system

Abstract

The system according to this embodiment aims to generate drafts of relevant content based on keywords and topics, and to automate keyword suggestions and placement for SEO optimization. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a suggestion and placement unit. The reception unit receives input of keywords or topics. The generation unit analyzes the keywords or topics received by the reception unit and generates a draft of related content. The suggestion and placement unit suggests and places keywords for SEO in the draft generated by the generation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of 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, the generation of drafts of related content based on keywords or topics and the proposal and arrangement of keywords for optimizing SEO are not sufficiently automated, and there is room for improvement.

[0005] The system according to the embodiment aims to generate a draft of related content based on keywords or topics and automate the proposal and arrangement of keywords for optimizing SEO.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, and a suggestion and placement unit. The reception unit receives input of keywords or topics. The generation unit analyzes the keywords or topics received by the reception unit and generates a draft of related content. The suggestion and placement unit suggests and places keywords for SEO in the draft generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate drafts of relevant content based on keywords and topics, and automate keyword suggestions and placement to optimize SEO. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated blog and article generation system according to an embodiment of the present invention provides the following functions using an AI assistant. First, the user inputs a keyword or topic. Next, the AI ​​analyzes the keyword or topic and generates a draft of related content. This draft includes keyword suggestions and placement to optimize SEO. It is also possible to learn the user's past writing style and generate personalized text accordingly. For example, the user inputs a keyword or topic. In this case, input suggestions are presented based on the user's current areas of interest. For example, if the user inputs the keyword "health," related topics such as "diet" and "exercise" are presented. Next, the AI ​​analyzes the input keyword or topic and generates a draft of related content. The generated draft includes keyword suggestions and placement to optimize SEO. For example, for the keyword "health," related keywords such as "healthy eating" and "importance of exercise" are suggested and appropriately placed. Furthermore, it learns the user's past writing style and generates personalized text accordingly. For example, it learns the writing style and expressions of articles the user has written in the past and generates a new draft based on that. This generates text that reflects the user's personality. This system allows users to easily generate high-quality blogs and articles. For example, keyword suggestions and placement for SEO optimization are automatically performed, leading to higher rankings in search engine results. Furthermore, personalized text is generated based on the user's past writing style, providing engaging content for readers. In short, this automated blog and article generation system allows users to easily produce high-quality content.

[0029] The automated blog and article generation system according to this embodiment comprises a reception unit, a generation unit, and an optimization unit. The reception unit accepts input of keywords or topics. For example, the reception unit can accept keywords or topics entered by a user. The reception unit can also use AI to analyze the user's input and suggest appropriate keywords or topics. The generation unit analyzes the keywords or topics received by the reception unit and generates a draft of related content. The generation unit uses generation AI to generate a draft based on the user's input. For example, the generation unit can use natural language processing technology to collect information related to the entered keywords or topics and generate a draft. The generation unit can also use generation AI to learn the user's past writing style and generate a personalized draft based on it. The optimization unit suggests and places keywords to optimize SEO in the draft generated by the generation unit. The optimization unit uses AI to suggest appropriate keywords for the generated draft and place them appropriately. The optimization unit optimizes the draft using, for example, keyword selection criteria and optimization methods for placement for SEO. As a result, the automated blog and article generation system according to the embodiment can automatically perform tasks from keyword or topic input to SEO optimization.

[0030] The reception desk accepts keyword or topic input. Specifically, it can accept keywords and topics entered by the user. For example, if a user enters keywords such as "latest technology trends" or "healthy eating habits," the reception desk receives them and passes them on to the next process. Furthermore, the reception desk can use AI to analyze the user's input and suggest appropriate keywords and topics. For example, if a user enters "technology," the AI ​​will suggest relevant specific topics such as "latest trends in artificial intelligence" or "advancements in 5G technology." This allows the user to select more specific and effective topics. The AI ​​uses natural language processing technology to analyze the user's input and extract highly relevant keywords and topics. For example, if a user enters "health," the AI ​​will suggest related topics such as "healthy eating," "exercise habits," and "mental health." This allows the reception desk to accurately understand the user's intent and provide appropriate keywords and topics. In addition, the reception desk can learn from the user's past input history and provide personalized suggestions based on the user's preferences and interests. For example, users who have frequently entered information on "fitness" topics in the past will be given priority in being suggested topics such as "latest fitness trends" and "effective training methods." This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0031] The generation unit analyzes keywords or topics received by the reception unit and generates a draft of relevant content. Specifically, it uses a generation AI to generate a draft based on the user's input. The generation AI utilizes natural language processing technology to collect information related to the input keywords and topics from the internet and databases, and then generates the draft. For example, if the keyword "latest technology trends" is entered, the generation AI will refer to the latest technology news, research papers, blog posts, etc., and create a draft based on this information. The generation AI can also learn the user's past writing style and generate a personalized draft based on that. For example, it can learn the writing style and expressions of articles the user has written in the past and generate a new draft in a similar style. As a result, the generated draft will match the user's personality and brand. Furthermore, the generation unit also has the function to automatically proofread the draft generated by the generation AI and correct grammatical and spelling errors. This allows the user to quickly obtain a high-quality draft. The generation unit can also receive user feedback and continuously improve the generation AI's algorithm. For example, if a user makes revisions to a generated draft, the system learns from those revisions and incorporates them into subsequent draft generation. This allows the generation unit to consistently provide high-quality drafts that are up-to-date and meet user needs.

