system

Abstract

The system according to this embodiment aims to efficiently analyze the SEO status of a web page and propose appropriate improvement measures. [Solution] The system according to this embodiment comprises an analysis unit, a proposal unit, and a reporting unit. The analysis unit analyzes the client company's web pages. The proposal unit proposes improvement measures based on the analysis results obtained by the analysis unit. The reporting unit reports on the effectiveness of the improvement measures proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently analyze the SEO countermeasure situation of a web page and propose appropriate improvement measures.

[0005] The system according to the embodiment aims to efficiently analyze the SEO countermeasure situation of a web page and propose appropriate improvement measures.

Means for Solving the Problems

[0006] The system according to the embodiment includes an analysis unit, a proposal unit, and a report unit. The analysis unit analyzes the web page of the client company. The proposal unit proposes improvement measures based on the analysis result obtained by the analysis unit. The report unit reports the effect of the improvement measures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently analyze the SEO status of a web page and propose appropriate improvement measures. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The SEO improvement support system according to an embodiment of the present invention is a system that supports SEO improvement by using AI to analyze the SEO status of a company's website and proposing improvement measures and reporting on the effects of those improvements. The SEO improvement support system uses AI to analyze the client company's web pages. In this process, the AI ​​proposes challenges and improvement measures from the perspective of recent search engine trends and successful case studies of other companies, in addition to general SEO knowledge. Next, the AI ​​proposes improvement measures based on the analysis results. Specifically, it proposes measures to aim for higher rankings in search engines. Furthermore, after the proposed improvement measures are implemented, the AI ​​reports on their effects. Specifically, it reports on changes in search engine rankings and increases in the number of accesses to the web pages after the improvement measures are implemented. This allows the client company to confirm the effectiveness of its SEO measures and make further improvements. This system is particularly targeted at small and medium-sized enterprises and is useful for companies that have few personnel with SEO improvement skills. By having the AI ​​act as an SEO consultant to propose challenges and improvement measures, the cost of hiring new personnel can be reduced, and SEO measures can be implemented efficiently. For example, if a small or medium-sized enterprise (SME) wants to improve the SEO of its e-commerce site, this system allows the AI ​​to analyze the site's current state and propose specific improvement measures. After implementing the proposed measures, the AI ​​reports on their effectiveness, allowing the SEO efforts to be verified. In this way, by utilizing AI, SEO improvements can be efficiently implemented even without specialized knowledge of SEO, increasing online traffic and boosting sales and lead generation. Thus, the SEO improvement support system can efficiently analyze, propose, and report on the SEO strategies for client companies' web pages.

[0029] The SEO improvement support system according to this embodiment comprises an analysis unit, a proposal unit, and a reporting unit. The analysis unit analyzes the client company's web pages. The client company's web pages include, but are not limited to, product pages, blog posts, and landing pages. The analysis unit analyzes, for example, the metadata, keywords, internal link structure, and page loading speed of the client company's web pages. The analysis unit can also analyze web pages considering recent search engine trends and successful case studies of other companies. For example, the analysis unit considers the latest changes in search engine algorithms and identifies optimization points for web pages. The proposal unit proposes improvement measures based on the analysis results. The proposal unit proposes measures such as keyword optimization, meta tag correction, strengthening internal links, and improving page loading speed. For example, the proposal unit proposes increasing the frequency of use of specific keywords or optimizing meta descriptions. The proposal unit can also analyze the SEO status of the client company's web pages in detail and propose specific improvement measures. For example, the proposal unit analyzes the SEO measures of competitors and proposes improvements to the client company's web pages based on that analysis. The reporting department reports on the effectiveness of the proposed improvement measures. For example, the reporting department reports on changes in search engine rankings and increases in the number of website visits after the implementation of the improvement measures. For example, the reporting department reports on an increase in search engine rankings and an increase in the number of website visitors. The reporting department can also periodically report on the effectiveness of the proposed improvement measures. For example, the reporting department can create and provide monthly or quarterly reports to the client company. In this way, the SEO improvement support system according to the embodiment can efficiently analyze, propose, and report on SEO measures for the client company's website.

[0030] The analytics department conducts a detailed analysis of client companies' web pages. These pages include, but are not limited to, product pages, blog posts, and landing pages. First, the analytics department analyzes the web page metadata to ensure that elements such as title tags, meta descriptions, and meta keywords are properly configured. Next, they investigate keyword usage to assess whether target keywords are appropriately placed and whether there is excessive keyword usage. Internal link structure analysis verifies that links between pages are properly configured and that the anchor text of the links is relevant. Regarding page loading speed, they evaluate image optimization, caching, and server response time to identify areas for improvement. The analytics department can also analyze web pages considering recent search engine trends and successful case studies from other companies. For example, they consider the latest changes in search engine algorithms to identify optimization points for web pages. This includes evaluating mobile-friendly design and verifying security measures (such as HTTPS implementation). Furthermore, the analytics department considers the SEO requirements specific to the client company's industry and identifies strengths and weaknesses by comparing the web pages with those of competitors. This allows the analysis department to gain a detailed understanding of the current state of the client company's web pages and clearly identify specific areas for improvement.

[0031] The proposal team will propose specific improvement measures based on the analysis results. First, they will suggest keyword optimization, recommending a review of the frequency and placement of target keywords. For example, they might suggest increasing the frequency of certain keywords or optimizing meta descriptions. Regarding meta tag modifications, they will verify that title tags and meta descriptions provide appropriate information to search engines and make corrections as needed. For strengthening internal links, they will suggest increasing links between related pages to allow users to navigate the site smoothly. For improving page loading speed, they will propose specific measures such as image compression, caching, and reducing server response times. Furthermore, the proposal team can conduct a detailed analysis of the client company's web page SEO status and propose specific improvement measures. For example, they might analyze competitor SEO strategies and propose improvements to the client company's web page based on that analysis. This includes analyzing the keywords and link strategies used by competitors. The proposal team will also provide specific procedures and tool usage instructions for implementing these measures, supporting the client company in easily executing them. This allows the proposal team to effectively improve the client company's web page SEO and enhance its search engine rankings.

