system

Abstract

To provide a system capable of automatically determining the category and purpose of a Website and evaluating a SEO state and accessibility.SOLUTION: A system according to an embodiment comprises a collection unit, a determination unit, an evaluation unit, and a proposal unit. The collection unit collects data on a Website. The determination unit determines the category and purpose of the Website on the basis of the data collected by the collection unit. The evaluation unit evaluates the SEO state and accessibility of the Website on the basis of the data collected by the collection unit. The proposal unit proposes the optimization of the Website on the basis of the data collected by the collection unit.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Existing technologies are not sufficient to automatically determine a website's category and purpose, and to evaluate its SEO status and accessibility, so there is room for improvement.

[0005] The system according to the embodiment aims to automatically determine the category and purpose of a website and evaluate its SEO status and accessibility. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a determination unit, an evaluation unit, and a suggestion unit. The collection unit collects data related to a website. The determination unit determines a category and purpose of the website based on the data collected by the collection unit. The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. The suggestion unit suggests optimization of the website based on the data collected by the collection unit. Effect of the Invention

[0007] The system according to the embodiment can automatically determine the category and purpose of a website and evaluate its SEO status and accessibility. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

[0014] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 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." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

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

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

[0028] (Example 1) The automatic classification and analysis system for websites according to an embodiment of the present invention is a system that collects data such as the content, design, and user behavior of a website, and automatically determines the category and purpose of the website using AI. This system has a function of evaluating the SEO status and accessibility of the website, analyzing user traffic and behavior, and making optimization suggestions for the website. For example, a collection unit collects data related to the website. This data includes the content, design, and user behavior of the website. For example, the text, images, layout, user click history, and stay time of the webpage are collected. Next, a determination unit determines the category and purpose of the website based on the collected data. A machine learning algorithm is used for the determination, and determination in multiple languages ​​is also possible. For example, categories such as news sites, shopping sites, and blogs are determined. Furthermore, an evaluation unit evaluates the SEO status and accessibility of the website based on the collected data. For example, appropriateness of meta tags, page loading speed, mobile friendliness, and the like are evaluated. Finally, a proposal unit makes optimization suggestions for the website based on the collected data, the determination results, and the evaluation results. The proposal takes into account the results of A / B tests and the engagement rate of users. For example, it suggests changes to the page layout, adding content, optimizing links, etc. This system allows website operators to take concrete actions to improve their site's performance.

[0029] The system for automatically classifying and analyzing websites according to the embodiment includes a collection unit, a determination unit, an evaluation unit, and a suggestion unit. The collection unit collects data related to websites. The data related to websites includes, for example, website content, design, user behavior, and the like, but is not limited to such examples. The collection unit collects, for example, text, images, layout, user click history, stay time, and the like of web pages. The collection unit can also collect data using AI. For example, the collection unit analyzes text data of web pages to extract important keywords. The collection unit can also analyze image data of web pages using image recognition technology. Furthermore, the collection unit can analyze user click history and stay time to collect user behavior data. The determination unit determines the category and purpose of the website based on the data collected by the collection unit. A machine learning algorithm is used for the determination. For example, the determination unit analyzes the collected text data to determine the category of the website. Furthermore, the determination unit can analyze the collected image data to determine the purpose of the website. Furthermore, the determination unit can make a determination in multiple languages. For example, the determination unit can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. AI can be used for the evaluation. For example, the evaluation unit evaluates the appropriateness of meta tags. The evaluation unit can also evaluate the page loading speed. Furthermore, the evaluation unit can evaluate mobile friendliness. The suggestion unit makes optimization suggestions for the website based on the data collected by the collection unit, the judgment result by the determination unit, and the evaluation result by the evaluation unit. AI can be used for the proposal. For example, the suggestion unit makes optimization suggestions based on the results of A / B testing. The suggestion unit can also make optimization suggestions based on the user engagement rate. As a result, the automatic website classification and analysis system according to the embodiment can provide specific actions to improve the performance of the website.

[0030] The collection unit collects data related to the website. The data related to the website includes, but is not limited to, for example, the contents, design, and user behavior of the website. The collection unit collects, for example, the text, images, layout, user click history, and stay time of the webpage. Specifically, the collection unit uses a web crawler to collect text data of the webpage, and uses natural language processing technology to extract important keywords and phrases. This allows the content of the website to be analyzed in detail. In addition, the image data of the webpage is analyzed using image recognition technology to extract the contents and features of the image. For example, objects and text contained in the image are recognized, and the information is stored in a database. Furthermore, in order to analyze the user's click history and stay time, a tracking code using JavaScript (registered trademark) is embedded in the webpage, and user behavior data is collected in real time. This allows a detailed understanding of which pages the user visited, which links he or she clicked, and how much time he or she spent on each page. The collection unit can manage these data in a centralized manner and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server, and can be accessed by the judgment unit and evaluation unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0031] The determination unit determines the category and purpose of the website based on the data collected by the collection unit. A machine learning algorithm is used for the determination. Specifically, the determination unit analyzes the collected text data and determines the category of the website. For example, the text data is tokenized using natural language processing technology, and the main theme or topic of the website is identified by analyzing the frequency of occurrence and co-occurrence of each token. The determination unit can also analyze the collected image data and determine the purpose of the website. For example, the content of the image is classified using image recognition technology, and if a website contains many images related to a specific category, the website is determined to belong to that category. Furthermore, the determination unit can make determinations in multiple languages. For example, the determination unit can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. For this, a multilingual natural language processing model can be used to analyze text data in different languages ​​using the same criteria and make accurate determinations. The determination unit can record the category and purpose of the website in a database based on these analysis results and share them with other departments and systems. This allows the determination unit to quickly and accurately analyze the collected data and clearly grasp the characteristics of the website.

[0032] The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. AI can be used for the evaluation. Specifically, the evaluation unit evaluates the appropriateness of meta tags. For example, the evaluation unit analyzes the meta tags of a webpage and checks whether they contain appropriate keywords from the perspective of search engine optimization (SEO). The evaluation unit can also evaluate the page loading speed. For example, the evaluation unit measures the loading time of a webpage and identifies areas that need optimization. Furthermore, the evaluation unit can evaluate mobile friendliness. For example, the evaluation unit checks whether a webpage is displayed properly on a mobile device and evaluates the application status of responsive design. Based on these evaluation results, the evaluation unit can identify improvements to the website and propose specific improvement measures. For example, the evaluation unit proposes specific methods for improving the improvement of meta tags and the page loading speed. The evaluation unit also evaluates from the perspective of accessibility. For example, the evaluation unit checks the availability of screen reader support and keyboard operation and makes suggestions for improving accessibility. As a result, the evaluation unit can comprehensively evaluate the SEO status and accessibility of the website and provide specific improvement measures.

