system

Abstract

The system according to this embodiment aims to efficiently support a company's content marketing. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a provision unit, an editing unit, and a proposal unit. The reception unit receives user input. The generation unit generates content based on the information received by the reception unit. The provision unit provides the content generated by the generation unit to the user. The editing unit edits the content generated by the generation unit. The proposal unit proposes topics based on the content 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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there has been a problem that it is difficult to efficiently generate high-quality content in corporate content marketing.

[0005] The system according to the embodiment aims to efficiently support corporate content marketing.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a provision unit, an editing unit, and a proposal unit. The reception unit receives user input. The generation unit generates content based on the information received by the reception unit. The provision unit provides the content generated by the generation unit to the user. The editing unit edits the content generated by the generation unit. The proposal unit proposes topics based on the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently support a company's content marketing. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The text generation agent according to an embodiment of the present invention is a system for supporting corporate content marketing. This system provides an agent that automatically generates content such as blogs, social media posts, and product descriptions. The target audience includes companies engaged in content marketing, advertising agencies, freelance marketers, and bloggers. The challenges faced by these target audiences include the time and cost involved in regularly creating high-quality content, the depletion of creative ideas, and the need to provide content in a timely manner. For example, companies need to update their blog posts regularly, which requires a lot of time and resources. Advertising agencies also need to create a lot of content for their clients and are required to keep their ideas constantly fresh. Similarly, freelance marketers and bloggers are required to create high-quality content with limited resources. As a solution to the problems of the present invention, AI can automatically generate high-quality content, thereby improving the efficiency of content creation, reducing creation costs, and maintaining the freshness of ideas. Specifically, it uses generative AI (e.g., natural language generation technology) to automatically generate new articles and posts based on user input and past content. It also includes topic suggestions and editing support. For example, when a user inputs the type of content or topic they want to generate, the generative AI analyzes the input information and past content to generate new content. The generated content is provided to the user, who can edit and modify it as needed. This allows users to quickly create high-quality content. This system simplifies and streamlines the content creation process, enabling any company to quickly produce high-quality content and maximize its marketing effectiveness. This allows text generation agents to support companies' content marketing efforts and efficiently generate high-quality content.

[0029] The text generation agent according to this embodiment comprises a reception unit, a generation unit, a provision unit, an editing unit, and a suggestion unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives, for example, text data entered by the user. The reception unit can also receive voice input and convert it into text data using voice recognition technology. Furthermore, the reception unit can also receive image input and convert it into text data using image recognition technology. For example, the reception unit analyzes image data entered by the user and extracts text within the image. The generation unit generates content based on the information received by the reception unit using a generation AI. The generation unit generates new content by analyzing, for example, text data entered by the user. The generation unit can also generate new content by analyzing user input and past content using a generation AI. For example, the generation unit inputs user input data into the generation AI, and the generation AI generates new content. The generation unit can also suggest relevant topics based on user input data using a generation AI. The Provider department provides the content generated by the Generator department to users. For example, the Provider department provides the generated content to users through a website or application. The Provider department can also send the generated content to users by email. For example, the Provider department sends the generated content to the user's email address. The Provider department can also print the generated content and provide it to users. For example, the Provider department prints the generated content on a printer and provides it to the user. The Editorial department edits the content generated by the Generator department. For example, the Editorial department modifies the content of the generated content. The Editorial department can also add new information to the generated content. For example, the Editorial department adds a new paragraph to the generated content. The Editorial department can also delete unnecessary parts of the generated content. For example, the Editorial department deletes redundant parts of the generated content. The Proposal department proposes topics based on the content generated by the Generator department.The suggestion unit, for example, analyzes the content of the generated content and suggests related topics. The suggestion unit can also suggest new topics based on the content of the generated content. For example, the suggestion unit extracts keywords from the generated content and suggests related topics. The suggestion unit can also provide the user with new ideas based on the content of the generated content. For example, the suggestion unit suggests new ideas related to the theme of the generated content. As a result, the text generation agent according to the embodiment can generate, provide, edit, and suggest content based on user input.