[0032] The optimization unit proposes and places keywords to optimize SEO in the draft generated by the generation unit. Specifically, it uses AI to suggest appropriate keywords for the generated draft and place them appropriately. For example, if the generated draft is about "latest technology trends," the AI ​​will suggest keywords such as "technology," "latest," "trend," and "technological innovation," and place them effectively. The optimization unit optimizes the draft using keyword selection criteria and placement optimization methods for SEO. For example, it optimizes keyword density, placement, headings, and meta tags. As a result, the generated draft is expected to rank higher in search engine results. Furthermore, the optimization unit can also analyze the SEO strategies of competitors and propose optimal keywords and placement methods based on that analysis. For example, it analyzes the keywords and placement methods used by competitors and uses that as a reference to optimize the draft. The optimization unit also has the function of collecting performance data of the user's website and continuously monitoring the effectiveness of SEO. For example, it collects data such as how much traffic a particular keyword generates and which pages are visited the most, and adjusts the SEO strategy based on this. This allows the optimization section to constantly adapt to the latest SEO trends and maximize the performance of the user's website.

[0033] The generation unit can learn the user's past writing style and generate drafts of content related to keywords or topics received by the reception unit, based on the user's past writing style. For example, the generation unit can learn the writing style and expressions of articles previously written by the user and generate new drafts based on them. The generation unit can use a generation AI to learn the user's past writing style and generate personalized drafts based on it. This allows for the generation of personalized drafts tailored to the user's past writing style. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, to learn the user's past writing style, the generation unit inputs past text data into the generation AI, which then analyzes and learns from that data. Based on the learned data, the generation unit generates new drafts.

[0034] The reception desk can suggest input options based on the user's areas of interest when a keyword or topic is entered. For example, if the user enters the keyword "health," the reception desk may suggest related topics such as "diet" and "exercise." The reception desk can use AI to analyze the user's current areas of interest and suggest appropriate input options based on that. This allows the reception desk to suggest appropriate input options based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, to identify the user's areas of interest, the reception desk inputs past search history and survey results into the AI, which then analyzes the data to identify the areas of interest. Based on the identified areas of interest, the reception desk suggests appropriate input options.

[0035] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display keywords that the user has frequently used in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest keywords that the user will use during specific time periods based on the user's past input history. This improves input efficiency by selecting the optimal input method based on the user's past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI, which then analyzes the data to select the optimal input method.

[0036] The reception desk can filter keywords or topics based on the user's current projects and areas of interest when they are entered. For example, the reception desk may prioritize displaying keywords related to the project the user is currently working on. The reception desk can suggest highly relevant topics based on the user's areas of interest. The reception desk can filter appropriate keywords by referring to the user's past project history. This allows for filtering of appropriate keywords and topics based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's project data into an AI, which then analyzes the data and filters appropriate keywords.

[0037] The input system can prioritize and present highly relevant suggestions when a user enters keywords or topics, taking into account their geographical location. For example, if a user is in a specific region, the input system will prioritize displaying keywords related to that region. The input system can suggest local topics based on the user's current location. If a user is traveling, the input system can prioritize displaying keywords related to their travel destination. This allows users to enter more appropriate keywords and topics by presenting highly relevant suggestions based on their geographical location. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system may obtain the user's geographical location information from GPS data or IP address, input it into the AI, and the AI ​​will analyze the data to present highly relevant suggestions.

[0038] The reception desk can analyze the user's social media activity and suggest relevant options when keywords or topics are entered. For example, the reception desk can display keywords that the user frequently uses on social media as suggestions. The reception desk can analyze the content of the user's social media posts and suggest relevant topics. The reception desk can suggest appropriate keywords based on topics that the user's followers and friends are interested in. This allows users to enter more appropriate keywords and topics by suggesting relevant options based on their social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which then analyzes the data and suggests relevant options.

[0039] The generation unit can adjust the level of detail in the draft based on the importance of keywords during draft generation. For example, the generation unit can add detailed explanations to important keywords. The generation unit can add concise explanations to less important keywords. The generation unit can adjust the length of paragraphs according to the importance of keywords. This allows for the generation of more appropriate drafts by adjusting the level of detail in the draft based on the importance of keywords. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs search volume and competition data into the generation AI to evaluate keyword importance, and the generation AI analyzes the data to evaluate importance. The generation unit then adjusts the level of detail in the draft based on the evaluated importance.

[0040] The generation unit can apply an appropriate generation algorithm to the topic category when generating a draft. For example, for technical topics, the generation unit can apply an algorithm that uses a lot of technical jargon. For lifestyle topics, the generation unit can apply an algorithm that uses familiar language. For news topics, the generation unit can apply an algorithm that emphasizes factual information. By applying different generation algorithms according to the topic category, a more appropriate draft can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, in order to classify the topic category, the generation unit inputs topic data into a generation AI, and the generation AI analyzes the data and classifies the category. Based on the classified category, the generation unit applies an appropriate generation algorithm.

[0041] The generation unit can determine the priority of drafts by considering the timing of keyword submissions when generating drafts. For example, the generation unit can determine the priority of drafts based on the most recent keywords. The generation unit can determine the priority of drafts based on keywords relevant to the season. The generation unit can determine the priority of drafts based on keywords related to a specific event. This allows for the generation of more appropriate drafts by determining the priority of drafts based on the timing of keyword submissions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, in order to evaluate the timing of keyword submissions, the generation unit inputs data on the date and time of submission and submission frequency into the generation AI, which then analyzes the data to evaluate the submission timing. Based on the evaluated submission timing, the generation unit determines the priority of drafts.