[0033] The suggestion unit makes optimization suggestions for the website based on the data collected by the collection unit, the judgment result by the judgment unit, and the evaluation result by the evaluation unit. AI can be used for the suggestions. Specifically, the suggestion unit makes optimization suggestions based on the results of A / B testing. For example, different designs and content versions are compared to identify the version with the highest user engagement rate. The suggestion unit can also make optimization suggestions based on the user's engagement rate. For example, the suggestion unit analyzes the user's click history and stay time to evaluate how specific content and design affect the user. Furthermore, the suggestion unit also makes optimization suggestions from the perspective of SEO. For example, the suggestion unit suggests appropriate keyword selection, improvement of meta tags, optimization of internal links, etc. The suggestion unit also makes suggestions for improving accessibility. For example, the suggestion unit suggests strengthening screen reader support, improving keyboard operation, and design for color blindness. The suggestion unit can provide these suggestions as a specific action plan and show specific methods for improving website performance. As a result, the suggestion unit can comprehensively support website optimization and achieve improved user experience and search engine ranking.

[0034] The collection unit can collect text data, layout data, and user operation data of a website as data related to the website. The collection unit, for example, collects text data of a website. The text data includes, but is not limited to, article contents, comments, reviews, etc. The collection unit, for example, collects layout data of a website. The layout data includes, but is not limited to, page configurations, design elements, etc. The collection unit, for example, collects user operation data. The operation data includes, but is not limited to, the number of clicks, scrolling actions, and stay times. This allows for more detailed analysis by collecting data on the content, design, and user behavior of a website. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input text data of a website into AI and extract important keywords.

[0035] The suggestion unit can make improvement suggestions for the website based on the data collected by the collection unit, the judgment result by the judgment unit, and the evaluation result by the evaluation unit. The suggestion unit makes improvement suggestions based on, for example, the data collected by the collection unit. For example, the suggestion unit analyzes text data of the website and proposes the addition or modification of content. The suggestion unit can also analyze layout data of the website and propose changes to the layout of the page. The suggestion unit can also analyze user operation data and propose link optimization. The suggestion unit makes improvement suggestions based on, for example, the judgment result by the judgment unit. For example, the suggestion unit proposes the addition of content suitable for the category based on the category of the website. The suggestion unit can also propose design changes suitable for the purpose based on the purpose of the website. The suggestion unit makes improvement suggestions based on, for example, the evaluation result by the evaluation unit. For example, the suggestion unit proposes meta tag corrections based on the evaluation result of the SEO state. The suggestion unit can also propose improvements to the page loading speed based on the evaluation result of accessibility. This makes it possible to make optimization suggestions for the website based on the collected data, the judgment result, and the evaluation result. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the data collected by the collection unit to AI and generate optimization suggestions.

[0036] The determination unit can determine the category and purpose of the website using a machine learning algorithm. The determination unit, for example, determines the category of the website using a machine learning algorithm. For example, the determination unit analyzes the collected text data and determines the category, such as a news site, a shopping site, or a blog. The determination unit can also analyze the collected image data and determine the purpose of the website. For example, the determination unit uses image recognition technology to determine whether the website is intended to provide information or to promote sales. Furthermore, the determination unit can make determinations in multiple languages. For example, the determination unit can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. As a result, the use of the machine learning algorithm improves the accuracy of determining the category and purpose of the website. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the collected text data to the generation AI and cause the generation AI to perform the determination of the category and purpose.

[0037] The suggestion unit can make optimization suggestions based on the results of the A / B test. For example, the suggestion unit can analyze the results of the A / B test and suggest changes to the layout of the page. The suggestion unit can also suggest adding or modifying content based on the results of the A / B test. Furthermore, the suggestion unit can also suggest link optimization based on the results of the A / B test. This makes it possible to make more effective optimization suggestions based on the results of the A / B test. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the results of the A / B test into AI to generate optimization suggestions.

[0038] The determination unit can determine the category and purpose of a website in multiple languages. The determination unit can determine the category and purpose of a website in languages ​​such as English, Japanese, and Chinese. For example, the determination unit analyzes English text data and determines categories such as news sites, shopping sites, and blogs. The determination unit can also analyze Japanese text data and determine the purpose of a website. Furthermore, the determination unit can analyze Chinese text data and determine the category and purpose of a website. This makes it possible to make determinations in multiple languages, and can also handle international websites. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input text data in multiple languages ​​to the AI ​​and cause the generation AI to perform category and purpose determination.

[0039] The suggestion unit can make optimization suggestions based on the user's engagement rate. For example, the suggestion unit analyzes engagement data such as the user's click rate, stay time, and number of shares, and proposes changes to the page layout. The suggestion unit can also propose adding or modifying content based on the user's engagement rate. Furthermore, the suggestion unit can also propose link optimization based on the user's engagement rate. This makes it possible to make more effective optimization suggestions based on the user's engagement rate. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's engagement data into AI to generate optimization suggestions.

[0040] The collection unit can analyze past data of the website and select the optimal data collection method. The collection unit, for example, analyzes past traffic data and concentrates data collection during peak hours. The collection unit can also focus on collecting data for specific pages or sections based on past user behavior data. Furthermore, the collection unit can analyze past errors and problems and select a method for improving the accuracy of data collection. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input past traffic data into AI and select the optimal data collection method.

[0041] The collection unit can perform filtering based on the current traffic situation of the website and the behavioral patterns of the user when collecting data. For example, when the current traffic is high, the collection unit preferentially collects only important data. The collection unit can also analyze the behavioral patterns of the user and focus on collecting data related to specific behavior. Furthermore, the collection unit can collect detailed data during times of low traffic to distribute the load on the system. This allows important data to be preferentially collected by filtering the data based on the current traffic situation and the behavioral patterns of the user. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the current traffic data into AI and perform filtering.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the website when collecting data. For example, when there is a lot of access from a specific area, the collection unit preferentially collects data related to that area. In addition, the collection unit can analyze user behavior patterns for each area based on the geographical location information and collect highly relevant data. Furthermore, the collection unit can adjust the priority of data collection in consideration of the traffic situation for each area. As a result, highly relevant data can be preferentially collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and preferentially collect highly relevant data.

[0043] The collection unit can analyze the social media activity of the website and collect related data when collecting data. For example, the collection unit can focus on collecting data for pages and content that are frequently mentioned on social media. The collection unit can also analyze social media trends and collect related data. Furthermore, the collection unit can determine the priority of data to be collected based on user responses on social media. This allows related data to be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input social media data into AI and collect related data.

[0044] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between websites when making the determination. The determination unit, for example, analyzes link relationships between websites and determines highly related categories. The determination unit can also determine the purpose based on mutual links between websites. Furthermore, the determination unit can improve the accuracy of the category by taking into account the interrelationships between websites. As a result, the accuracy of the determination is improved by taking into account the interrelationships between websites. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input link data between websites into AI and make a determination taking into account the interrelationships.

[0045] The determination unit can make a determination by taking into consideration attribute information of the operator of the website at the time of determination. The determination unit can determine a category based on, for example, the industry or occupation of the operator of the website. The determination unit can also determine a purpose by taking into consideration the past activity history of the operator of the website. Furthermore, the determination unit can improve the accuracy of the determination based on the attribute information of the operator of the website. As a result, the accuracy of the determination is improved by taking into consideration the attribute information of the operator. A part or all of the above-mentioned processing in the determination unit can be performed, for example, using AI or can be performed without using AI. For example, the determination unit can input the attribute information of the operator into AI and determine a category and purpose.