[0030] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk receives text data entered by the user. The reception desk can also receive voice input and convert it into text data using speech recognition technology. Specifically, the speech recognition technology used is one that analyzes the speech waveform and breaks it down into phonemes and words. This allows for accurate conversion of what the user says into text data. Furthermore, the reception desk can also receive image input and convert it into text data using image recognition technology. For example, the reception desk analyzes image data entered by the user and extracts text from the image. Optical character recognition (OCR) technology is used as the image recognition technology to detect characters in the image and convert them into text data. This allows the content to be used as text data even if the user inputs handwritten notes or printed documents as images. By supporting these diverse input methods, the reception desk can improve user convenience and accommodate various situations. For example, if the user is on the move and their hands are busy, they can use voice input, and if they are in a quiet environment, they can use text input. Furthermore, by utilizing image input, information from paper documents can be digitized and managed efficiently. This allows the reception department to provide flexible input methods that meet the diverse needs of users, improving the overall usability of the system.

[0031] The generation unit uses generative AI to generate content based on information received by the reception unit. For example, the generation unit analyzes text data entered by a user and generates new content. The generation unit can also use generative AI to analyze user input and past content and generate new content. Specifically, the generative AI utilizes natural language processing technology to understand user input and generate new text in an appropriate context and style. For example, the generation unit inputs user data into the generative AI, and the generative AI generates new content. The generative AI has been trained on a large amount of text data in advance and excels at understanding grammar, vocabulary, and context, so it can generate natural and fluent sentences. The generation unit can also use the generative AI to suggest relevant topics based on user input data. For example, if the text entered by the user is about travel, the generative AI can generate information about recommended spots in the travel destination and travel preparations. Furthermore, the generation unit can analyze the user's past input data and generated content to generate personalized content based on the user's preferences and interests. This allows the generation unit to quickly generate and deliver high-quality content that meets the user's needs.

[0032] The delivery unit provides users with content generated by the generation unit. For example, the delivery unit provides the generated content to users through websites and applications. Specifically, the delivery unit displays the generated content on a webpage, making it accessible to users. The delivery unit can also send the generated content to users via email. For example, the delivery unit sends the generated content to the user's email address. This allows the user to receive the generated content via email and review it at any time. Furthermore, the delivery unit can provide users with printed copies of the generated content. For example, the delivery unit prints the generated content using a printer and provides it to the user. This allows the user to receive the generated content in paper form and use it offline. By supporting these diverse delivery methods, the delivery unit can improve user convenience and accommodate various situations. For example, users can view content via a smartphone application while on the go and use a website on their computer at home. They can also access content in environments without internet access by using email or printed materials. This allows the delivery unit to provide flexible delivery methods to meet diverse user needs and improve the overall usability of the system.

[0033] The editorial team edits the content generated by the generation team. For example, the editorial team revises the content of the generated content. Specifically, the editorial team corrects grammatical and spelling errors and improves the flow of the writing. The editorial team can also add new information to the generated content. For example, the editorial team can add new paragraphs to enrich the content. Furthermore, the editorial team can delete unnecessary parts of the generated content. For example, the editorial team can remove redundant parts to make the content easier to read. Through these editing tasks, the editorial team can improve the quality of the generated content. The editorial team can also improve the generated content based on user feedback. For example, they can review the content and expression based on comments and ratings from users. In addition, the editorial team can apply appropriate styles and formats according to the theme and topic of the generated content. This allows the editorial team to optimize the generated content to meet user needs and provide higher quality content.

[0034] The suggestion unit proposes topics based on the content generated by the generation unit. For example, the suggestion unit analyzes the content of the generated content and proposes related topics. Specifically, the suggestion unit extracts keywords from the generated content and proposes topics related to them. For example, if the generated content is about health, the suggestion unit can propose topics related to exercise, diet, and sleep. Furthermore, the suggestion unit can also propose new topics based on the content of the generated content. For example, the suggestion unit can propose new ideas related to the theme of the generated content. This allows users to gain new perspectives and ideas based on the generated content. The suggestion unit can also analyze the user's past input data and generated content to propose topics based on the user's interests and concerns. This allows the suggestion unit to provide personalized suggestions that meet the user's needs and improve user satisfaction.

[0035] The generation unit can evaluate the quality of the content using a generation AI. For example, the generation unit can evaluate the quality of the generated content using a generation AI. The generation unit can also use a generation AI to evaluate the grammar and structure of the generated content. For example, the generation unit inputs the generated content into a generation AI, and the generation AI evaluates the grammar and structure. The generation unit can also use a generation AI to evaluate the consistency of the content of the generated content. For example, the generation unit inputs the generated content into a generation AI, and the generation AI evaluates the consistency of the content. The generation unit can also use a generation AI to evaluate the accuracy of the information in the generated content. For example, the generation unit inputs the generated content into a generation AI, and the generation AI evaluates the accuracy of the information. By evaluating the quality of the content using a generation AI, high-quality content can be provided. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit inputs the generated content into a generation AI, and the generation AI performs a quality evaluation.

