diagnostic reports based on the estimated emotions. For example, if the user is stressed, it can prioritize displaying information of high importance. If the user is relaxed, it can also prioritize displaying detailed information. If the user is in a hurry, it can also prioritize displaying information that can be quickly viewed. In this way, by prioritizing diagnostic reports based on the user's emotions, information tailored to the user's needs 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. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The service provider can prioritize providing highly relevant reports based on the user's geographical location. For example, if the user is in a specific region, it will prioritize displaying information related to that region. It can also provide reports that include region-specific information based on the user's geographical location. If the user is traveling, it can prioritize providing information related to their travel destination. This allows for diagnosis that takes region-specific information into account by prioritizing the provision of highly relevant reports based on the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select highly relevant reports.

[0089] The following briefly describes the processing flow for example form 2.

[0090] Step 1: The reception unit receives the website URL from the user. The reception unit provides an interface for the user to enter the website URL and sends the entered URL to the analysis unit. Step 2: The analysis unit automatically analyzes the website's homepage based on the URL entered by the reception unit. The analysis unit uses image recognition AI, natural language processing AI, pattern recognition AI, predictive AI, and multimodal AI to comprehensively analyze visual elements, text content, characteristics of successful websites, user behavior, and multiple other factors to generate a comprehensive evaluation and improvement suggestions. Step 3: The service provider provides the user with the diagnostic report and improvement suggestions generated by the analysis unit. The service provider can generate the diagnostic report in PDF format and send it to the user via email or display it through a web interface. They will also provide specific improvement suggestions based on the diagnostic report.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] Each of the multiple elements described above, including the reception unit, analysis unit, and provision 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 control unit 46A of the smart device 14 and provides an interface for the user to enter a website URL. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically analyzes the top page of a website using image recognition AI, natural language processing AI, etc. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the user with a diagnostic report and improvement suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0095] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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).

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] Each of the multiple elements described above, including the reception unit, analysis unit, and provision 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 control unit 46A of the smart glasses 214 and provides an interface for the user to enter a website URL. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically analyzes the top page of a website using image recognition AI, natural language processing AI, etc. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the user with a diagnostic report and improvement suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0111] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reception unit, analysis unit, and provision 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 control unit 46A of the headset terminal 314 and provides an interface for the user to enter a website URL. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically analyzes the top page of a website using image recognition AI, natural language processing AI, etc. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the user with a diagnostic report and improvement suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0138] In 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.

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0141] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. 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.

[0142] 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.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, and provision 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 control unit 46A of the robot 414 and provides an interface for the user to enter a website URL. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and automatically analyzes the top page of a website using image recognition AI, natural language processing AI, etc. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides the user with a diagnostic report and improvement suggestions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] (Note 1) A reception area where the user enters the website URL, An analysis unit that automatically analyzes the top page of a website based on the URL entered by the reception unit, The system includes a provisioning unit that provides the user with a diagnostic report and improvement suggestions generated by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Using image recognition AI, we analyze the visual elements of the homepage and generate design evaluations and improvement suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We use natural language processing AI to analyze the text content of a website and evaluate it from the perspectives of SEO and user engagement. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Using pattern recognition AI, we learn the characteristics of successful websites and propose the optimal structure and layout. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We use predictive AI to forecast user behavior and generate improvement suggestions to increase conversion rates. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Using multimodal AI, multiple elements such as text, images, and layout are analyzed in an integrated manner to provide a comprehensive evaluation and suggest improvements. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of URL input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the user's past website diagnostic history and select the most suitable method of contact. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering a URL, 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 10) The aforementioned reception unit is It estimates the user's sentiment and determines the priority of URLs to accept based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When a user enters a URL, the system prioritizes displaying websites that are more relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a user enters a URL, the system analyzes their social media activity and accepts relevant websites. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, It estimates the user's emotions and adjusts how the diagnostic report is displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, When providing a diagnostic report, the system selects the optimal display method by referring to the user's past diagnostic results. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing diagnostic reports, customize them based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, It estimates the user's emotions and prioritizes diagnostic reports based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, When providing diagnostic reports, we prioritize providing reports that are highly relevant based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing the diagnostic report, we analyze the user's social media activity and provide relevant reports. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0163] 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 the user enters the website URL, An analysis unit that automatically analyzes the top page of a website based on the URL entered by the reception unit, The system includes a provisioning unit that provides the user with a diagnostic report and improvement suggestions generated by the analysis unit. A system characterized by the following features.

2. The aforementioned analysis unit, Using image recognition AI, we analyze the visual elements of the homepage and generate design evaluations and improvement suggestions. The system according to feature 1.

3. The aforementioned analysis unit, We use natural language processing AI to analyze the text content of a website and evaluate it from the perspectives of SEO and user engagement. The system according to feature 1.

4. The aforementioned analysis unit, Using pattern recognition AI, we learn the characteristics of successful websites and propose the optimal structure and layout. The system according to feature 1.

5. The aforementioned analysis unit, Using predictive AI, we predict user behavior and generate improvement suggestions to increase conversion rates. The system according to feature 1.

6. The aforementioned analysis unit, Using multimodal AI, it comprehensively analyzes multiple elements such as text, images, and layout to provide a holistic evaluation and suggest improvements. The system according to feature 1.

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

8. The aforementioned reception unit is We analyze the user's past website diagnostic history and select the most suitable method of contact. The system according to feature 1.

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