[0046] The determination unit can make a determination taking into consideration the geographical distribution of websites when making a determination. For example, the determination unit gives priority to geographically close websites. The determination unit can also determine a category based on the geographical distribution. Furthermore, the determination unit can determine a purpose taking into consideration the geographical distribution. As a result, the accuracy of the determination is improved by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input geographical distribution data into AI to determine a category and a purpose.

[0047] The determination unit can improve the accuracy of the determination by referring to related literature on the website at the time of determination. The determination unit, for example, determines the category based on the related literature. The determination unit can also determine the purpose by referring to the related literature. Furthermore, the determination unit can improve the accuracy of the determination by taking the related literature into consideration. As a result, the accuracy of the determination is improved by referring to the related literature. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input related literature data into AI to determine the category and purpose.

[0048] The evaluation unit can predict the current evaluation by referring to past evaluation data at the time of evaluation. The evaluation unit can predict the current SEO state based on, for example, past evaluation data. The evaluation unit can also predict the current accessibility by referring to the past evaluation data. Furthermore, the evaluation unit can analyze the past evaluation data and predict the current evaluation. As a result, the current evaluation can be predicted by referring to the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input the past evaluation data into AI and predict the current evaluation.

[0049] The evaluation unit can apply different evaluation methods to each website category during evaluation. For example, the evaluation unit can apply an evaluation method that emphasizes update frequency and reliability to a news site. The evaluation unit can also apply an evaluation method that emphasizes the richness of product information and user reviews to a shopping site. Furthermore, the evaluation unit can apply an evaluation method that emphasizes the quality of content and reader engagement to a blog. This allows for more appropriate evaluation by applying different evaluation methods to each category. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input evaluation data for each category into AI and apply different evaluation methods.

[0050] The evaluation unit can analyze the change in the evaluation based on the submission time of the website at the time of evaluation. For example, if the submission time is old, the evaluation unit adjusts the evaluation criteria and performs the evaluation. Also, if the submission time is new, the evaluation unit can apply the latest evaluation criteria and perform the evaluation. Furthermore, the evaluation unit can analyze the change in the evaluation based on the submission time and provide the evaluation result. This enables more appropriate evaluation by analyzing the change in the evaluation based on the submission time. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submission time data into AI and analyze the change in the evaluation.

[0051] The evaluation unit can analyze the evaluation by referring to relevant market data of the website at the time of evaluation. The evaluation unit can, for example, evaluate the SEO status based on the relevant market data. The evaluation unit can also evaluate the accessibility by referring to the relevant market data. Furthermore, the evaluation unit can provide an evaluation result by taking into account the relevant market data. Thereby, by referring to the relevant market data, the accuracy of the evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or can be performed without using AI. For example, the evaluation unit can input the relevant market data into AI and analyze the evaluation.

[0052] At the time of proposal, the suggestion unit can select an optimal proposal method by analyzing the past optimization history of the website. The suggestion unit selects an effective proposal method based on, for example, the past optimization history. The suggestion unit can also analyze the past optimization history and avoid failed proposal methods. Furthermore, the suggestion unit can customize an optimal proposal method by referring to the past optimization history. In this way, the optimal proposal method can be selected by analyzing the past optimization history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input past optimization history data into AI to select an optimal proposal method.

[0053] The suggestion unit can customize the optimization suggestion means based on the current traffic status of the website at the time of suggestion. For example, when the current traffic is high, the suggestion unit makes a suggestion to distribute the traffic. Also, when the current traffic is low, the suggestion unit can make a suggestion to increase the traffic. Furthermore, the suggestion unit can customize the optimal suggestion means based on the current traffic status. This enables more effective suggestions by customizing the suggestion means based on the current traffic status. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input current traffic data into AI to customize the optimal suggestion means.

[0054] The suggestion unit can select the optimal suggestion method by considering the geographical location information of the website when making a suggestion. For example, when there is a lot of access from a specific region, the suggestion unit makes a suggestion related to that region. In addition, the suggestion unit can analyze user behavior patterns for each region based on the geographical location information and select the optimal suggestion method. Furthermore, the suggestion unit can select the optimal suggestion method by considering the traffic situation for each region. In this way, the optimal suggestion method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input geographical location information into AI and select the optimal suggestion method.

[0055] At the time of making a proposal, the suggestion unit can analyze the social media activity of the website and propose a means of optimization proposal. For example, the suggestion unit makes an optimization proposal for a page or content that is frequently mentioned on social media. The suggestion unit can also analyze social media trends and make related optimization proposals. Furthermore, the suggestion unit can propose a means of optimization proposal based on user responses on social media. This enables more effective optimization proposals by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input social media data into AI and propose a means of optimization proposal.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze past data of the website and select the optimal data collection method. For example, past traffic data can be analyzed and data collection can be concentrated during peak hours. Also, based on past user behavior data, data can be collected with a focus on specific pages or sections. Furthermore, past errors and problems can be analyzed and a method for improving the accuracy of data collection can be selected. Thus, by analyzing past data, the optimal data collection method can be selected.

[0058] The collection unit can filter data based on the current traffic status of the website and the user's behavioral patterns when collecting data. For example, when the current traffic is high, only important data is collected preferentially. In addition, the user's behavioral patterns can be analyzed and data related to specific actions can be collected with a focus. Furthermore, detailed data can be collected during periods of low traffic, distributing the load on the system. This makes it possible to prioritize the collection of important data by filtering data based on the current traffic status and the user's behavioral patterns.

[0059] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between websites when making a determination. For example, the link relationships between websites are analyzed to determine highly related categories. Also, the purpose can be determined based on mutual links between websites. Furthermore, the accuracy of the category can be improved by taking into account the interrelationships between websites. In this way, the accuracy of the determination is improved by taking into account the interrelationships between websites.

[0060] During evaluation, the evaluation unit can predict a current evaluation by referring to past evaluation data. For example, the current SEO state can be predicted based on past evaluation data. The evaluation unit can also predict current accessibility by referring to past evaluation data. Furthermore, the evaluation unit can analyze past evaluation data and predict a current evaluation. In this way, the current evaluation can be predicted by referring to past evaluation data.

[0061] When making a proposal, the proposal unit can select an optimal proposal method by analyzing the past optimization history of the website. For example, an effective proposal method can be selected based on the past optimization history. Also, the past optimization history can be analyzed to avoid unsuccessful proposal methods. Furthermore, the optimal proposal method can be customized by referring to the past optimization history. In this way, the optimal proposal method can be selected by analyzing the past optimization history.

[0062] The process flow of the first embodiment will be briefly described below.