[0042] The generation unit can adjust the order of drafts based on the relevance of keywords during draft generation. For example, the generation unit can determine the order of drafts based on major keywords. The generation unit can adjust the order of drafts by prioritizing highly relevant keywords. The generation unit can adjust the order of drafts according to the importance of keywords. This allows for the generation of more appropriate drafts by adjusting the order of drafts based on the relevance of keywords. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, to evaluate the relevance of keywords, the generation unit inputs data on co-occurrence frequency and semantic relevance into the generation AI, which then analyzes the data and evaluates the relevance. The generation unit then adjusts the order of drafts based on the evaluated relevance.

[0043] The optimization unit can select appropriate keyword placements by referring to past SEO data during optimization. For example, the optimization unit can select the most effective keyword placements based on past SEO data. The optimization unit can analyze past SEO data and refer to the keyword placements of competitors. The optimization unit can select keyword placements that are effective for specific time periods based on past SEO data. In this way, the optimal keyword placement can be selected by referring to past SEO data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past SEO data into AI, and the AI ​​will analyze that data to select the optimal keyword placement.

[0044] The optimization unit can apply appropriate optimization methods to each keyword category during optimization. For example, for technical keywords, the optimization unit can apply an optimization method that uses a lot of technical terms. For lifestyle keywords, the optimization unit can apply an optimization method that uses friendly language. For news keywords, the optimization unit can apply an optimization method that emphasizes factual information. By applying different optimization methods to each keyword category, more effective SEO optimization becomes possible. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, to classify keyword categories, the optimization unit inputs keyword data into AI, and the AI ​​analyzes the data to classify the categories. Based on the classified categories, the optimization unit applies an appropriate optimization method.

[0045] The optimization unit can determine optimization priorities by considering the timing of keyword submissions during the optimization process. For example, the optimization unit can determine optimization priorities based on the latest keywords. The optimization unit can determine optimization priorities based on keywords relevant to the season. The optimization unit can determine optimization priorities based on keywords related to specific events. This allows for more effective SEO optimization by determining optimization priorities based on the timing of keyword submissions. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, to evaluate the timing of keyword submissions, the optimization unit inputs data on the submission date and time and submission frequency into the AI, which then analyzes the data to evaluate the submission timing. Based on the evaluated submission timing, the optimization unit determines the optimization priorities.

[0046] The optimization unit can perform optimization by referring to market data for keywords during the optimization process. For example, the optimization unit can select the most effective keywords based on market data. The optimization unit can analyze market data and refer to the keyword strategies of competitors. The optimization unit can select keywords that are effective during specific time periods based on market data. This enables more effective SEO optimization by referring to relevant market data for keywords. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input market data for keywords into AI, and the AI ​​can analyze that data to select the most suitable keywords.

[0047] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0048] When analyzing user input, the reception desk can consider the user's past search and browsing history to suggest more personalized keywords and topics. For example, if a user has frequently searched for articles related to "health" in the past, the reception desk will prioritize suggesting new topics and keywords related to "health." Furthermore, if a user frequently visits a specific website, it can suggest relevant keywords based on the content of that website. It can also analyze the trends of keywords the user has searched in the past and suggest keywords that are relevant to the season and current trends. This allows for the suggestion of more relevant keywords and topics based on the user's past behavioral data.

[0049] The optimization unit can optimize keyword placement in the generated draft by referencing the user's past SEO performance data. For example, it can analyze keyword placement patterns that have shown high performance in the past and apply them to the new draft. It can also optimize keyword placement for specific times of day or seasons based on past SEO performance data. Furthermore, it can suggest more effective keyword placement by referencing the SEO performance data of competitors. By optimizing SEO based on past data, higher rankings in search engine results can be expected.

[0050] The generation unit can generate drafts based not only on the user's past writing style but also on their current projects and goals. For example, it can prioritize keywords and topics related to the user's current project. It can also adjust the content and style of the draft based on the user's goals (e.g., appealing to a specific audience or focusing on a specific theme). Furthermore, it can adjust the speed and level of detail of the draft generation based on deadlines and schedules set by the user. This allows for the generation of personalized drafts tailored to the user's current situation and goals.

[0051] The optimization unit can optimize the generated draft for SEO, taking into account the user's current areas of interest and trends. For example, it can prioritize keywords related to topics the user is currently interested in. It can also suggest highly relevant keywords based on current trends and topics. Furthermore, it can place keywords to appeal to specific reader segments based on the user's areas of interest. In this way, by optimizing SEO based on the user's current areas of interest and trends, more effective content can be delivered.

[0052] The reception desk can suggest region-specific keywords and topics, taking into account the user's geographical location. For example, if the user is in a specific city, it can suggest events and news related to that city as keywords. If the user is traveling, it can also suggest tourist spots and local specialties related to their destination as keywords. Furthermore, it can suggest keywords related to local trends and topics based on the user's geographical location. This allows for the suggestion of more relevant keywords and topics based on the user's geographical location.

[0053] The following briefly describes the processing flow for example form 1.