[0032] The reporting department will provide detailed reports on the effectiveness of proposed improvement measures. First, the reporting department will track changes in search engine rankings after the implementation of the improvement measures and report them with specific numerical data. For example, it will show the improvement in search rankings for a particular keyword. It will also provide detailed reports on increases in web page traffic, offering metrics such as visitor numbers, time spent on site, and page views. This allows client companies to concretely understand the effectiveness of the improvement measures. Furthermore, the reporting department can provide regular reports on the effectiveness of the proposed improvement measures. For example, monthly and quarterly reports will be created and provided to client companies. These reports will include the progress of the improvement measures, newly discovered issues, and recommended next steps. Through these reports, the reporting department provides client companies with information to continuously monitor the progress of their SEO efforts and adjust their strategies as needed. The reporting department also creates visual reports using graphs and charts to visually convey the effectiveness of the improvement measures. This makes it easier for client companies to intuitively understand the data and make quick decisions. The reporting department maintains close communication with client companies and continuously improves the report content by incorporating their feedback. This allows the reporting department to maximize the effectiveness of client companies' SEO measures and support their sustainable growth.

[0033] The analysis department can analyze web pages based on recent search engine trends and successful case studies from other companies. For example, the analysis department can identify optimization points for web pages by considering the latest changes in search engine algorithms. The analysis department can also refer to successful case studies from other companies and apply similar measures to the client company's web pages. For example, the analysis department can refer to successful keyword strategies and link-building methods of competitors. This enables analysis that takes into account the latest trends and successful case studies. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input recent search engine trend data into a generating AI and have the generating AI identify optimization points based on the trends.

[0034] The proposal team can suggest improvement measures such as keyword optimization, meta tag correction, and strengthening internal links. For example, the proposal team might suggest increasing the frequency of use of specific keywords or optimizing meta descriptions. The proposal team can also review the placement of internal links and strengthen them. For example, the proposal team might suggest increasing links to important pages or optimizing the anchor text of links. This allows for the proposal of specific SEO improvement measures. Some or all of the above processes performed by the proposal team may be carried out using AI, or not. For example, the proposal team could input keyword optimization suggestions into a generation AI and have the generation AI select the most suitable keywords.

[0035] The reporting unit can report on changes in search engine rankings and increases in website traffic after implementing improvement measures. For example, the reporting unit may report an increase in search engine rankings or an increase in the number of website visitors. The reporting unit can also show the effects of improvement measures with specific numerical data. For example, the reporting unit can display changes in search engine rankings in a graph and increases in traffic in a table. This allows for concrete confirmation of the effects of improvement measures. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input search engine ranking data into a generating AI and analyze the changes in rankings.

[0036] The proposal department can conduct a detailed analysis of the SEO status of a client company's web page and propose specific improvement measures. For example, the proposal department can analyze the SEO strategies of competitors and propose improvements to the client company's web page based on that analysis. The proposal department can also conduct a detailed analysis of the internal link structure and metadata optimization points of the client company's web page. For example, the proposal department can propose revising the placement of internal links to strengthen them, or optimizing meta descriptions. This allows the proposal of specific improvement measures based on detailed analysis. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input competitor SEO data into a generating AI and have the generating AI propose the most suitable improvement measures.

[0037] The reporting department can periodically report on the effectiveness of proposed improvement measures. For example, the reporting department can create and provide monthly or quarterly reports to client companies. The reporting department can also show the effectiveness of improvement measures with specific numerical data. For example, the reporting department can display changes in search engine rankings in a graph and increase access numbers in a table. This allows for regular verification of the effectiveness of improvement measures. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can have a generation AI perform the periodic report generation.

[0038] The analysis department can select the optimal analysis method by referring to the client company's past SEO strategy history. For example, the analysis department can prioritize analyzing highly effective measures based on past SEO strategy history. The analysis department can also refer to past failures and conduct analyses to avoid making the same mistakes. For example, the analysis department can analyze past SEO trends and select the optimal method by comparing it with current trends. This allows for the selection of the optimal analysis method based on past history. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input past SEO strategy history data into a generating AI and have the generating AI select the optimal analysis method.

[0039] The analysis department can perform analyses while considering the industry-specific SEO requirements of client companies. For example, the analysis department can prioritize the analysis of industry-specific keywords. The analysis department can also refer to and compare the SEO strategies of competitors in the same industry. For example, the analysis department can conduct analyses to propose optimal SEO strategies, taking into account the latest industry trends. This enables analyses that consider industry-specific requirements. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input industry-specific SEO requirement data into a generating AI and have the generating AI perform an analysis based on those requirements.

[0040] The analysis department can conduct analyses that take into account the geographical market characteristics of client companies. For example, the analysis department can analyze region-specific keywords based on geographical market characteristics. The analysis department can also refer to and compare the SEO strategies of regional competitors. For example, the analysis department can conduct analyses to propose optimal SEO strategies by considering regional consumer behavior. This makes it possible to conduct analyses that take geographical market characteristics into account. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input geographical market characteristic data into a generating AI and have the generating AI perform an analysis based on those characteristics.

[0041] The analysis department can improve the accuracy of its analysis by referring to the SEO strategies of the client company's competitors. For example, the analysis department can refer to the success stories of competitors and analyze similar measures. The analysis department can also refer to the failure stories of competitors and conduct analyses to avoid making the same mistakes. For example, the analysis department can analyze the latest SEO trends of competitors and select the optimal method. This improves the accuracy of the analysis by referring to the strategies of competitors. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input competitor SEO strategy data into a generating AI and have the generating AI perform a highly accurate analysis.