[0063] Step 1: The collection unit collects data about the website. The collected data includes the website's content, design, and user behavior. Specifically, it collects the text, images, and layout of web pages, as well as the user's click history and stay time. The collection unit collects data using AI, for example, to extract important keywords from text data and analyze image data using image recognition technology. It can also collect user behavior data by analyzing the user's click history and stay time. Step 2: The determination unit determines the category and purpose of the website based on the data collected by the collection unit. A machine learning algorithm is used for the determination, for example, analyzing the collected text data to determine the category of the website, or analyzing image data to determine the purpose of the website. Furthermore, the determination unit can make determinations in multiple languages, and can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. Step 3: The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. The evaluation can use AI, for example, to evaluate the appropriateness of meta tags, page loading speed, mobile friendliness, etc. Step 4: The suggestion unit makes optimization suggestions for the website based on the data collected by the collection unit, the judgment results by the judgment unit, and the evaluation results by the evaluation unit. AI can be used for the suggestions, and optimization suggestions are made based on, for example, the results of A / B tests and user engagement rates.

[0064] (Example 2) The automatic classification and analysis system for websites according to an embodiment of the present invention is a system that collects data such as the content, design, and user behavior of a website, and automatically determines the category and purpose of the website using AI. This system has a function of evaluating the SEO status and accessibility of the website, analyzing user traffic and behavior, and making optimization suggestions for the website. For example, a collection unit collects data related to the website. This data includes the content, design, and user behavior of the website. For example, the text, images, layout, user click history, and stay time of the webpage are collected. Next, a determination unit determines the category and purpose of the website based on the collected data. A machine learning algorithm is used for the determination, and determination in multiple languages ​​is also possible. For example, categories such as news sites, shopping sites, and blogs are determined. Furthermore, an evaluation unit evaluates the SEO status and accessibility of the website based on the collected data. For example, appropriateness of meta tags, page loading speed, mobile friendliness, and the like are evaluated. Finally, a proposal unit makes optimization suggestions for the website based on the collected data, the determination results, and the evaluation results. The proposal takes into account the results of A / B tests and the engagement rate of users. For example, it suggests changes to the page layout, adding content, optimizing links, etc. This system allows website owners to take concrete actions to improve their site's performance.

[0065] The system for automatically classifying and analyzing websites according to the embodiment includes a collection unit, a determination unit, an evaluation unit, and a suggestion unit. The collection unit collects data related to websites. The data related to websites includes, for example, website content, design, user behavior, and the like, but is not limited to such examples. The collection unit collects, for example, text, images, layout, user click history, stay time, and the like of web pages. The collection unit can also collect data using AI. For example, the collection unit analyzes text data of web pages to extract important keywords. The collection unit can also analyze image data of web pages using image recognition technology. Furthermore, the collection unit can analyze user click history and stay time to collect user behavior data. The determination unit determines the category and purpose of the website based on the data collected by the collection unit. A machine learning algorithm is used for the determination. For example, the determination unit analyzes the collected text data to determine the category of the website. Furthermore, the determination unit can analyze the collected image data to determine the purpose of the website. Furthermore, the determination unit can make a determination in multiple languages. For example, the determination unit can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. AI can be used for the evaluation. For example, the evaluation unit evaluates the appropriateness of meta tags. The evaluation unit can also evaluate the page loading speed. Furthermore, the evaluation unit can evaluate mobile friendliness. The suggestion unit makes optimization suggestions for the website based on the data collected by the collection unit, the judgment result by the determination unit, and the evaluation result by the evaluation unit. AI can be used for the proposal. For example, the suggestion unit makes optimization suggestions based on the results of A / B testing. The suggestion unit can also make optimization suggestions based on the user engagement rate. As a result, the automatic website classification and analysis system according to the embodiment can provide specific actions to improve the performance of the website.

[0066] The collection unit collects data related to the website. The data related to the website includes, but is not limited to, for example, the contents, design, and user behavior of the website. The collection unit collects, for example, the text, images, layout, user click history, and stay time of the webpage. Specifically, the collection unit uses a web crawler to collect text data of the webpage, and uses natural language processing technology to extract important keywords and phrases. This allows the content of the website to be analyzed in detail. In addition, image recognition technology is used to analyze image data of the webpage, and the content and features of the image are extracted. For example, objects and text contained in the image are recognized, and the information is stored in a database. Furthermore, in order to analyze the user's click history and stay time, a tracking code using JavaScript is embedded in the webpage, and user behavior data is collected in real time. This allows a detailed understanding of which pages the user visited, which links he clicked, and how much time he spent on each page. The collection unit can centrally manage these data and cooperate with other systems and departments as necessary. For example, the collected data is stored in a cloud server, and can be accessed by the judgment unit and evaluation unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the collection unit to collect data efficiently and effectively, improving the performance of the entire system.

[0067] The determination unit determines the category and purpose of the website based on the data collected by the collection unit. A machine learning algorithm is used for the determination. Specifically, the determination unit analyzes the collected text data and determines the category of the website. For example, the text data is tokenized using natural language processing technology, and the main theme or topic of the website is identified by analyzing the frequency of occurrence and co-occurrence of each token. The determination unit can also analyze the collected image data and determine the purpose of the website. For example, the content of the image is classified using image recognition technology, and if a website contains many images related to a specific category, the website is determined to belong to that category. Furthermore, the determination unit can make determinations in multiple languages. For example, the determination unit can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. For this, a multilingual natural language processing model can be used to analyze text data in different languages ​​using the same criteria and make accurate determinations. The determination unit can record the category and purpose of the website in a database based on these analysis results and share them with other departments and systems. This allows the determination unit to quickly and accurately analyze the collected data and clearly grasp the characteristics of the website.

[0068] The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. AI can be used for the evaluation. Specifically, the evaluation unit evaluates the appropriateness of meta tags. For example, the evaluation unit analyzes the meta tags of a webpage and checks whether they contain appropriate keywords from the perspective of search engine optimization (SEO). The evaluation unit can also evaluate the page loading speed. For example, the evaluation unit measures the loading time of a webpage and identifies areas that need optimization. Furthermore, the evaluation unit can evaluate mobile friendliness. For example, the evaluation unit checks whether a webpage is displayed properly on a mobile device and evaluates the application status of responsive design. Based on these evaluation results, the evaluation unit can identify improvements to the website and propose specific improvement measures. For example, the evaluation unit proposes specific methods for improving the improvement of meta tags and the page loading speed. The evaluation unit also evaluates from the perspective of accessibility. For example, the evaluation unit checks the availability of screen reader support and keyboard operation and makes suggestions for improving accessibility. As a result, the evaluation unit can comprehensively evaluate the SEO status and accessibility of the website and provide specific improvement measures.