[0054] Step 1: The reception desk accepts keyword or topic input. For example, it can accept keywords or topics entered by the user. Furthermore, the reception desk can use AI to analyze the user's input and suggest appropriate keywords or topics. Step 2: The generation unit analyzes the keywords or topics received by the reception unit and generates a draft of relevant content. The generation unit uses generation AI to generate a draft based on the user's input. For example, it can use natural language processing technology to collect information related to the entered keywords and topics and generate a draft. The generation unit can also learn the user's past writing style and generate a personalized draft based on that. Step 3: The optimization unit proposes and places keywords to optimize SEO in the draft generated by the generation unit. The optimization unit can propose appropriate keywords for the draft generated using AI and place them appropriately. For example, it optimizes the draft using keyword selection criteria and optimization methods for placement for SEO.

[0055] (Example of form 2) The automated blog and article generation system according to an embodiment of the present invention provides the following functions using an AI assistant. First, the user inputs a keyword or topic. Next, the AI ​​analyzes the keyword or topic and generates a draft of related content. This draft includes keyword suggestions and placement to optimize SEO. It is also possible to learn the user's past writing style and generate personalized text accordingly. For example, the user inputs a keyword or topic. In this case, input suggestions are presented based on the user's current areas of interest. For example, if the user inputs the keyword "health," related topics such as "diet" and "exercise" are presented. Next, the AI ​​analyzes the input keyword or topic and generates a draft of related content. The generated draft includes keyword suggestions and placement to optimize SEO. For example, for the keyword "health," related keywords such as "healthy eating" and "importance of exercise" are suggested and appropriately placed. Furthermore, it learns the user's past writing style and generates personalized text accordingly. For example, it learns the writing style and expressions of articles the user has written in the past and generates a new draft based on that. This generates text that reflects the user's personality. This system allows users to easily generate high-quality blogs and articles. For example, keyword suggestions and placement for SEO optimization are automatically performed, leading to higher rankings in search engine results. Furthermore, personalized text is generated based on the user's past writing style, providing engaging content for readers. In short, this automated blog and article generation system allows users to easily produce high-quality content.

[0056] The automated blog and article generation system according to this embodiment comprises a reception unit, a generation unit, and an optimization unit. The reception unit accepts input of keywords or topics. For example, the reception unit can accept keywords or topics entered by a user. The reception unit can also use AI to analyze the user's input and suggest appropriate keywords or topics. The generation unit analyzes the keywords or topics received by the reception unit and generates a draft of related content. The generation unit uses generation AI to generate a draft based on the user's input. For example, the generation unit can use natural language processing technology to collect information related to the entered keywords or topics and generate a draft. The generation unit can also use generation AI to learn the user's past writing style and generate a personalized draft based on it. The optimization unit suggests and places keywords to optimize SEO in the draft generated by the generation unit. The optimization unit uses AI to suggest appropriate keywords for the generated draft and place them appropriately. The optimization unit optimizes the draft using, for example, keyword selection criteria and optimization methods for placement for SEO. As a result, the automated blog and article generation system according to the embodiment can automatically perform tasks from keyword or topic input to SEO optimization.

[0057] The reception desk accepts keyword or topic input. Specifically, it can accept keywords and topics entered by the user. For example, if a user enters keywords such as "latest technology trends" or "healthy eating habits," the reception desk receives them and passes them on to the next process. Furthermore, the reception desk can use AI to analyze the user's input and suggest appropriate keywords and topics. For example, if a user enters "technology," the AI ​​will suggest relevant specific topics such as "latest trends in artificial intelligence" or "advancements in 5G technology." This allows the user to select more specific and effective topics. The AI ​​uses natural language processing technology to analyze the user's input and extract highly relevant keywords and topics. For example, if a user enters "health," the AI ​​will suggest related topics such as "healthy eating," "exercise habits," and "mental health." This allows the reception desk to accurately understand the user's intent and provide appropriate keywords and topics. In addition, the reception desk can learn from the user's past input history and provide personalized suggestions based on the user's preferences and interests. For example, users who have frequently entered information on "fitness" topics in the past will be given priority in being suggested topics such as "latest fitness trends" and "effective training methods." This allows the reception desk to respond flexibly to user needs and improve the user experience.

[0058] The generation unit analyzes keywords or topics received by the reception unit and generates a draft of relevant content. Specifically, it uses a generation AI to generate a draft based on the user's input. The generation AI utilizes natural language processing technology to collect information related to the input keywords and topics from the internet and databases, and then generates the draft. For example, if the keyword "latest technology trends" is entered, the generation AI will refer to the latest technology news, research papers, blog posts, etc., and create a draft based on this information. The generation AI can also learn the user's past writing style and generate a personalized draft based on that. For example, it can learn the writing style and expressions of articles the user has written in the past and generate a new draft in a similar style. As a result, the generated draft will match the user's personality and brand. Furthermore, the generation unit also has the function to automatically proofread the draft generated by the generation AI and correct grammatical and spelling errors. This allows the user to quickly obtain a high-quality draft. The generation unit can also receive user feedback and continuously improve the generation AI's algorithm. For example, if a user makes revisions to a generated draft, the system learns from those revisions and incorporates them into subsequent draft generation. This allows the generation unit to consistently provide high-quality drafts that are up-to-date and meet user needs.