[0036] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also suggest relevant input methods based on the content the user has previously entered. For example, the reception desk can analyze the content the user has previously entered and suggest relevant input methods. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest input methods that the user frequently uses during specific time periods. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can suggest the optimal input method.

[0037] The reception desk can provide real-time feedback to the user based on the input content. For example, the reception desk can immediately display a confirmation message for the content entered by the user. The reception desk can also provide relevant information in real time based on the content entered by the user. For example, the reception desk can immediately display information related to the content entered by the user. The reception desk can also immediately suggest corrections to the content entered by the user. For example, the reception desk can point out areas that need correction in the content entered by the user. This improves user convenience by providing real-time feedback based on the input content. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's input content into the AI, and the AI ​​provides real-time feedback.

[0038] The reception desk can automatically suggest relevant topics based on the user's geographical location. For example, if the user is in a specific region, the reception desk will suggest topics related to that region. If the user is traveling, the reception desk can also suggest topics related to their travel destination. For example, if the user is traveling, the reception desk will suggest tourist information and event information for their destination. If the user is participating in a specific event, the reception desk can also suggest topics related to that event. For example, the reception desk will suggest the latest information and related news about the event the user is participating in. This allows for the provision of content tailored to the user's interests by suggesting relevant topics based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk inputs the user's geographical location into the AI, and the AI ​​suggests relevant topics.

[0039] The reception desk can analyze a user's social media activity and automatically complete relevant input content. For example, the reception desk can suggest relevant input content based on what the user has shared on social media. The reception desk can also suggest relevant input content based on posts from accounts the user follows on social media. For example, the reception desk can suggest relevant topics based on the latest posts from accounts the user follows. The reception desk can also suggest relevant input content based on the activities of groups the user participates in on social media. For example, the reception desk can suggest relevant topics based on the discussions of groups the user participates in. In this way, by analyzing the user's social media activity, relevant input content can be automatically completed. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's social media activity data into AI, and the AI ​​suggests relevant input content.

[0040] The generation unit can learn the style of the user's past content during generation and generate consistent content. For example, the generation unit can learn the style of blog posts the user has created in the past and generate new articles in a similar style. The generation unit can also learn the content of social media posts the user has made in the past and generate consistent posts. For example, the generation unit can generate new posts based on content the user has posted in the past. The generation unit can also learn the style of product descriptions the user has created in the past and generate new descriptions in a similar style. For example, the generation unit can generate new product descriptions based on product descriptions the user has created in the past. In this way, consistent content can be generated by learning the style of the user's past content. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's past content data into a generation AI, and the generation AI generates consistent content.

[0041] The generation unit can adjust the content during generation to emphasize specific keywords or phrases. For example, the generation unit adjusts the structure of the text to emphasize keywords specified by the user. The generation unit can also adjust the content to repeatedly use phrases that the user considers important. For example, the generation unit uses a user-specified phrase multiple times. The generation unit can also adjust the content to include keywords specified by the user in the title or headings. For example, the generation unit includes keywords specified by the user in the title. This allows for the generation of content that aligns with the user's intent by emphasizing specific keywords or phrases. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit inputs keywords specified by the user into a generation AI, and the generation AI generates content to emphasize those keywords.

[0042] The generation unit can automatically incorporate the user's industry-specific terminology during generation. For example, the generation unit can learn the specialized terminology of the user's industry and incorporate it into the content. The generation unit can also adjust the sentence structure to emphasize industry-specific terminology specified by the user. For example, the generation unit can use the industry terminology specified by the user multiple times. The generation unit can also generate new content based on industry-specific terminology previously used by the user. For example, the generation unit can learn the industry terminology previously used by the user and incorporate it into new content. This allows for the generation of more specialized content by incorporating the user's industry-specific terminology. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's industry-specific terminology data into a generation AI, and the generation AI generates content incorporating the industry terminology.