[0069] The suggestion unit makes optimization suggestions for the website based on the data collected by the collection unit, the judgment result by the judgment unit, and the evaluation result by the evaluation unit. AI can be used for the suggestions. Specifically, the suggestion unit makes optimization suggestions based on the results of A / B testing. For example, different designs and content versions are compared to identify the version with the highest user engagement rate. The suggestion unit can also make optimization suggestions based on the user's engagement rate. For example, the suggestion unit analyzes the user's click history and stay time to evaluate how specific content and design affect the user. Furthermore, the suggestion unit also makes optimization suggestions from the perspective of SEO. For example, the suggestion unit suggests appropriate keyword selection, improvement of meta tags, optimization of internal links, etc. The suggestion unit also makes suggestions for improving accessibility. For example, the suggestion unit suggests strengthening screen reader support, improving keyboard operation, and design for color blindness. The suggestion unit can provide these suggestions as a specific action plan and show specific methods for improving website performance. As a result, the suggestion unit can comprehensively support website optimization and achieve improved user experience and search engine ranking.

[0070] The collection unit can collect text data, layout data, and user operation data of a website as data related to the website. The collection unit, for example, collects text data of a website. The text data includes, but is not limited to, article contents, comments, reviews, etc. The collection unit, for example, collects layout data of a website. The layout data includes, but is not limited to, page configurations, design elements, etc. The collection unit, for example, collects user operation data. The operation data includes, but is not limited to, the number of clicks, scrolling actions, and stay times. This allows for more detailed analysis by collecting data on the content, design, and user behavior of a website. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input text data of a website into AI and extract important keywords.

[0071] The suggestion unit can make improvement suggestions for the website based on the data collected by the collection unit, the judgment result by the judgment unit, and the evaluation result by the evaluation unit. The suggestion unit makes improvement suggestions based on, for example, the data collected by the collection unit. For example, the suggestion unit analyzes text data of the website and proposes the addition or modification of content. The suggestion unit can also analyze layout data of the website and propose changes to the layout of the page. The suggestion unit can also analyze user operation data and propose link optimization. The suggestion unit makes improvement suggestions based on, for example, the judgment result by the judgment unit. For example, the suggestion unit proposes the addition of content suitable for the category based on the category of the website. The suggestion unit can also propose design changes suitable for the purpose based on the purpose of the website. The suggestion unit makes improvement suggestions based on, for example, the evaluation result by the evaluation unit. For example, the suggestion unit proposes meta tag corrections based on the evaluation result of the SEO state. The suggestion unit can also propose improvements to the page loading speed based on the evaluation result of accessibility. This makes it possible to make optimization suggestions for the website based on the collected data, the judgment result, and the evaluation result. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit may input the data collected by the collection unit to AI and generate optimization suggestions.

[0072] The determination unit can determine the category and purpose of the website using a machine learning algorithm. The determination unit, for example, determines the category of the website using a machine learning algorithm. For example, the determination unit analyzes the collected text data and determines the category, such as a news site, a shopping site, or a blog. The determination unit can also analyze the collected image data and determine the purpose of the website. For example, the determination unit uses image recognition technology to determine whether the website is intended to provide information or to promote sales. Furthermore, the determination unit can make determinations in multiple languages. For example, the determination unit can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. As a result, the use of the machine learning algorithm improves the accuracy of determining the category and purpose of the website. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the determination unit can input the collected text data to the generation AI and cause the generation AI to perform the determination of the category and purpose.

[0073] The suggestion unit can make optimization suggestions based on the results of the A / B test. For example, the suggestion unit can analyze the results of the A / B test and suggest changes to the layout of the page. The suggestion unit can also suggest adding or modifying content based on the results of the A / B test. Furthermore, the suggestion unit can also suggest link optimization based on the results of the A / B test. This makes it possible to make more effective optimization suggestions based on the results of the A / B test. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the results of the A / B test into AI to generate optimization suggestions.

[0074] The determination unit can determine the category and purpose of a website in multiple languages. The determination unit can determine the category and purpose of a website in languages ​​such as English, Japanese, and Chinese. For example, the determination unit analyzes English text data and determines categories such as news sites, shopping sites, and blogs. The determination unit can also analyze Japanese text data and determine the purpose of a website. Furthermore, the determination unit can analyze Chinese text data and determine the category and purpose of a website. This makes it possible to make determinations in multiple languages, and can also handle international websites. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input text data in multiple languages ​​to the AI ​​and cause the generation AI to perform category and purpose determination.

[0075] The suggestion unit can make optimization suggestions based on the user's engagement rate. For example, the suggestion unit analyzes engagement data such as the user's click rate, stay time, and number of shares, and proposes changes to the page layout. The suggestion unit can also propose adding or modifying content based on the user's engagement rate. Furthermore, the suggestion unit can also propose link optimization based on the user's engagement rate. This makes it possible to make more effective optimization suggestions based on the user's engagement rate. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's engagement data into AI to generate optimization suggestions.

[0076] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. In addition, when the user is relaxed, the collection unit can increase the collection frequency to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can collect only important data preferentially. This can reduce the burden on the user by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0077] The collection unit can analyze past data of the website and select the optimal data collection method. The collection unit, for example, analyzes past traffic data and concentrates data collection during peak hours. The collection unit can also focus on collecting data for specific pages or sections based on past user behavior data. Furthermore, the collection unit can analyze past errors and problems and select a method for improving the accuracy of data collection. In this way, the optimal data collection method can be selected by analyzing past data. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or may be performed without using AI. For example, the collection unit can input past traffic data into AI and select the optimal data collection method.

[0078] The collection unit can perform filtering based on the current traffic situation of the website and the behavioral patterns of the user when collecting data. For example, when the current traffic is high, the collection unit preferentially collects only important data. The collection unit can also analyze the behavioral patterns of the user and focus on collecting data related to specific behavior. Furthermore, the collection unit can collect detailed data during times of low traffic to distribute the load on the system. This allows important data to be preferentially collected by filtering the data based on the current traffic situation and the behavioral patterns of the user. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the current traffic data into AI and perform filtering.

[0079] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is feeling stressed, the collection unit preferentially collects only important data. Also, when the user is relaxed, the collection unit can adjust the priority to collect detailed data. Furthermore, when the user is in a hurry, the collection unit can prioritize data that can be collected quickly. In this way, important data can be preferentially collected by determining the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0080] The collection unit can preferentially collect highly relevant data in consideration of the geographical location information of the website when collecting data. For example, when there is a lot of access from a specific area, the collection unit preferentially collects data related to that area. In addition, the collection unit can analyze user behavior patterns for each area based on the geographical location information and collect highly relevant data. Furthermore, the collection unit can adjust the priority of data collection in consideration of the traffic situation for each area. As a result, highly relevant data can be preferentially collected by considering the geographical location information. A part or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the collection unit can input geographical location information into AI and preferentially collect highly relevant data.

[0081] The collection unit can analyze the social media activity of the website and collect related data when collecting data. For example, the collection unit can focus on collecting data for pages and content that are frequently mentioned on social media. The collection unit can also analyze social media trends and collect related data. Furthermore, the collection unit can determine the priority of data to be collected based on user responses on social media. This allows related data to be collected by analyzing social media activity. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without using AI. For example, the collection unit can input social media data into AI and collect related data.