[0042] The proposal department can determine the priority of proposals based on the client company's business objectives. For example, the proposal department can prioritize proposing the most effective measures based on the client company's sales targets. The proposal department can also prioritize proposing measures aimed at improving the client company's brand awareness. For example, the proposal department can prioritize proposing measures aimed at promoting the client company's new products. This allows for proposals to be made in a priority order based on business objectives. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the client company's business objective data into a generating AI and have the generating AI perform the determination of priorities based on those objectives.

[0043] The proposal department can make proposals that take into account the industry-specific SEO requirements of the client company. For example, the proposal department can make proposals that optimize industry-specific keywords. The proposal department can also make proposals that refer to the success stories of competitors in the industry. For example, the proposal department can make proposals that take into account the latest industry trends. This makes it possible to make proposals that take into account industry-specific requirements. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input industry-specific SEO requirement data into a generating AI and have the generating AI execute proposals based on those requirements.

[0044] The proposal department can make proposals that take into account the geographical market characteristics of the client company. For example, the proposal department can make proposals that optimize region-specific keywords based on geographical market characteristics. The proposal department can also make proposals that refer to the success stories of local competitors. For example, the proposal department can make proposals that take into account local consumer behavior. This makes it possible to make proposals that take geographical market characteristics into account. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input geographical market characteristic data into a generating AI and have the generating AI execute proposals based on those characteristics.

[0045] The proposal department can improve the accuracy of its proposals by referring to the success stories of the client company's competitors. For example, the proposal department can refer to the success stories of competitors and propose similar measures. Alternatively, the proposal department can refer to the failure stories of competitors and make suggestions to avoid the same mistakes. For example, the proposal department can analyze the latest SEO trends of competitors and propose the optimal approach. This improves the accuracy of proposals by referring to the success stories of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on competitor success stories into a generating AI and have the generating AI produce highly accurate proposals.

[0046] The reporting unit can optimize the report content by referring to the client company's past SEO strategy history. For example, the reporting unit can highlight the results of highly effective measures based on past SEO strategy history. The reporting unit can also refer to past failures and clearly identify areas for improvement. For example, the reporting unit can analyze past SEO trends and optimize the report by comparing it with current trends. This allows for the creation of an optimal report based on past history. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input past SEO strategy history data into a generating AI and have the generating AI create an optimal report.

[0047] The reporting department can create reports that take into account the industry-specific SEO requirements of client companies. For example, the reporting department can reflect the effectiveness of industry-specific keywords in its reports. It can also refer to and compare the SEO strategies of competitors in the industry. For example, the reporting department can optimize reports by considering the latest industry trends. This makes it possible to create reports that take industry-specific requirements into account. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input industry-specific SEO requirement data into a generating AI and have the generating AI execute a report based on those requirements.

[0048] The reporting department can create reports that take into account the geographical market characteristics of client companies. For example, the reporting department can reflect the effectiveness of region-specific keywords in the report based on geographical market characteristics. The reporting department can also refer to and perform comparative analysis of the SEO strategies of regional competitors. For example, the reporting department can optimize the report by considering regional consumer behavior. This makes it possible to create reports that take geographical market characteristics into account. Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input geographical market characteristic data into a generating AI and have the generating AI execute a report based on those characteristics.

[0049] The reporting department can improve the accuracy of its reports by referring to the SEO results of the client company's competitors. For example, the reporting department can refer to the success stories of competitors and reflect the results of similar measures in the report. The reporting department can also refer to the failure stories of competitors and clarify areas for improvement. For example, the reporting department can analyze the latest SEO trends of competitors and reflect the optimal methods in the report. This improves the accuracy of the report by referring to the results of competitors' strategies. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input competitor SEO result data into a generating AI and have the generating AI produce a highly accurate report.

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

[0051] The SEO improvement support system can further prioritize proposals based on the client company's business objectives. For example, the proposal department can prioritize proposing the most effective measures based on the client company's sales targets. It can also prioritize proposing measures aimed at improving the client company's brand awareness. Furthermore, it can prioritize proposing measures aimed at promoting the client company's new products. This allows for proposals to be made in a priority order based on business objectives. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the client company's business objective data into a generating AI and have the generating AI perform the determination of priorities based on those objectives.

[0052] The SEO improvement support system can further select the optimal analysis method by referring to the client company's past SEO strategy history. For example, the analysis department can prioritize analyzing highly effective measures based on past SEO strategy history. It can also refer to past failure cases and conduct analyses to avoid making the same mistakes. Furthermore, it can analyze past SEO trends and select the optimal method by comparing them with current trends. This allows for the selection of the optimal analysis method based on past history. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input past SEO strategy history data into a generating AI and have the generating AI select the optimal analysis method.

[0053] The SEO improvement support system can further analyze the client company's geographical market characteristics. For example, the analysis department can prioritize region-specific keywords based on geographical market characteristics. It can also refer to and compare the SEO strategies of local competitors. Furthermore, it can conduct analysis to propose optimal SEO strategies by considering local consumer behavior. This enables analysis that takes geographical market characteristics into account. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input geographical market characteristic data into a generating AI and have the generating AI perform analysis based on those characteristics.

[0054] The SEO improvement support system can further improve the accuracy of its analysis by referencing the SEO strategies of the client company's competitors. For example, the analysis department can refer to the success stories of competitors and analyze similar measures. It can also refer to the failure stories of competitors and conduct analyses to avoid making the same mistakes. Furthermore, it can analyze the latest SEO trends of competitors and select the optimal method. In this way, the accuracy of the analysis is improved by referring to the strategies of competitors. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input competitor SEO strategy data into a generating AI and have the generating AI perform a highly accurate analysis.