[0043] The generation unit can adjust the content format to match the user's target audience during generation. For example, if the user's target audience is young people, the generation unit will generate content in a casual format. If the user's target audience is business professionals, the generation unit can also generate content in a formal format. For example, if the user's target audience is business professionals, the generation unit will generate formal text using a lot of technical jargon. If the user's target audience is international, the generation unit can also generate content in a multilingual format. For example, if the user's target audience is international, the generation unit will generate content in multiple languages. This allows for the provision of more effective content by adjusting the content format to match the user's target audience. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit inputs the user's target audience information into the generation AI, and the generation AI generates content in an appropriate format.

[0044] The content delivery unit can select the optimal delivery timing by referring to the user's past browsing history at the time of delivery. For example, if the user has previously viewed content during a specific time period, the delivery unit will deliver the content according to that time period. The delivery unit can also deliver content according to a specific day of the week if the user has previously viewed content on that day of the week. For example, if the user has frequently viewed content on a specific day of the week in the past, the delivery unit will deliver the content according to that day of the week. The delivery unit can also predict the optimal delivery timing from the user's past browsing history and deliver the content accordingly. For example, the delivery unit analyzes the user's past browsing history and selects the optimal delivery timing. This allows the content to be delivered at the optimal time by referring to the user's past browsing history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit inputs the user's past browsing history data into AI, and the AI ​​selects the optimal delivery timing.

[0045] The service provider can collect user feedback in real time at the time of delivery and reflect it in the next delivery. For example, the service provider can enable users to provide real-time feedback on the delivered content. The service provider can also reflect improvements in the next content delivery based on user feedback. For example, the service provider collects user feedback and reflects it in the next content delivery. The service provider can also collect user feedback in real time and respond immediately. For example, the service provider collects user feedback in real time and makes corrections immediately. This allows the service provider to reflect user feedback in the next delivery by collecting it in real time. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs user feedback data into AI, and the AI ​​reflects it in the next delivery.

[0046] The service provider can select the optimal display format based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider will provide a display format that matches the screen size. If the user is using a tablet, the service provider can also provide a display format optimized for a larger screen. For example, if the user is using a tablet, the service provider will provide a display format that matches the screen size. If the user is using a smartwatch, the service provider can also provide a concise and highly visible display format. For example, if the user is using a smartwatch, the service provider will provide a format that displays concise information. By selecting the optimal display format based on the user's device information, user convenience is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider inputs the user's device information into the AI, and the AI ​​selects the optimal display format.

[0047] The service provider can analyze the user's social media activity at the time of delivery and prioritize providing relevant content. For example, the service provider can provide relevant content based on what the user has shared on social media. The service provider can also provide relevant content based on the posts of accounts the user follows on social media. For example, the service provider can provide relevant content based on the latest posts of accounts the user follows. The service provider can also provide relevant content based on the activities of groups the user participates in on social media. For example, the service provider can provide relevant content based on the discussions of groups the user participates in. This allows the service provider to prioritize providing relevant content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can provide relevant content.

[0048] The editorial team can suggest the optimal editing method by referring to the user's past editing history during editing. For example, the editorial team can suggest a similar editing method based on the user's past editing history. The editorial team can also prioritize suggesting editing tools that the user has used in the past. For example, the editorial team can suggest a similar tool based on the user's past editing history. The editorial team can also predict and suggest specific editing patterns from the user's past editing history. For example, the editorial team can analyze the user's past editing history and suggest specific editing patterns. This allows the editorial team to suggest the optimal editing method by referring to the user's past editing history. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input the user's past editing history data into AI, and the AI ​​can suggest the optimal editing method.

[0049] The editorial team can adjust the edited content to emphasize specific keywords or phrases during the editing process. For example, the editorial team can adjust the structure of the text to emphasize keywords specified by the user. The editorial team can also adjust the edited content to repeatedly use phrases that the user considers important. For example, the editorial team can use a phrase specified by the user multiple times. The editorial team can also adjust the edited content to include keywords specified by the user in the title or headings. For example, the editorial team can include keywords specified by the user in the title. This allows for editing that aligns with the user's intent by emphasizing specific keywords or phrases. Some or all of the above processes performed by the editorial team may be carried out using AI, for example, or not. For example, the editorial team can input keywords specified by the user into the AI, and the AI ​​can adjust the edited content to emphasize those keywords.