[0059] The optimization unit proposes and places keywords to optimize SEO in the draft generated by the generation unit. Specifically, it uses AI to suggest appropriate keywords for the generated draft and place them appropriately. For example, if the generated draft is about "latest technology trends," the AI ​​will suggest keywords such as "technology," "latest," "trend," and "technological innovation," and place them effectively. The optimization unit optimizes the draft using keyword selection criteria and placement optimization methods for SEO. For example, it optimizes keyword density, placement, headings, and meta tags. As a result, the generated draft is expected to rank higher in search engine results. Furthermore, the optimization unit can also analyze the SEO strategies of competitors and propose optimal keywords and placement methods based on that analysis. For example, it analyzes the keywords and placement methods used by competitors and uses that as a reference to optimize the draft. The optimization unit also has the function of collecting performance data of the user's website and continuously monitoring the effectiveness of SEO. For example, it collects data such as how much traffic a particular keyword generates and which pages are visited the most, and adjusts the SEO strategy based on this. This allows the optimization section to constantly adapt to the latest SEO trends and maximize the performance of the user's website.

[0060] The generation unit can learn the user's past writing style and generate drafts of content related to keywords or topics received by the reception unit, based on the user's past writing style. For example, the generation unit can learn the writing style and expressions of articles previously written by the user and generate new drafts based on them. The generation unit can use a generation AI to learn the user's past writing style and generate personalized drafts based on it. This allows for the generation of personalized drafts tailored to the user's past writing style. Some or all of the above-described processes in the generation unit may be performed using a generation AI or not. For example, to learn the user's past writing style, the generation unit inputs past text data into the generation AI, which then analyzes and learns from that data. Based on the learned data, the generation unit generates new drafts.

[0061] The reception desk can suggest input options based on the user's areas of interest when a keyword or topic is entered. For example, if the user enters the keyword "health," the reception desk may suggest related topics such as "diet" and "exercise." The reception desk can use AI to analyze the user's current areas of interest and suggest appropriate input options based on that. This allows the reception desk to suggest appropriate input options based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, to identify the user's areas of interest, the reception desk inputs past search history and survey results into the AI, which then analyzes the data to identify the areas of interest. Based on the identified areas of interest, the reception desk suggests appropriate input options.

[0062] The reception unit can estimate the user's emotions and adjust the timing of keyword or topic input considering the estimated emotions. For example, if the user is stressed, the reception unit can delay the input timing to allow them to relax. If the user is concentrating, the reception unit can send an immediate notification prompting them to input. If the user is tired, the reception unit can flexibly adjust the input timing to encourage them to take a break. This allows users to input keywords or topics at a more appropriate time by adjusting the input timing according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit acquires the user's facial expression data from a camera, inputs it into the generative AI, and the generative AI analyzes the data to estimate emotions. Based on the estimated emotions, the reception unit adjusts the input timing.

[0063] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can automatically display keywords that the user has frequently used in the past as suggestions. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest keywords that the user will use during specific time periods based on the user's past input history. This improves input efficiency by selecting the optimal input method based on the user's past input history. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk can input the user's past input data into an AI, which then analyzes the data to select the optimal input method.

[0064] The reception desk can filter keywords or topics based on the user's current projects and areas of interest when they are entered. For example, the reception desk may prioritize displaying keywords related to the project the user is currently working on. The reception desk can suggest highly relevant topics based on the user's areas of interest. The reception desk can filter appropriate keywords by referring to the user's past project history. This allows for filtering of appropriate keywords and topics based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk may input the user's project data into an AI, which then analyzes the data and filters appropriate keywords.

[0065] The reception unit can estimate the user's emotions and prioritize input suggestions based on those emotions. For example, if the user is relaxed, the reception unit may prioritize displaying detailed input suggestions. If the user is in a hurry, the reception unit may prioritize displaying concise input suggestions. If the user is excited, the reception unit may prioritize displaying visually appealing input suggestions. This allows for the presentation of more appropriate input suggestions by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit acquires user facial expression data from a camera, inputs it into the generative AI, and the generative AI analyzes the data to estimate emotions. Based on the estimated emotions, the reception unit determines the priority of input suggestions.

[0066] The input system can prioritize and present highly relevant suggestions when a user enters keywords or topics, taking into account their geographical location. For example, if a user is in a specific region, the input system will prioritize displaying keywords related to that region. The input system can suggest local topics based on the user's current location. If a user is traveling, the input system can prioritize displaying keywords related to their travel destination. This allows users to enter more appropriate keywords and topics by presenting highly relevant suggestions based on their geographical location. Some or all of the above processing in the input system may be performed using AI or not. For example, the input system may obtain the user's geographical location information from GPS data or IP address, input it into the AI, and the AI ​​will analyze the data to present highly relevant suggestions.

[0067] The reception desk can analyze the user's social media activity and suggest relevant options when keywords or topics are entered. For example, the reception desk can display keywords that the user frequently uses on social media as suggestions. The reception desk can analyze the content of the user's social media posts and suggest relevant topics. The reception desk can suggest appropriate keywords based on topics that the user's followers and friends are interested in. This allows users to enter more appropriate keywords and topics by suggesting relevant options based on their social media activity. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's social media data into an AI, which then analyzes the data and suggests relevant options.

[0068] The generation unit can estimate the user's emotions and adjust the way the draft is expressed, taking the estimated emotions into consideration. For example, if the user is relaxed, the generation unit can generate a draft using soft language. If the user is in a hurry, the generation unit can generate a concise and to-the-point draft. If the user is excited, the generation unit can generate a draft using energetic language. In this way, by adjusting the way the draft is expressed according to the user's emotions, a more appropriate draft can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using the generation AI or not. For example, the generation unit acquires the user's facial expression data from a camera, inputs it into the generation AI, and the generation AI analyzes the data to estimate emotions. Based on the estimated emotions, the generation unit adjusts the way the draft is expressed.