[0055] The SEO improvement support system can further consider the industry-specific SEO requirements of client companies when making proposals. For example, the proposal department can propose optimizing industry-specific keywords. It can also make proposals that refer to the success stories of competitors in the same industry. Furthermore, it can make proposals that take into account the latest industry trends. This makes it possible to make proposals that take industry-specific requirements into account. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input industry-specific SEO requirement data into a generating AI and have the generating AI execute proposals based on those requirements.

[0056] The SEO improvement support system can further improve the accuracy of its proposals by referencing the success stories of the client company's competitors. For example, the proposal department can refer to the success stories of competitors and propose similar measures. It can also refer to the failure stories of competitors and make suggestions to avoid the same mistakes. Furthermore, it can analyze the latest SEO trends of competitors and propose the optimal method. In this way, the accuracy of the proposals is improved by referring to the success stories of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the success stories of competitors into a generating AI and have the generating AI execute highly accurate proposals.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The analysis department analyzes the client company's web pages. Specifically, they analyze metadata, keywords, internal link structure, and page loading speed for product pages, blog posts, landing pages, etc. They also consider recent search engine trends and successful case studies from other companies, and identify optimization points for web pages based on the latest changes in search engine algorithms. Step 2: The proposal department proposes improvement measures based on the analysis results obtained by the analysis department. Specifically, they propose measures such as keyword optimization, meta tag correction, strengthening internal links, and improving page loading speed. They also analyze the SEO strategies of competitors and propose improvements to the client company's web page based on that analysis. Step 3: The reporting department reports on the effectiveness of the proposed improvement measures. Specifically, they report on changes in search engine rankings and increases in web page traffic after the implementation of the improvement measures. They also regularly report on the effectiveness of the proposed improvement measures and provide monthly and quarterly reports to the client company.

[0059] (Example of form 2) The SEO improvement support system according to an embodiment of the present invention is a system that supports SEO improvement by using AI to analyze the SEO status of a company's website and proposing improvement measures and reporting on the effects of those improvements. The SEO improvement support system uses AI to analyze the client company's web pages. In this process, the AI ​​proposes challenges and improvement measures from the perspective of recent search engine trends and successful case studies of other companies, in addition to general SEO knowledge. Next, the AI ​​proposes improvement measures based on the analysis results. Specifically, it proposes measures to aim for higher rankings in search engines. Furthermore, after the proposed improvement measures are implemented, the AI ​​reports on their effects. Specifically, it reports on changes in search engine rankings and increases in the number of accesses to the web pages after the improvement measures are implemented. This allows the client company to confirm the effectiveness of its SEO measures and make further improvements. This system is particularly targeted at small and medium-sized enterprises and is useful for companies that have few personnel with SEO improvement skills. By having the AI ​​act as an SEO consultant to propose challenges and improvement measures, the cost of hiring new personnel can be reduced, and SEO measures can be implemented efficiently. For example, if a small or medium-sized enterprise (SME) wants to improve the SEO of its e-commerce site, this system allows the AI ​​to analyze the site's current state and propose specific improvement measures. After implementing the proposed measures, the AI ​​reports on their effectiveness, allowing the SEO efforts to be verified. In this way, by utilizing AI, SEO improvements can be efficiently implemented even without specialized knowledge of SEO, increasing online traffic and boosting sales and lead generation. Thus, the SEO improvement support system can efficiently analyze, propose, and report on the SEO strategies for client companies' web pages.

[0060] The SEO improvement support system according to this embodiment comprises an analysis unit, a proposal unit, and a reporting unit. The analysis unit analyzes the client company's web pages. The client company's web pages include, but are not limited to, product pages, blog posts, and landing pages. The analysis unit analyzes, for example, the metadata, keywords, internal link structure, and page loading speed of the client company's web pages. The analysis unit can also analyze web pages considering recent search engine trends and successful case studies of other companies. For example, the analysis unit considers the latest changes in search engine algorithms and identifies optimization points for web pages. The proposal unit proposes improvement measures based on the analysis results. The proposal unit proposes measures such as keyword optimization, meta tag correction, strengthening internal links, and improving page loading speed. For example, the proposal unit proposes increasing the frequency of use of specific keywords or optimizing meta descriptions. The proposal unit can also analyze the SEO status of the client company's web pages in detail and propose specific improvement measures. For example, the proposal unit analyzes the SEO measures of competitors and proposes improvements to the client company's web pages based on that analysis. The reporting department reports on the effectiveness of the proposed improvement measures. For example, the reporting department reports on changes in search engine rankings and increases in the number of website visits after the implementation of the improvement measures. For example, the reporting department reports on an increase in search engine rankings and an increase in the number of website visitors. The reporting department can also periodically report on the effectiveness of the proposed improvement measures. For example, the reporting department can create and provide monthly or quarterly reports to the client company. In this way, the SEO improvement support system according to the embodiment can efficiently analyze, propose, and report on SEO measures for the client company's website.

[0061] The analytics department conducts a detailed analysis of client companies' web pages. These pages include, but are not limited to, product pages, blog posts, and landing pages. First, the analytics department analyzes the web page metadata to ensure that elements such as title tags, meta descriptions, and meta keywords are properly configured. Next, they investigate keyword usage to assess whether target keywords are appropriately placed and whether there is excessive keyword usage. Internal link structure analysis verifies that links between pages are properly configured and that the anchor text of the links is relevant. Regarding page loading speed, they evaluate image optimization, caching, and server response time to identify areas for improvement. The analytics department can also analyze web pages considering recent search engine trends and successful case studies from other companies. For example, they consider the latest changes in search engine algorithms to identify optimization points for web pages. This includes evaluating mobile-friendly design and verifying security measures (such as HTTPS implementation). Furthermore, the analytics department considers the SEO requirements specific to the client company's industry and identifies strengths and weaknesses by comparing the web pages with those of competitors. This allows the analysis department to gain a detailed understanding of the current state of the client company's web pages and clearly identify specific areas for improvement.