[0082] The determination unit can estimate the user's emotion and adjust the category and purpose determination criteria based on the estimated user's emotion. For example, when the user is relaxed, the determination unit performs detailed categorization. Furthermore, when the user is in a hurry, the determination unit can perform simplified categorization. Furthermore, when the user is excited, the determination unit can perform visually stimulating categorization. This allows for more appropriate category and purpose determination by adjusting the determination criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0083] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between websites when making the determination. The determination unit, for example, analyzes link relationships between websites and determines highly related categories. The determination unit can also determine the purpose based on mutual links between websites. Furthermore, the determination unit can improve the accuracy of the category by taking into account the interrelationships between websites. As a result, the accuracy of the determination is improved by taking into account the interrelationships between websites. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input link data between websites into AI and make a determination taking into account the interrelationships.

[0084] The determination unit can make a determination by taking into consideration attribute information of the operator of the website at the time of determination. The determination unit can determine a category based on, for example, the industry or occupation of the operator of the website. The determination unit can also determine a purpose by taking into consideration the past activity history of the operator of the website. Furthermore, the determination unit can improve the accuracy of the determination based on the attribute information of the operator of the website. As a result, the accuracy of the determination is improved by taking into consideration the attribute information of the operator. A part or all of the above-mentioned processing in the determination unit can be performed, for example, using AI or can be performed without using AI. For example, the determination unit can input the attribute information of the operator into AI and determine a category and purpose.

[0085] The determination unit can estimate the user's emotion and adjust the display order of the determination results based on the estimated user's emotion. For example, when the user is relaxed, the determination unit displays in an order including detailed information. Also, when the user is in a hurry, the determination unit can display in an order that covers the main points. Furthermore, when the user is excited, the determination unit can display in a visually stimulating order. In this way, more appropriate information can be provided by adjusting the display order according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the determination unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the determination unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0086] The determination unit can make a determination taking into consideration the geographical distribution of websites when making a determination. For example, the determination unit gives priority to geographically close websites. The determination unit can also determine a category based on the geographical distribution. Furthermore, the determination unit can determine a purpose taking into consideration the geographical distribution. As a result, the accuracy of the determination is improved by taking the geographical distribution into consideration. A part or all of the above-mentioned processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input geographical distribution data into AI to determine a category and a purpose.

[0087] The determination unit can improve the accuracy of the determination by referring to related literature on the website at the time of determination. The determination unit, for example, determines the category based on the related literature. The determination unit can also determine the purpose by referring to the related literature. Furthermore, the determination unit can improve the accuracy of the determination by taking the related literature into consideration. As a result, the accuracy of the determination is improved by referring to the related literature. A part or all of the above-mentioned processing in the determination unit may be performed, for example, using AI, or may be performed without using AI. For example, the determination unit can input related literature data into AI to determine the category and purpose.

[0088] The evaluation unit can estimate the user's emotion and adjust the evaluation criteria of the SEO status and accessibility based on the estimated user's emotion. For example, the evaluation unit applies detailed evaluation criteria when the user is relaxed. Also, the evaluation unit can apply simplified evaluation criteria when the user is in a hurry. Furthermore, the evaluation unit can apply visually stimulating evaluation criteria when the user is excited. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0089] The evaluation unit can predict the current evaluation by referring to past evaluation data at the time of evaluation. The evaluation unit can predict the current SEO state based on, for example, past evaluation data. The evaluation unit can also predict the current accessibility by referring to the past evaluation data. Furthermore, the evaluation unit can analyze the past evaluation data and predict the current evaluation. As a result, the current evaluation can be predicted by referring to the past evaluation data. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI, or can be performed without using AI. For example, the evaluation unit can input the past evaluation data into AI and predict the current evaluation.

[0090] The evaluation unit can apply different evaluation methods to each website category during evaluation. For example, the evaluation unit can apply an evaluation method that emphasizes update frequency and reliability to a news site. The evaluation unit can also apply an evaluation method that emphasizes the richness of product information and user reviews to a shopping site. Furthermore, the evaluation unit can apply an evaluation method that emphasizes the quality of content and reader engagement to a blog. This allows for more appropriate evaluation by applying different evaluation methods to each category. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input evaluation data for each category into AI and apply different evaluation methods.

[0091] The evaluation unit can estimate the user's emotion and adjust the importance of the evaluation result based on the estimated user's emotion. For example, when the user is relaxed, the evaluation unit provides a detailed evaluation result. Furthermore, when the user is in a hurry, the evaluation unit can provide an evaluation result that covers the main points. Furthermore, when the user is excited, the evaluation unit can provide a visually stimulating evaluation result. This allows a more appropriate evaluation result to be provided by adjusting the importance of the evaluation result according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the evaluation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0092] The evaluation unit can analyze the change in the evaluation based on the submission time of the website at the time of evaluation. For example, if the submission time is old, the evaluation unit adjusts the evaluation criteria and performs the evaluation. Also, if the submission time is new, the evaluation unit can apply the latest evaluation criteria and perform the evaluation. Furthermore, the evaluation unit can analyze the change in the evaluation based on the submission time and provide the evaluation result. This enables more appropriate evaluation by analyzing the change in the evaluation based on the submission time. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the submission time data into AI and analyze the change in the evaluation.

[0093] The evaluation unit can analyze the evaluation by referring to relevant market data of the website at the time of evaluation. The evaluation unit can, for example, evaluate the SEO status based on the relevant market data. The evaluation unit can also evaluate the accessibility by referring to the relevant market data. Furthermore, the evaluation unit can provide an evaluation result by taking into account the relevant market data. Thereby, by referring to the relevant market data, the accuracy of the evaluation is improved. Some or all of the above-mentioned processing in the evaluation unit can be performed, for example, using AI or can be performed without using AI. For example, the evaluation unit can input the relevant market data into AI and analyze the evaluation.

[0094] The suggestion unit can estimate the user's emotion and adjust the optimization suggestion method based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit makes a detailed optimization suggestion. Also, when the user is in a hurry, the suggestion unit can make a simplified optimization suggestion. Furthermore, when the user is excited, the suggestion unit can make a visually stimulating optimization suggestion. This allows for more appropriate suggestions by adjusting the optimization suggestion method according to the user's emotion. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0095] At the time of proposal, the suggestion unit can select an optimal proposal method by analyzing the past optimization history of the website. The suggestion unit selects an effective proposal method based on, for example, the past optimization history. The suggestion unit can also analyze the past optimization history and avoid failed proposal methods. Furthermore, the suggestion unit can customize an optimal proposal method by referring to the past optimization history. In this way, the optimal proposal method can be selected by analyzing the past optimization history. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI, or may be performed without using AI. For example, the suggestion unit can input past optimization history data into AI to select an optimal proposal method.

[0096] The suggestion unit can customize the optimization suggestion means based on the current traffic status of the website at the time of suggestion. For example, when the current traffic is high, the suggestion unit makes a suggestion to distribute the traffic. Also, when the current traffic is low, the suggestion unit can make a suggestion to increase the traffic. Furthermore, the suggestion unit can customize the optimal suggestion means based on the current traffic status. This enables more effective suggestions by customizing the suggestion means based on the current traffic status. Part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input current traffic data into AI to customize the optimal suggestion means.