[0069] The generation unit can adjust the level of detail in the draft based on the importance of keywords during draft generation. For example, the generation unit can add detailed explanations to important keywords. The generation unit can add concise explanations to less important keywords. The generation unit can adjust the length of paragraphs according to the importance of keywords. This allows for the generation of more appropriate drafts by adjusting the level of detail in the draft based on the importance of keywords. Some or all of the above processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit inputs search volume and competition data into the generation AI to evaluate keyword importance, and the generation AI analyzes the data to evaluate importance. The generation unit then adjusts the level of detail in the draft based on the evaluated importance.

[0070] The generation unit can apply an appropriate generation algorithm to the topic category when generating a draft. For example, for technical topics, the generation unit can apply an algorithm that uses a lot of technical jargon. For lifestyle topics, the generation unit can apply an algorithm that uses familiar language. For news topics, the generation unit can apply an algorithm that emphasizes factual information. By applying different generation algorithms according to the topic category, a more appropriate draft can be generated. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, in order to classify the topic category, the generation unit inputs topic data into a generation AI, and the generation AI analyzes the data and classifies the category. Based on the classified category, the generation unit applies an appropriate generation algorithm.

[0071] The generation unit can estimate the user's emotions and adjust the length of the draft considering the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise draft. If the user is relaxed, the generation unit can generate a longer draft with detailed explanations. If the user is excited, the generation unit can generate a draft with visually stimulating effects. This allows for the generation of more appropriate drafts by adjusting the length of the draft according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 processing in the generation unit may be performed using or without a generation AI. For example, the generation unit acquires user facial expression data from a camera, inputs it into the generation AI, and the generation AI analyzes the data to estimate emotions. The generation unit then adjusts the length of the draft based on the estimated emotions.

[0072] The generation unit can determine the priority of drafts by considering the timing of keyword submissions when generating drafts. For example, the generation unit can determine the priority of drafts based on the most recent keywords. The generation unit can determine the priority of drafts based on keywords relevant to the season. The generation unit can determine the priority of drafts based on keywords related to a specific event. This allows for the generation of more appropriate drafts by determining the priority of drafts based on the timing of keyword submissions. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, in order to evaluate the timing of keyword submissions, the generation unit inputs data on the date and time of submission and submission frequency into the generation AI, which then analyzes the data to evaluate the submission timing. Based on the evaluated submission timing, the generation unit determines the priority of drafts.

[0073] The generation unit can adjust the order of drafts based on the relevance of keywords during draft generation. For example, the generation unit can determine the order of drafts based on major keywords. The generation unit can adjust the order of drafts by prioritizing highly relevant keywords. The generation unit can adjust the order of drafts according to the importance of keywords. This allows for the generation of more appropriate drafts by adjusting the order of drafts based on the relevance of keywords. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, to evaluate the relevance of keywords, the generation unit inputs data on co-occurrence frequency and semantic relevance into the generation AI, which then analyzes the data and evaluates the relevance. The generation unit then adjusts the order of drafts based on the evaluated relevance.

[0074] The optimization unit can estimate the user's emotions and adjust the SEO optimization method considering the estimated user emotions. For example, if the user is relaxed, the optimization unit can perform detailed SEO optimization. If the user is in a hurry, the optimization unit can perform concise SEO optimization. If the user is excited, the optimization unit can perform visually appealing SEO optimization. This allows for more effective SEO optimization by adjusting the SEO optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 optimization unit may be performed using AI or not. For example, the optimization unit acquires user facial expression data from a camera, inputs it into the generative AI, and the generative AI analyzes the data to estimate emotions. Based on the estimated emotions, the optimization unit adjusts the SEO optimization method.

[0075] The optimization unit can select appropriate keyword placements by referring to past SEO data during optimization. For example, the optimization unit can select the most effective keyword placements based on past SEO data. The optimization unit can analyze past SEO data and refer to the keyword placements of competitors. The optimization unit can select keyword placements that are effective for specific time periods based on past SEO data. In this way, the optimal keyword placement can be selected by referring to past SEO data. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input past SEO data into AI, and the AI ​​will analyze that data to select the optimal keyword placement.

[0076] The optimization unit can apply appropriate optimization methods to each keyword category during optimization. For example, for technical keywords, the optimization unit can apply an optimization method that uses a lot of technical terms. For lifestyle keywords, the optimization unit can apply an optimization method that uses friendly language. For news keywords, the optimization unit can apply an optimization method that emphasizes factual information. By applying different optimization methods to each keyword category, more effective SEO optimization becomes possible. Some or all of the above processing in the optimization unit may be performed using AI or not. For example, to classify keyword categories, the optimization unit inputs keyword data into AI, and the AI ​​analyzes the data to classify the categories. Based on the classified categories, the optimization unit applies an appropriate optimization method.

[0077] The optimization unit can estimate the user's emotions and determine keyword priorities based on those emotions. For example, if the user is relaxed, the optimization unit can prioritize detailed keywords. If the user is in a hurry, the optimization unit can prioritize concise keywords. If the user is excited, the optimization unit can prioritize visually appealing keywords. This allows for more effective SEO optimization by prioritizing keywords according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI 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 optimization unit may be performed using AI or not. For example, the optimization unit acquires user facial expression data from a camera, inputs it into the generative AI, and the generative AI analyzes the data to estimate emotions. Based on the estimated emotions, the optimization unit determines keyword priorities.