[0062] The proposal team will propose specific improvement measures based on the analysis results. First, they will suggest keyword optimization, recommending a review of the frequency and placement of target keywords. For example, they might suggest increasing the frequency of certain keywords or optimizing meta descriptions. Regarding meta tag modifications, they will verify that title tags and meta descriptions provide appropriate information to search engines and make corrections as needed. For strengthening internal links, they will suggest increasing links between related pages to allow users to navigate the site smoothly. For improving page loading speed, they will propose specific measures such as image compression, caching, and reducing server response times. Furthermore, the proposal team can conduct a detailed analysis of the client company's web page SEO status and propose specific improvement measures. For example, they might analyze competitor SEO strategies and propose improvements to the client company's web page based on that analysis. This includes analyzing the keywords and link strategies used by competitors. The proposal team will also provide specific procedures and tool usage instructions for implementing these measures, supporting the client company in easily executing them. This allows the proposal team to effectively improve the client company's web page SEO and enhance its search engine rankings.

[0063] The reporting department will provide detailed reports on the effectiveness of proposed improvement measures. First, the reporting department will track changes in search engine rankings after the implementation of the improvement measures and report them with specific numerical data. For example, it will show the improvement in search rankings for a particular keyword. It will also provide detailed reports on increases in web page traffic, offering metrics such as visitor numbers, time spent on site, and page views. This allows client companies to concretely understand the effectiveness of the improvement measures. Furthermore, the reporting department can provide regular reports on the effectiveness of the proposed improvement measures. For example, monthly and quarterly reports will be created and provided to client companies. These reports will include the progress of the improvement measures, newly discovered issues, and recommended next steps. Through these reports, the reporting department provides client companies with information to continuously monitor the progress of their SEO efforts and adjust their strategies as needed. The reporting department also creates visual reports using graphs and charts to visually convey the effectiveness of the improvement measures. This makes it easier for client companies to intuitively understand the data and make quick decisions. The reporting department maintains close communication with client companies and continuously improves the report content by incorporating their feedback. This allows the reporting department to maximize the effectiveness of client companies' SEO measures and support their sustainable growth.

[0064] The analysis department can analyze web pages based on recent search engine trends and successful case studies from other companies. For example, the analysis department can identify optimization points for web pages by considering the latest changes in search engine algorithms. The analysis department can also refer to successful case studies from other companies and apply similar measures to the client company's web pages. For example, the analysis department can refer to successful keyword strategies and link-building methods of competitors. This enables analysis that takes into account the latest trends and successful case studies. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input recent search engine trend data into a generating AI and have the generating AI identify optimization points based on the trends.

[0065] The proposal team can suggest improvement measures such as keyword optimization, meta tag correction, and strengthening internal links. For example, the proposal team might suggest increasing the frequency of use of specific keywords or optimizing meta descriptions. The proposal team can also review the placement of internal links and strengthen them. For example, the proposal team might suggest increasing links to important pages or optimizing the anchor text of links. This allows for the proposal of specific SEO improvement measures. Some or all of the above processes performed by the proposal team may be carried out using AI, or not. For example, the proposal team could input keyword optimization suggestions into a generation AI and have the generation AI select the most suitable keywords.

[0066] The reporting unit can report on changes in search engine rankings and increases in website traffic after implementing improvement measures. For example, the reporting unit may report an increase in search engine rankings or an increase in the number of website visitors. The reporting unit can also show the effects of improvement measures with specific numerical data. For example, the reporting unit can display changes in search engine rankings in a graph and increases in traffic in a table. This allows for concrete confirmation of the effects of improvement measures. Some or all of the above processing in the reporting unit may be performed using AI, for example, or without AI. For example, the reporting unit can input search engine ranking data into a generating AI and analyze the changes in rankings.

[0067] The proposal department can conduct a detailed analysis of the SEO status of a client company's web page and propose specific improvement measures. For example, the proposal department can analyze the SEO strategies of competitors and propose improvements to the client company's web page based on that analysis. The proposal department can also conduct a detailed analysis of the internal link structure and metadata optimization points of the client company's web page. For example, the proposal department can propose revising the placement of internal links to strengthen them, or optimizing meta descriptions. This allows the proposal of specific improvement measures based on detailed analysis. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input competitor SEO data into a generating AI and have the generating AI propose the most suitable improvement measures.

[0068] The reporting department can periodically report on the effectiveness of proposed improvement measures. For example, the reporting department can create and provide monthly or quarterly reports to client companies. The reporting department can also show the effectiveness of improvement measures with specific numerical data. For example, the reporting department can display changes in search engine rankings in a graph and increase access numbers in a table. This allows for regular verification of the effectiveness of improvement measures. Some or all of the above processing in the reporting department may be performed using AI, for example, or not. For example, the reporting department can have a generation AI perform the periodic report generation.

[0069] The analysis unit can estimate the user's emotions and adjust the analysis priorities based on those emotions. For example, if the user is anxious, the analysis unit will prioritize analyzing urgent SEO issues. Conversely, if the user is relaxed, the analysis unit can analyze the overall SEO situation in a balanced way. For example, if the user is feeling anxious, the analysis unit will focus on analyzing areas with particularly high problem levels. This allows for analysis to be performed with priorities that match the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion-based priority adjustments.

[0070] The analysis department can select the optimal analysis method by referring to the client company's past SEO strategy history. For example, the analysis department can prioritize analyzing highly effective measures based on past SEO strategy history. The analysis department can also refer to past failures and conduct analyses to avoid making the same mistakes. For example, the analysis department can analyze past SEO trends and select the optimal method by comparing it with current trends. This allows for the selection of the optimal analysis method based on past history. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input past SEO strategy history data into a generating AI and have the generating AI select the optimal analysis method.