[0097] The suggestion unit can estimate the user's emotion and determine the priority of optimization suggestions based on the estimated user's emotion. For example, when the user is relaxed, the suggestion unit prioritizes detailed optimization suggestions. Also, when the user is in a hurry, the suggestion unit can prioritize simplified optimization suggestions. Furthermore, when the user is excited, the suggestion unit can prioritize visually stimulating optimization suggestions. This enables more appropriate suggestions by determining the priority of suggestions according to the user's emotion. The estimation of emotions is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's facial expression data to the generation AI and cause the generation AI to execute emotion estimation.

[0098] The suggestion unit can select the optimal suggestion method by considering the geographical location information of the website when making a suggestion. For example, when there is a lot of access from a specific region, the suggestion unit makes a suggestion related to that region. In addition, the suggestion unit can analyze user behavior patterns for each region based on the geographical location information and select the optimal suggestion method. Furthermore, the suggestion unit can select the optimal suggestion method by considering the traffic situation for each region. In this way, the optimal suggestion method can be selected by considering the geographical location information. A part or all of the above-mentioned processing in the suggestion unit may be performed, for example, using AI or may be performed without using AI. For example, the suggestion unit can input geographical location information into AI and select the optimal suggestion method.

[0099] At the time of making a proposal, the suggestion unit can analyze the social media activity of the website and propose a means of optimization proposal. For example, the suggestion unit makes an optimization proposal for a page or content that is frequently mentioned on social media. The suggestion unit can also analyze social media trends and make related optimization proposals. Furthermore, the suggestion unit can propose a means of optimization proposal based on user responses on social media. This enables more effective optimization proposals by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without using AI. For example, the suggestion unit can input social media data into AI and propose a means of optimization proposal.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, when the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, when the user is relaxed, the frequency of collection can be increased to collect detailed data. Furthermore, when the user is in a hurry, only important data can be collected preferentially. In this way, the burden on the user can be reduced by adjusting the timing of data collection according to the user's emotions.

[0102] The determination unit can estimate the user's emotion and adjust the criteria for category and purpose based on the estimated emotion. For example, when the user is relaxed, detailed categorization can be performed. When the user is in a hurry, simplified categorization can be performed. Furthermore, when the user is excited, visually stimulating categorization can be performed. This allows more appropriate category and purpose determination by adjusting the criteria according to the user's emotion.

[0103] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria for SEO status and accessibility based on the estimated emotions. For example, if the user is relaxed, detailed evaluation criteria can be applied. If the user is in a hurry, simplified evaluation criteria can be applied. Furthermore, if the user is excited, visually stimulating evaluation criteria can be applied. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions.

[0104] The suggestion unit can estimate the user's emotion and adjust the optimization suggestion method based on the estimated emotion. For example, when the user is relaxed, a detailed optimization suggestion can be made. When the user is in a hurry, a simplified optimization suggestion can be made. Furthermore, when the user is excited, a visually stimulating optimization suggestion can be made. In this way, by adjusting the optimization suggestion method according to the user's emotion, more appropriate suggestions can be made.

[0105] The suggestion unit can estimate the user's emotion and determine the priority order of optimization suggestions based on the estimated emotion. For example, if the user is relaxed, detailed optimization suggestions can be prioritized. Also, if the user is in a hurry, simplified optimization suggestions can be prioritized. Furthermore, if the user is excited, visually stimulating optimization suggestions can be prioritized. In this way, by determining the priority order of suggestions according to the user's emotion, more appropriate suggestions can be made.

[0106] The collection unit can analyze past data of the website and select the optimal data collection method. For example, past traffic data can be analyzed and data collection can be concentrated during peak hours. Also, based on past user behavior data, data can be collected with a focus on specific pages or sections. Furthermore, past errors and problems can be analyzed and a method for improving the accuracy of data collection can be selected. Thus, by analyzing past data, the optimal data collection method can be selected.

[0107] The collection unit can filter data based on the current traffic status of the website and the user's behavioral patterns when collecting data. For example, when the current traffic is high, only important data is collected preferentially. In addition, the user's behavioral patterns can be analyzed and data related to specific actions can be collected with a focus. Furthermore, detailed data can be collected during periods of low traffic, distributing the load on the system. This makes it possible to prioritize the collection of important data by filtering data based on the current traffic status and the user's behavioral patterns.

[0108] The determination unit can improve the accuracy of the determination by taking into account the interrelationships between websites when making a determination. For example, the link relationships between websites are analyzed to determine highly related categories. Also, the purpose can be determined based on mutual links between websites. Furthermore, the accuracy of the category can be improved by taking into account the interrelationships between websites. In this way, the accuracy of the determination is improved by taking into account the interrelationships between websites.

[0109] During evaluation, the evaluation unit can predict a current evaluation by referring to past evaluation data. For example, the current SEO state can be predicted based on past evaluation data. The evaluation unit can also predict current accessibility by referring to past evaluation data. Furthermore, the evaluation unit can analyze past evaluation data and predict a current evaluation. In this way, the current evaluation can be predicted by referring to past evaluation data.

[0110] When making a proposal, the proposal unit can select an optimal proposal method by analyzing the past optimization history of the website. For example, an effective proposal method can be selected based on the past optimization history. Also, the past optimization history can be analyzed to avoid unsuccessful proposal methods. Furthermore, the optimal proposal method can be customized by referring to the past optimization history. In this way, the optimal proposal method can be selected by analyzing the past optimization history.

[0111] The process flow of the second embodiment will be briefly described below.

[0112] Step 1: The collection unit collects data about the website. The collected data includes the website's content, design, and user behavior. Specifically, it collects the text, images, and layout of web pages, as well as the user's click history and stay time. The collection unit collects data using AI, for example, to extract important keywords from text data and analyze image data using image recognition technology. It can also collect user behavior data by analyzing the user's click history and stay time. Step 2: The determination unit determines the category and purpose of the website based on the data collected by the collection unit. A machine learning algorithm is used for the determination, for example, analyzing the collected text data to determine the category of the website, or analyzing image data to determine the purpose of the website. Furthermore, the determination unit can make determinations in multiple languages, and can determine the category and purpose of the website in languages ​​such as English, Japanese, and Chinese. Step 3: The evaluation unit evaluates the SEO status and accessibility of the website based on the data collected by the collection unit. The evaluation can use AI, for example, to evaluate the appropriateness of meta tags, page loading speed, mobile friendliness, etc. Step 4: The suggestion unit makes optimization suggestions for the website based on the data collected by the collection unit, the judgment results by the judgment unit, and the evaluation results by the evaluation unit. AI can be used for the suggestions, and optimization suggestions are made based on, for example, the results of A / B tests and user engagement rates.