[0050] The editorial team can automatically incorporate industry-specific terminology from the user's industry during the editing process. For example, the editorial team can learn the specialized terminology of the user's industry and incorporate it into the edited content. The editorial team can also adjust the structure of the text to emphasize industry-specific terminology specified by the user. For example, the editorial team can use the industry terminology specified by the user multiple times. The editorial team can also generate new edited content based on industry-specific terminology previously used by the user. For example, the editorial team can learn the industry terminology previously used by the user and incorporate it into new edited content. This allows for more specialized editing by incorporating the user's industry-specific terminology. Some or all of the above processes in the editorial team may be performed using AI, or not. For example, the editorial team can input the user's industry-specific terminology data into an AI, which can then generate edited content incorporating the industry terminology.

[0051] The editorial team can adjust the content to suit the user's target audience during the editing process. For example, if the target audience is young people, the editorial team can provide casual content. If the target audience is business professionals, the editorial team can also provide formal content. For example, if the target audience is business professionals, the editorial team can provide formal writing that uses a lot of technical jargon. If the target audience is international, the editorial team can also provide multilingual content. For example, if the target audience is international, the editorial team can provide content in multiple languages. By adjusting the content to suit the user's target audience, more effective editing becomes possible. Some or all of the above processes in the editorial team may be performed using AI, or not. For example, the editorial team can input the user's target audience information into AI, and the AI ​​can provide appropriate content.

[0052] The suggestion unit can analyze the user's past content trends and propose the most suitable topic when making a suggestion. For example, the suggestion unit can analyze the trends of content the user has created in the past and propose similar topics. The suggestion unit can also analyze the content of social media posts the user has made in the past and propose relevant topics. For example, the suggestion unit can propose relevant topics based on content the user has posted in the past. The suggestion unit can also analyze the trends of product descriptions the user has created in the past and propose relevant topics. For example, the suggestion unit can propose relevant topics based on product descriptions the user has created in the past. In this way, the suggestion unit can propose the most suitable topic by analyzing the user's past content trends. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past content data into AI, and the AI ​​can propose the most suitable topic.

[0053] The suggestion function can prioritize suggesting topics that contain specific keywords or phrases. For example, it can prioritize suggesting topics that contain keywords specified by the user. The suggestion function can also prioritize suggesting topics that contain phrases that the user considers important. For example, it can prioritize suggesting topics that contain phrases specified by the user. The suggestion function can also prioritize suggesting topics that contain keywords specified by the user in their titles or headings. For example, it can suggest topics that contain keywords specified by the user in their titles. By prioritizing the suggestion of topics that contain specific keywords or phrases, the suggestion function can suggest topics that align with the user's intent. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function inputs keywords specified by the user into the AI, and the AI ​​suggests topics that contain those keywords.

[0054] The suggestion unit can suggest relevant topics based on the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will suggest topics related to that region. If the user is traveling, the suggestion unit can also suggest topics related to their travel destination. For example, if the user is traveling, the suggestion unit will suggest tourist information and event information for their destination. If the user is participating in a specific event, the suggestion unit can also suggest topics related to that event. For example, the suggestion unit will suggest the latest information and related news about the event the user is participating in. In this way, by suggesting relevant topics based on the user's geographical location information, topics tailored to the user's interests can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit inputs the user's geographical location information into the AI, and the AI ​​suggests relevant topics.

[0055] The suggestion unit can analyze the user's social media activity and prioritize suggesting relevant topics when making suggestions. For example, the suggestion unit can suggest relevant topics based on what the user has shared on social media. The suggestion unit can also suggest relevant topics based on the content of posts from accounts the user follows on social media. For example, the suggestion unit can suggest relevant topics based on the latest posts from accounts the user follows. The suggestion unit can also suggest relevant topics based on the activities of groups the user participates in on social media. For example, the suggestion unit can suggest relevant topics based on the discussions in groups the user participates in. This allows the suggestion unit to prioritize suggesting relevant topics by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into AI, and the AI ​​can suggest relevant topics.

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

[0057] The reception desk can automatically search for and provide relevant past content based on user input. For example, if a user inputs information about a specific topic, the reception desk will search for and present relevant content generated in the past. This allows users to create new content while referring to past content. The reception desk can also search for and provide relevant external information sources based on user input. For example, if a user inputs information about a specific topic, the reception desk will search for relevant articles and data on the internet and present them to the user. This allows users to create content while referring to external information. Furthermore, the reception desk can also search for and provide relevant images and videos based on user input. For example, if a user inputs information about a specific topic, the reception desk will search for and present relevant images and videos to the user. This allows users to create content that incorporates visual elements.