[0071] The analysis department can perform analyses while considering the industry-specific SEO requirements of client companies. For example, the analysis department can prioritize the analysis of industry-specific keywords. The analysis department can also refer to and compare the SEO strategies of competitors in the same industry. For example, the analysis department can conduct analyses to propose optimal SEO strategies, taking into account the latest industry trends. This enables analyses that consider industry-specific requirements. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input industry-specific SEO requirement data into a generating AI and have the generating AI perform an analysis based on those requirements.

[0072] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and highly visible display method. It can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is in a hurry, the analysis unit can provide a concise display method. This allows the analysis results to be presented in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the display method based on the emotions.

[0073] The analysis department can conduct analyses that take into account the geographical market characteristics of client companies. For example, the analysis department can analyze region-specific keywords based on geographical market characteristics. The analysis department can also refer to and compare the SEO strategies of regional competitors. For example, the analysis department can conduct analyses to propose optimal SEO strategies by considering regional consumer behavior. This makes it possible to conduct analyses that take geographical market characteristics into account. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input geographical market characteristic data into a generating AI and have the generating AI perform an analysis based on those characteristics.

[0074] The analysis department can improve the accuracy of its analysis by referring to the SEO strategies of the client company's competitors. For example, the analysis department can refer to the success stories of competitors and analyze similar measures. The analysis department can also refer to the failure stories of competitors and conduct analyses to avoid making the same mistakes. For example, the analysis department can analyze the latest SEO trends of competitors and select the optimal method. This improves the accuracy of the analysis by referring to the strategies of competitors. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input competitor SEO strategy data into a generating AI and have the generating AI perform a highly accurate analysis.

[0075] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can make simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. For example, if the user is in a hurry, the suggestion unit can make concise suggestions. This allows suggestions to be presented in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI adjust the presentation based on those emotions.

[0076] The proposal department can determine the priority of proposals based on the client company's business objectives. For example, the proposal department can prioritize proposing the most effective measures based on the client company's sales targets. The proposal department can also prioritize proposing measures aimed at improving the client company's brand awareness. For example, the proposal department can prioritize proposing measures aimed at promoting the client company's new products. This allows for proposals to be made in a priority order based on business objectives. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the client company's business objective data into a generating AI and have the generating AI perform the determination of priorities based on those objectives.

[0077] The proposal department can make proposals that take into account the industry-specific SEO requirements of the client company. For example, the proposal department can make proposals that optimize industry-specific keywords. The proposal department can also make proposals that refer to the success stories of competitors in the industry. For example, the proposal department can make proposals that take into account the latest industry trends. This makes it possible to make proposals that take into account industry-specific requirements. Some or all of the above processes in the proposal department may be performed using AI, or not using AI. For example, the proposal department can input industry-specific SEO requirement data into a generating AI and have the generating AI execute proposals based on those requirements.

[0078] The suggestion unit can estimate the user's emotions and adjust the level of detail of its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple, highly visible suggestions. If the user is relaxed, the suggestion unit can also provide suggestions with more detailed information. For example, if the user is in a hurry, the suggestion unit will provide concise suggestions. This allows for suggestions to be provided with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion-based level of detail adjustments.

[0079] The proposal department can make proposals that take into account the geographical market characteristics of the client company. For example, the proposal department can make proposals that optimize region-specific keywords based on geographical market characteristics. The proposal department can also make proposals that refer to the success stories of local competitors. For example, the proposal department can make proposals that take into account local consumer behavior. This makes it possible to make proposals that take geographical market characteristics into account. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input geographical market characteristic data into a generating AI and have the generating AI execute proposals based on those characteristics.

[0080] The proposal department can improve the accuracy of its proposals by referring to the success stories of the client company's competitors. For example, the proposal department can refer to the success stories of competitors and propose similar measures. Alternatively, the proposal department can refer to the failure stories of competitors and make suggestions to avoid the same mistakes. For example, the proposal department can analyze the latest SEO trends of competitors and propose the optimal approach. This improves the accuracy of proposals by referring to the success stories of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on competitor success stories into a generating AI and have the generating AI produce highly accurate proposals.

[0081] The reporting unit can estimate the user's emotions and adjust the report display method based on the estimated emotions. For example, if the user is stressed, the reporting unit can provide a simple and highly visible display method. Alternatively, if the user is relaxed, it can provide a display method that includes detailed information. For example, if the user is in a hurry, the reporting unit can provide a concise display method. This allows the report to be presented in a way that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI, or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the display method based on the emotions.

[0082] The reporting unit can optimize the report content by referring to the client company's past SEO strategy history. For example, the reporting unit can highlight the results of highly effective measures based on past SEO strategy history. The reporting unit can also refer to past failures and clearly identify areas for improvement. For example, the reporting unit can analyze past SEO trends and optimize the report by comparing it with current trends. This allows for the creation of an optimal report based on past history. Some or all of the above processes in the reporting unit may be performed using AI, or not. For example, the reporting unit can input past SEO strategy history data into a generating AI and have the generating AI create an optimal report.

[0083] The reporting department can create reports that take into account the industry-specific SEO requirements of client companies. For example, the reporting department can reflect the effectiveness of industry-specific keywords in its reports. It can also refer to and compare the SEO strategies of competitors in the industry. For example, the reporting department can optimize reports by considering the latest industry trends. This makes it possible to create reports that take industry-specific requirements into account. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input industry-specific SEO requirement data into a generating AI and have the generating AI execute a report based on those requirements.