[0078] The optimization unit can determine optimization priorities by considering the timing of keyword submissions during the optimization process. For example, the optimization unit can determine optimization priorities based on the latest keywords. The optimization unit can determine optimization priorities based on keywords relevant to the season. The optimization unit can determine optimization priorities based on keywords related to specific events. This allows for more effective SEO optimization by determining optimization priorities based on the timing of keyword submissions. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, to evaluate the timing of keyword submissions, the optimization unit inputs data on the submission date and time and submission frequency into the AI, which then analyzes the data to evaluate the submission timing. Based on the evaluated submission timing, the optimization unit determines the optimization priorities.

[0079] The optimization unit can perform optimization by referring to market data for keywords during the optimization process. For example, the optimization unit can select the most effective keywords based on market data. The optimization unit can analyze market data and refer to the keyword strategies of competitors. The optimization unit can select keywords that are effective during specific time periods based on market data. This enables more effective SEO optimization by referring to relevant market data for keywords. Some or all of the above processes in the optimization unit may be performed using AI or not. For example, the optimization unit can input market data for keywords into AI, and the AI ​​can analyze that data to select the most suitable keywords.

[0080] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0081] When analyzing user input, the reception desk can consider the user's past search and browsing history to suggest more personalized keywords and topics. For example, if a user has frequently searched for articles related to "health" in the past, the reception desk will prioritize suggesting new topics and keywords related to "health." Furthermore, if a user frequently visits a specific website, it can suggest relevant keywords based on the content of that website. It can also analyze the trends of keywords the user has searched in the past and suggest keywords that are relevant to the season and current trends. This allows for the suggestion of more relevant keywords and topics based on the user's past behavioral data.

[0082] The generation unit can estimate the user's emotions and adjust the tone and style of the draft based on those emotions. For example, if the user is relaxed, the generation unit can generate a draft in a soft tone. If the user is stressed, the generation unit can generate a concise and to-the-point draft. Furthermore, if the user is excited, the generation unit can generate a draft in an energetic tone. This allows for the delivery of more appropriate content by adjusting the tone and style of the draft according to the user's emotions. Emotion estimation is performed by analyzing the user's facial expression and voice data.

[0083] The optimization unit can optimize keyword placement in the generated draft by referencing the user's past SEO performance data. For example, it can analyze keyword placement patterns that have shown high performance in the past and apply them to the new draft. It can also optimize keyword placement for specific times of day or seasons based on past SEO performance data. Furthermore, it can suggest more effective keyword placement by referencing the SEO performance data of competitors. By optimizing SEO based on past data, higher rankings in search engine results can be expected.

[0084] The reception desk can estimate the user's emotions and adjust the input interface based on those emotions. For example, if the user is relaxed, the reception desk can provide a visually appealing interface. If the user is in a hurry, the reception desk can provide a simple and intuitive interface. Furthermore, if the user is stressed, the reception desk can provide an interface with relaxing colors and designs. By adjusting the input interface according to the user's emotions, a more comfortable user experience can be provided. Emotion estimation is performed by analyzing the user's facial expression data and voice data.

[0085] The generation unit can generate drafts based not only on the user's past writing style but also on their current projects and goals. For example, it can prioritize keywords and topics related to the user's current project. It can also adjust the content and style of the draft based on the user's goals (e.g., appealing to a specific audience or focusing on a specific theme). Furthermore, it can adjust the speed and level of detail of the draft generation based on deadlines and schedules set by the user. This allows for the generation of personalized drafts tailored to the user's current situation and goals.

[0086] The reception desk can estimate the user's emotions and adjust how input suggestions are presented based on those estimates. For example, if the user is relaxed, detailed input suggestions can be presented in a list format. If the user is in a hurry, concise input suggestions can be presented in a pop-up format. Furthermore, if the user is stressed, input suggestions can be presented using visually appealing icons or images. By adjusting how input suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is performed by analyzing the user's facial expression data and voice data.

[0087] The optimization unit can optimize the generated draft for SEO, taking into account the user's current areas of interest and trends. For example, it can prioritize keywords related to topics the user is currently interested in. It can also suggest highly relevant keywords based on current trends and topics. Furthermore, it can place keywords to appeal to specific reader segments based on the user's areas of interest. In this way, by optimizing SEO based on the user's current areas of interest and trends, more effective content can be delivered.

[0088] The generation unit can estimate the user's emotions and adjust the draft content based on those emotions. For example, if the user is relaxed, the generation unit can generate a draft that includes detailed explanations and background information. If the user is in a hurry, the generation unit can generate a concise and to-the-point draft. Furthermore, if the user is excited, the generation unit can generate a draft that includes energetic expressions and visually appealing elements. This allows for the provision of more appropriate content by adjusting the draft content according to the user's emotions. Emotion estimation is performed by analyzing the user's facial expression data and voice data.

[0089] The reception desk can suggest region-specific keywords and topics, taking into account the user's geographical location. For example, if the user is in a specific city, it can suggest events and news related to that city as keywords. If the user is traveling, it can also suggest tourist spots and local specialties related to their destination as keywords. Furthermore, it can suggest keywords related to local trends and topics based on the user's geographical location. This allows for the suggestion of more relevant keywords and topics based on the user's geographical location.