[0113] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0114] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0115] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0116] For example, the collection unit can collect data on the website using the camera 42 and microphone 38B of the smart device 14. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects text, images, layouts, user click history, stay time, and the like of web pages. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the category and purpose of the website based on the collected data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the SEO status and accessibility of the website. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimization suggestions for the website based on the collected data, the judgment results, and the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0117] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0118] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0124] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0127] In the smart glasses 214, the 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 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0129] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed 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 required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0132] For example, the collection unit can collect data on the website using the camera 42 and microphone 238 of the smart glasses 214. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects text, images, layouts, user click history, stay time, etc. of web pages. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the category and purpose of the website based on the collected data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the SEO status and accessibility of the website. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimization suggestions for the website based on the collected data, the judgment results, and the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0133] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0136] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0137] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0139] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0140] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0141] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0142] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0143] In the headset type terminal 314, the 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 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0144] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0146] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0148] For example, the collection unit can collect data on the website using the camera 42 and microphone 238 of the headset terminal 314. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects text, images, layouts, user click history, stay time, and the like of web pages. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the category and purpose of the website based on the collected data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the SEO status and accessibility of the website. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimization suggestions for the website based on the collected data, the judgment results, and the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0149] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0150] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] 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. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. In addition, the microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0153] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0155] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0156] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0157] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0158] The processor 28 reads the specific processing program 56 from the storage 32, and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0159] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0160] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0161] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0163] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed 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 required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0165] For example, the collection unit can collect data on the website using the camera 42 and microphone 238 of the robot 414. The collection unit is also realized by the specific processing unit 290 of the data processing device 12, and collects text, images, layouts, user click history, stay time, and the like of web pages. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the category and purpose of the website based on the collected data. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and evaluates the SEO status and accessibility of the website. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and makes optimization suggestions for the website based on the collected data, the judgment results, and the evaluation results. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0166] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0167] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0168] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0169] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out on emotion map 400 you go, the more visible the emotions become (the more they are expressed in behavior).

[0170] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0171] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this 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 positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0174] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0175] In addition, 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 upon request from the data processing device 12.

[0176] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0177] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0178] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0179] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0180] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0181] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0182] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0183] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0184] (Appendix 1) A collection unit that collects data regarding the website; a determination unit that determines a category and purpose of the website based on the data collected by the collection unit; an evaluation unit that evaluates the SEO status and accessibility of the website based on the data collected by the collection unit; A website suggestion unit based on the data collected by the collection unit. A system characterized by: (Appendix 2) The collecting unit includes: Collecting text data of the website, layout data of the website, and user operation data on the website as data related to the website. 2. The system of claim 1, (Appendix 3) The suggestion unit, Proposing improvements to the website based on the data collected by the collection unit, the judgment result by the judgment unit, and the evaluation result by the evaluation unit. 2. The system of claim 1, (Appendix 4) The determination unit is Using machine learning algorithms to determine the category and purpose of the website 2. The system of claim 1, (Appendix 5) The suggestion unit, Make optimization suggestions based on A / B test results 2. The system of claim 1, (Appendix 6) The determination unit is Determine the category and purpose of said website in multiple languages 2. The system of claim 1, (Appendix 7) The suggestion unit, Make optimization suggestions based on user engagement rates 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze the website's historical data and choose the best data collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: When collecting data, we filter it based on the current traffic situation of the website and user behavior patterns. 2. The system of claim 1, (Appendix 11) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, take into account the geographic location of the website to prioritize collecting the most relevant data 2. The system of claim 1, (Appendix 13) The collecting unit includes: At the time of data collection, analyze the website's social media activity and collect relevant data 2. The system of claim 1, (Appendix 14) The determination unit is Inferring a user's emotion and adjusting category and objective criteria based on the estimated user's emotion 2. The system of claim 1, (Appendix 15) The determination unit is When assessing a website, we take into account its interrelationships to improve the accuracy of the assessment. 2. The system of claim 1, (Appendix 16) The determination unit is When making a judgment, the website operator's attribute information is taken into consideration. 2. The system of claim 1, (Appendix 17) The determination unit is A user's emotion is estimated, and a display order of the judgment results is adjusted based on the estimated user's emotion. 2. The system of claim 1, (Appendix 18) The determination unit is When making a decision, the geographical distribution of the website is taken into account. 2. The system of claim 1, (Appendix 19) The determination unit is When making a judgment, refer to related literature on the website to improve the accuracy of the judgment. 2. The system of claim 1, (Appendix 20) The evaluation unit is Infer user sentiment and adjust SEO status and accessibility evaluation criteria based on the inferred user sentiment 2. The system of claim 1, (Appendix 21) The evaluation unit is During evaluation, past evaluation data is referenced to predict the current evaluation 2. The system of claim 1, (Appendix 22) The evaluation unit is During evaluation, different evaluation methodologies are applied to different categories of websites. 2. The system of claim 1, (Appendix 23) The evaluation unit is The user's emotions are estimated, and the importance of the evaluation results is adjusted based on the estimated user's emotions. 2. The system of claim 1, (Appendix 24) The evaluation unit is During evaluation, analyze changes in ratings based on when a website was submitted 2. The system of claim 1, (Appendix 25) The evaluation unit is During the evaluation, we will refer to the relevant market data on the website to analyze the evaluation. 2. The system of claim 1, (Appendix 26) The suggestion unit, Infer user emotions and adjust the optimization suggestions based on the inferred user emotions 2. The system of claim 1, (Appendix 27) The suggestion unit, When making a proposal, we analyze the website's past optimization history to select the most appropriate proposal method. 2. The system of claim 1, (Appendix 28) The suggestion unit, When making a recommendation, customize the optimization suggestions based on the current traffic situation on your website 2. The system of claim 1, (Appendix 29) The suggestion unit, Infer user sentiment and prioritize optimization suggestions based on the inferred user sentiment 2. The system of claim 1, (Appendix 30) The suggestion unit, When making suggestions, the geographic location of the website is taken into account to select the optimal suggestion method. 2. The system of claim 1, (Appendix 31) The suggestion unit, When making a proposal, we analyze the website's social media activity to provide optimization suggestions 2. The system of claim 1, [Explanation of symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data regarding the website; a determination unit that determines a category and purpose of the website based on the data collected by the collection unit; an evaluation unit that evaluates the SEO status and accessibility of the website based on the data collected by the collection unit; A website suggestion unit based on the data collected by the collection unit. A system characterized by:

2. The collecting unit includes: Collecting text data of the website, layout data of the website, and user operation data on the website as data related to the website.

2. The system of claim 1 .

3. The suggestion unit, Proposing improvements to the website based on the data collected by the collection unit, the judgment result by the judgment unit, and the evaluation result by the evaluation unit.

2. The system of claim 1 .

4. The determination unit is Using machine learning algorithms to determine the category and purpose of the website 2. The system of claim 1 .

5. The suggestion unit, Make optimization suggestions based on A / B test results 2. The system of claim 1 .

6. The determination unit is Determine the category and purpose of said website in multiple languages 2. The system of claim 1 .

7. The suggestion unit, Make optimization suggestions based on user engagement rates 2. The system of claim 1 .

8. The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1 .

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