[0084] The reporting unit can estimate the user's emotions and adjust the frequency of reports based on the estimated emotions. For example, if the user is stressed, the reporting unit can provide frequent reports to reassure them. It can also provide regular reports if the user is relaxed. For example, if the user is in a hurry, the reporting unit can quickly provide important information. This allows for reports to be provided at a frequency appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into a generative AI and have the generative AI adjust the report frequency based on emotions.

[0085] The reporting department can create reports that take into account the geographical market characteristics of client companies. For example, the reporting department can reflect the effectiveness of region-specific keywords in the report based on geographical market characteristics. The reporting department can also refer to and perform comparative analysis of the SEO strategies of regional competitors. For example, the reporting department can optimize the report by considering regional consumer behavior. This makes it possible to create reports that take geographical market characteristics into account. Some or all of the above processes in the reporting department may be performed using AI, for example, or not using AI. For example, the reporting department can input geographical market characteristic data into a generating AI and have the generating AI execute a report based on those characteristics.

[0086] The reporting department can improve the accuracy of its reports by referring to the SEO results of the client company's competitors. For example, the reporting department can refer to the success stories of competitors and reflect the results of similar measures in the report. The reporting department can also refer to the failure stories of competitors and clarify areas for improvement. For example, the reporting department can analyze the latest SEO trends of competitors and reflect the optimal methods in the report. This improves the accuracy of the report by referring to the results of competitors' strategies. Some or all of the above processes in the reporting department may be performed using AI, for example, or not. For example, the reporting department can input competitor SEO result data into a generating AI and have the generating AI produce a highly accurate report.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The SEO improvement support system can further estimate the user's emotions and customize the suggestions based on those emotions. For example, if the user is anxious, the suggestion section will prioritize suggesting urgent measures. If the user is relaxed, it can also suggest improvement measures from a long-term perspective. Furthermore, if the user is feeling uneasy, it can provide reassurance by offering suggestions that include concrete success stories. This enables optimal suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into the generative AI and have the generative AI customize the suggestions based on those emotions.

[0089] The SEO improvement support system can further prioritize proposals based on the client company's business objectives. For example, the proposal department can prioritize proposing the most effective measures based on the client company's sales targets. It can also prioritize proposing measures aimed at improving the client company's brand awareness. Furthermore, it can prioritize proposing measures aimed at promoting the client company's new products. This allows for proposals to be made in a priority order based on business objectives. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input the client company's business objective data into a generating AI and have the generating AI perform the determination of priorities based on those objectives.

[0090] The SEO improvement support system can further select the optimal analysis method by referring to the client company's past SEO strategy history. For example, the analysis department can prioritize analyzing highly effective measures based on past SEO strategy history. It can also refer to past failure cases and conduct analyses to avoid making the same mistakes. Furthermore, it can analyze past SEO trends and select the optimal method by comparing them with current trends. This allows for the selection of the optimal analysis method based on past history. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input past SEO strategy history data into a generating AI and have the generating AI select the optimal analysis method.

[0091] The SEO improvement support system can further analyze the client company's geographical market characteristics. For example, the analysis department can prioritize region-specific keywords based on geographical market characteristics. It can also refer to and compare the SEO strategies of local competitors. Furthermore, it can conduct analysis to propose optimal SEO strategies by considering local consumer behavior. This enables analysis that takes geographical market characteristics into account. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input geographical market characteristic data into a generating AI and have the generating AI perform analysis based on those characteristics.

[0092] The SEO improvement support system can further improve the accuracy of its analysis by referencing the SEO strategies of the client company's competitors. For example, the analysis department can refer to the success stories of competitors and analyze similar measures. It can also refer to the failure stories of competitors and conduct analyses to avoid making the same mistakes. Furthermore, it can analyze the latest SEO trends of competitors and select the optimal method. In this way, the accuracy of the analysis is improved by referring to the strategies of competitors. Some or all of the above processes in the analysis department may be performed using AI, for example, or not. For example, the analysis department can input competitor SEO strategy data into a generating AI and have the generating AI perform a highly accurate analysis.

[0093] The SEO improvement support system can further estimate the user's emotions and adjust the report display method based on the estimated emotions. For example, if the user is stressed, the report section can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. This allows the report to be delivered in a display method that suits the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the report section may be performed using AI or not using AI. For example, the report section can input user emotion data into the generative AI and have the generative AI perform the emotion-based adjustment of the display method.

[0094] The SEO improvement support system can further consider the industry-specific SEO requirements of client companies when making proposals. For example, the proposal department can propose optimizing industry-specific keywords. It can also make proposals that refer to the success stories of competitors in the same industry. Furthermore, it can make proposals that take into account the latest industry trends. This makes it possible to make proposals that take industry-specific requirements into account. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input industry-specific SEO requirement data into a generating AI and have the generating AI execute proposals based on those requirements.

[0095] The SEO improvement support system can further estimate the user's emotions and adjust the level of detail of suggestions based on those emotions. For example, if the user is stressed, the suggestion section will provide simple and highly visible suggestions. If the user is relaxed, it can provide suggestions with more detailed information. Furthermore, if the user is in a hurry, it can provide concise suggestions. This allows suggestions to be provided with a level of detail appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using AI or not. For example, the suggestion section can input user emotion data into the generative AI and have the generative AI perform the emotion-based level of detail adjustment.

[0096] The SEO improvement support system can further improve the accuracy of its proposals by referencing the success stories of the client company's competitors. For example, the proposal department can refer to the success stories of competitors and propose similar measures. It can also refer to the failure stories of competitors and make suggestions to avoid the same mistakes. Furthermore, it can analyze the latest SEO trends of competitors and propose the optimal method. In this way, the accuracy of the proposals is improved by referring to the success stories of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input data on the success stories of competitors into a generating AI and have the generating AI execute highly accurate proposals.