[0090] The optimization unit can estimate the user's emotions and adjust the SEO optimization method based on those emotions. For example, if the user is relaxed, the optimization unit can perform detailed SEO optimization. If the user is in a hurry, the optimization unit can perform concise SEO optimization. Furthermore, if the user is excited, the optimization unit can perform SEO optimization that includes visually appealing elements. This allows for more effective SEO optimization by adjusting the SEO optimization method according to the user's emotions. Emotion estimation is performed by analyzing the user's facial expression data and voice data.

[0091] The following briefly describes the processing flow for example form 2.

[0092] Step 1: The reception desk accepts keyword or topic input. For example, it can accept keywords or topics entered by the user. Furthermore, the reception desk can use AI to analyze the user's input and suggest appropriate keywords or topics. Step 2: The generation unit analyzes the keywords or topics received by the reception unit and generates a draft of relevant content. The generation unit uses generation AI to generate a draft based on the user's input. For example, it can use natural language processing technology to collect information related to the entered keywords and topics and generate a draft. The generation unit can also learn the user's past writing style and generate a personalized draft based on that. Step 3: The optimization unit proposes and places keywords to optimize SEO in the draft generated by the generation unit. The optimization unit can propose appropriate keywords for the draft generated using AI and place them appropriately. For example, it optimizes the draft using keyword selection criteria and optimization methods for placement for SEO.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] For example, the reception unit is implemented by the reception device 38 of the smart device 14, which receives keywords and topics entered by the user. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12, which generates a draft based on the entered keywords and topics. For example, the optimization unit is implemented by the specific processing unit 290 of the data processing device 12, which suggests appropriate keywords for the generated draft and arranges them appropriately. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0097] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives keywords and topics entered by the user. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a draft based on the entered keywords and topics. For example, the optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests appropriate keywords for the generated draft and arranges them appropriately. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0113] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives keywords and topics entered by the user. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a draft based on the entered keywords and topics. For example, the optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests appropriate keywords for the generated draft and arranges them appropriately. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0129] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives keywords and topics entered by the user. For example, the generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates a draft based on the entered keywords and topics. For example, the optimization unit is implemented by the specific processing unit 290 of the data processing device 12 and suggests appropriate keywords for the generated draft and arranges them appropriately. The correspondence between each unit and the device and control unit is not limited to the examples described above and can be changed in various ways.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] (Note 1) A reception area that accepts input of keywords or topics, A generation unit analyzes keywords or topics received by the reception unit and generates a draft of related content. The system includes a unit that suggests and places keywords for SEO in the draft generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The system learns the user's past writing style and generates a draft of content related to keywords or topics received by the reception unit, based on the user's past writing style. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is When entering keywords or topics, suggestions are presented based on the user's areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is The system estimates the user's sentiment and adjusts the timing of keyword or topic input based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When entering keywords or topics, filtering is performed based on the user's projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and prioritizes input suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When users enter keywords or topics, relevant suggestions are prioritized based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When a user enters a keyword or topic, the system analyzes their social media activity and suggests appropriate options. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is We estimate the user's emotions and adjust the way the draft is presented, taking those estimated emotions into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating a draft, adjust the level of detail in the draft based on the importance of the keywords. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating a draft, apply the appropriate generation algorithm according to the topic category. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is Estimate the user's emotions and adjust the length of the draft considering the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating drafts, prioritize the drafts by considering the timing of keyword submissions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating drafts, adjust the order of the drafts based on the relevance of the keywords. The system described in Appendix 1, characterized by the features described herein. (Note 16) The optimization unit, We estimate user sentiment and adjust SEO optimization methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The optimization unit, During optimization, past SEO data is referenced to select appropriate keyword placement. The system described in Appendix 1, characterized by the features described herein. (Note 18) The optimization unit, During optimization, apply the appropriate optimization method for each keyword category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The optimization unit, We estimate user sentiment and prioritize keywords by considering the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The optimization unit, When optimizing, prioritize optimizations by considering the timing of keyword submission. The system described in Appendix 1, characterized by the features described herein. (Note 21) The optimization unit, During optimization, market data for keywords is referenced. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0165] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts input of keywords or topics, A generation unit analyzes keywords or topics received by the reception unit and generates a draft of related content. The system includes a unit that suggests and places keywords for SEO in the draft generated by the generation unit. A system characterized by the following features.

2. The generating unit is The system learns the user's past writing style and generates a draft of content related to keywords or topics received by the reception unit, which is generated based on the user's past writing style. The system according to feature 1.

3. The aforementioned reception unit is When entering keywords or topics, suggestions are presented based on the user's areas of interest. The system according to feature 1.

4. The aforementioned reception unit is The system estimates the user's sentiment and adjusts the timing of keyword or topic input based on that estimated sentiment. The system according to feature 1.

5. The aforementioned reception unit is Analyze the user's past input history and select the appropriate input method. The system according to feature 1.

6. The aforementioned reception unit is When entering keywords or topics, filtering is performed based on the user's projects and areas of interest. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and prioritizes input suggestions based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is When users enter keywords or topics, relevant suggestions are prioritized based on their geographical location. The system according to feature 1.

9. The aforementioned reception unit is When a user enters a keyword or topic, the system analyzes their social media activity and suggests appropriate options. The system according to feature 1.

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