[0058] The generation unit can automatically perform SEO (Search Engine Optimization) on the generated content. For example, the generation unit inserts appropriate keywords into the generated content to improve its ranking in search engines. This increases the likelihood that the generated content will be viewed by more users. The generation unit can also automatically generate metadata (title, description, tags, etc.) for the generated content to enhance SEO. For example, the generation unit generates appropriate titles and descriptions for the generated content to improve its ranking in search engines. Furthermore, the generation unit can automatically set up internal links within the generated content to enhance SEO. For example, the generation unit inserts links to other related content within the generated content to encourage users to navigate the site. This improves the SEO effectiveness of the generated content and increases the likelihood that it will be viewed by more users.

[0059] The input system can analyze a user's past input history and suggest optimal input completion. For example, it can prioritize suggesting phrases and words that the user has frequently used in the past. This allows the user to streamline their input process. The input system can also suggest relevant input completion based on the user's past input. For example, it can analyze the user's past input and suggest related phrases and words. This allows the user to make new inputs while referring to past input. Furthermore, the input system can suggest contextual input completion based on the user's past input history. For example, if the user is typing about a specific topic, it can suggest phrases and words related to that topic. This allows the user to receive contextually appropriate input completion.

[0060] The reception desk can provide users with appropriate guidelines and templates based on their input. For example, if a user is creating a blog post, the reception desk will provide guidelines on the structure and writing style of the blog post. This allows the user to create the blog post efficiently. The reception desk can also provide templates suitable for content in a specific format if the user is creating content in that format. For example, if a user is creating a product description, the reception desk will provide a product description template. This allows the user to create content efficiently using the template. Furthermore, the reception desk can provide relevant reference materials and samples based on the user's input. For example, if a user is inputting information about a specific topic, the reception desk will provide reference materials and samples related to that topic. This allows the user to create content using the reference materials and samples.

[0061] The reception desk can automatically suggest relevant local events and news based on the user's geographical location. For example, if the user is in a specific region, it can suggest events and the latest news happening in that region. This allows the user to easily access local information. The reception desk can also suggest tourist information and event information related to the user's travel destination if the user is traveling. For example, if the user is traveling, it can suggest tourist attractions and event information in the destination. This allows the user to easily access information about their travel destination. Furthermore, if the reception desk is participating in a specific event, it can suggest the latest information and related news related to that event. For example, it can suggest the latest information and related news about the event the user is attending. This allows the user to easily access information related to the event.

[0062] The reception desk can analyze a user's social media activity and automatically suggest relevant topics and hashtags. For example, it can suggest relevant topics and hashtags based on what the user has shared on social media. This allows users to post more effectively on social media. The reception desk can also suggest relevant topics and hashtags based on the content of accounts the user follows. For example, it can suggest relevant topics and hashtags based on the latest posts from accounts the user follows. This allows users to post content that is highly relevant to the accounts they follow. Furthermore, the reception desk can suggest relevant topics and hashtags based on the activities of groups the user participates in. For example, it can suggest relevant topics and hashtags based on the discussions in groups the user participates in. This allows users to post content that is highly relevant to the group's activities.

[0063] The generation unit can analyze the performance data of a user's past content during the generation process to generate optimal content. For example, it can analyze the performance data of blog posts a user has previously created and generate new articles with similar performance. This allows users to create content based on past successes. The generation unit can also analyze the performance data of social media posts a user has previously made and generate consistent posts. For example, it can generate new posts based on the performance data of content a user has previously posted. This allows users to create social media posts based on past successes. Furthermore, the generation unit can analyze the performance data of product descriptions a user has previously created and generate new descriptions with similar performance. For example, it can generate new product descriptions based on the performance data of product descriptions a user has previously created. This allows users to create product descriptions based on past successes.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk receives text data entered by the user. It can also receive voice input and convert it into text data using speech recognition technology. Furthermore, it can receive image input and convert it into text data using image recognition technology. Step 2: The generation unit uses a generation AI to generate content based on the information received by the reception unit. For example, it analyzes text data entered by the user and generates new content. The generation unit can also use the generation AI to analyze user input and past content and generate new content. Step 3: The provider delivers the content generated by the generator to the user. For example, the provider delivers the generated content to the user through a website or application. The provider can also send the generated content to the user via email. Furthermore, the provider can print the generated content and deliver it to the user. Step 4: The editorial team edits the content generated by the generation team. For example, they may revise the content, add new information, or delete unnecessary parts. Step 5: The suggestion unit proposes topics based on the content generated by the generation unit. For example, it analyzes the content of the generated material and proposes relevant topics or new ideas.