[0097] The SEO improvement support system can further estimate the user's emotions and adjust the frequency of reports based on those emotions. For example, if the user is stressed, the reporting unit can provide frequent reports to reassure them. If the user is relaxed, it can provide reports regularly. Furthermore, if the user is in a hurry, it can provide important information quickly. This allows reports to be provided at a frequency that matches the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not. For example, the reporting unit can input user emotion data into the generative AI and have the generative AI adjust the report frequency based on emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The analysis department analyzes the client company's web pages. Specifically, they analyze metadata, keywords, internal link structure, and page loading speed for product pages, blog posts, landing pages, etc. They also consider recent search engine trends and successful case studies from other companies, and identify optimization points for web pages based on the latest changes in search engine algorithms. Step 2: The proposal department proposes improvement measures based on the analysis results obtained by the analysis department. Specifically, they propose measures such as keyword optimization, meta tag correction, strengthening internal links, and improving page loading speed. They also analyze the SEO strategies of competitors and propose improvements to the client company's web page based on that analysis. Step 3: The reporting department reports on the effectiveness of the proposed improvement measures. Specifically, they report on changes in search engine rankings and increases in web page traffic after the implementation of the improvement measures. They also regularly report on the effectiveness of the proposed improvement measures and provide monthly and quarterly reports to the client company.

[0100] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0101] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0102] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0103] Each of the multiple elements described above, including the analysis unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit analyzes the client company's web pages using the control unit 46A of the smart device 14 and analyzes metadata, keywords, etc. The proposal unit proposes improvement measures based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The reporting unit reports the effects of the improvement measures using, for example, the control unit 46A of the smart device 14, and reports changes in search engine rankings and increases in access numbers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0107] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0108] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0109] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0110] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0111] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0112] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0113] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0114] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0115] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0116] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0117] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0118] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0119] Each of the multiple elements described above, including the analysis unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit analyzes the client company's web pages using the control unit 46A of the smart glasses 214 and analyzes metadata, keywords, etc. The proposal unit proposes improvement measures based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The reporting unit reports the effects of the improvement measures using, for example, the control unit 46A of the smart glasses 214, and reports changes in search engine rankings and increases in access numbers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0123] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0124] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0126] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0127] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0128] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0129] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0130] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0131] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0135] Each of the multiple elements described above, including the analysis unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit analyzes the client company's web page using the control unit 46A of the headset terminal 314 and analyzes metadata, keywords, etc. The proposal unit proposes improvement measures based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The reporting unit reports the effects of the improvement measures using, for example, the control unit 46A of the headset terminal 314, and reports changes in search engine rankings and increases in access numbers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0144] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the analysis unit, proposal unit, and reporting unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit analyzes the client company's web pages using the control unit 46A of the robot 414 and analyzes metadata, keywords, etc. The proposal unit proposes improvement measures based on the analysis results using, for example, the specific processing unit 290 of the data processing unit 12. The reporting unit reports on the effectiveness of the improvement measures using, for example, the control unit 46A of the robot 414, and reports on changes in search engine rankings and increases in access numbers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

[0154] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0155] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0156] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0157] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

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

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

[0160] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

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

[0162] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0163] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0164] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

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

[0166] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0168] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0169] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0170] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0171] (Note 1) The analytics department analyzes the web pages of client companies, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes improvement measures, The system includes a reporting unit that reports on the effectiveness of the improvement measures proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit is Analyze web pages based on recent search engine trends and successful case studies from other companies. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned proposal section is, We propose improvement measures such as keyword optimization, meta tag correction, and strengthening internal links. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned report section is, This report details changes in search engine rankings and increases in web page traffic after implementing improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We conduct a detailed analysis of the SEO status of client companies' websites and propose specific improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned report section is, Regularly report on the effectiveness of the proposed improvement measures. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is It estimates user sentiment and adjusts analysis priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is We select the optimal analysis method by referring to the client company's past SEO strategy history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is We conduct analysis while considering the SEO requirements specific to the client company's industry. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is The analysis will take into account the geographical market characteristics of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is Improve the accuracy of your analysis by referencing the SEO strategies of your client company's competitors. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned proposal section is, Prioritize proposals based on the client company's business objectives. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned proposal section is, We make proposals that take into account the industry-specific SEO requirements of our client companies. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned proposal section is, It estimates the user's emotions and adjusts the level of detail of suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned proposal section is, We make proposals that take into account the geographical market characteristics of our client companies. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, We improve the accuracy of our proposals by referencing the success stories of our client company's competitors. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned report section is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned report section is, The report content is optimized by referencing the client company's past SEO strategy history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned report section is, We create reports that take into account the industry-specific SEO requirements of our client companies. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned report section is, It estimates user sentiment and adjusts the frequency of reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned report section is, The report is prepared taking into account the geographical market characteristics of the client company. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned report section is, Improve the accuracy of reports by referencing the SEO results of client companies' competitors. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analytics department analyzes the web pages of client companies, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes improvement measures, The system includes a reporting unit that reports on the effectiveness of the improvement measures proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned analysis unit is Analyze web pages based on recent search engine trends and successful case studies from other companies. The system according to feature 1.

3. The aforementioned proposal section is, We propose improvement measures such as keyword optimization, meta tag correction, and strengthening internal links. The system according to feature 1.

4. The aforementioned report section is, This report details changes in search engine rankings and increases in web page traffic after implementing improvement measures. The system according to feature 1.

5. The aforementioned proposal section is, We conduct a detailed analysis of the SEO status of client companies' websites and propose specific improvement measures. The system according to feature 1.

6. The aforementioned report section is, Regularly report on the effectiveness of the proposed improvement measures. The system according to feature 1.

7. The aforementioned analysis unit is It estimates user sentiment and adjusts analysis priorities based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned analysis unit is We select the optimal analysis method by referring to the client company's past SEO strategy history. The system according to feature 1.

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