[0066] (Example of form 2) The text generation agent according to an embodiment of the present invention is a system for supporting corporate content marketing. This system provides an agent that automatically generates content such as blogs, social media posts, and product descriptions. The target audience includes companies engaged in content marketing, advertising agencies, freelance marketers, and bloggers. The challenges faced by these target audiences include the time and cost involved in regularly creating high-quality content, the depletion of creative ideas, and the need to provide content in a timely manner. For example, companies need to update their blog posts regularly, which requires a lot of time and resources. Advertising agencies also need to create a lot of content for their clients and are required to keep their ideas constantly fresh. Similarly, freelance marketers and bloggers are required to create high-quality content with limited resources. As a solution to the problems of the present invention, AI can automatically generate high-quality content, thereby improving the efficiency of content creation, reducing creation costs, and maintaining the freshness of ideas. Specifically, it uses generative AI (e.g., natural language generation technology) to automatically generate new articles and posts based on user input and past content. It also includes topic suggestions and editing support. For example, when a user inputs the type of content or topic they want to generate, the generative AI analyzes the input information and past content to generate new content. The generated content is provided to the user, who can edit and modify it as needed. This allows users to quickly create high-quality content. This system simplifies and streamlines the content creation process, enabling any company to quickly produce high-quality content and maximize its marketing effectiveness. This allows text generation agents to support companies' content marketing efforts and efficiently generate high-quality content.

[0067] The text generation agent according to this embodiment comprises a reception unit, a generation unit, a provision unit, an editing unit, and a suggestion unit. The reception unit receives user input. User input includes, but is not limited to, text input, voice input, and image input. The reception unit receives, for example, text data entered by the user. The reception unit can also receive voice input and convert it into text data using voice recognition technology. Furthermore, the reception unit can also receive image input and convert it into text data using image recognition technology. For example, the reception unit analyzes image data entered by the user and extracts text within the image. The generation unit generates content based on the information received by the reception unit using a generation AI. The generation unit generates new content by analyzing, for example, text data entered by the user. The generation unit can also generate new content by analyzing user input and past content using a generation AI. For example, the generation unit inputs user input data into the generation AI, and the generation AI generates new content. The generation unit can also suggest relevant topics based on user input data using a generation AI. The Provider department provides the content generated by the Generator department to users. For example, the Provider department provides the generated content to users through a website or application. The Provider department can also send the generated content to users by email. For example, the Provider department sends the generated content to the user's email address. The Provider department can also print the generated content and provide it to users. For example, the Provider department prints the generated content on a printer and provides it to the user. The Editorial department edits the content generated by the Generator department. For example, the Editorial department modifies the content of the generated content. The Editorial department can also add new information to the generated content. For example, the Editorial department adds a new paragraph to the generated content. The Editorial department can also delete unnecessary parts of the generated content. For example, the Editorial department deletes redundant parts of the generated content. The Proposal department proposes topics based on the content generated by the Generator department.The suggestion unit, for example, analyzes the content of the generated content and suggests related topics. The suggestion unit can also suggest new topics based on the content of the generated content. For example, the suggestion unit extracts keywords from the generated content and suggests related topics. The suggestion unit can also provide the user with new ideas based on the content of the generated content. For example, the suggestion unit suggests new ideas related to the theme of the generated content. As a result, the text generation agent according to the embodiment can generate, provide, edit, and suggest content based on user input.

[0068] The reception desk receives user input. User input includes, but is not limited to, text input, voice input, and image input. For example, the reception desk receives text data entered by the user. The reception desk can also receive voice input and convert it into text data using speech recognition technology. Specifically, the speech recognition technology used is one that analyzes the speech waveform and breaks it down into phonemes and words. This allows for accurate conversion of what the user says into text data. Furthermore, the reception desk can also receive image input and convert it into text data using image recognition technology. For example, the reception desk analyzes image data entered by the user and extracts text from the image. Optical character recognition (OCR) technology is used as the image recognition technology to detect characters in the image and convert them into text data. This allows the content to be used as text data even if the user inputs handwritten notes or printed documents as images. By supporting these diverse input methods, the reception desk can improve user convenience and accommodate various situations. For example, if the user is on the move and their hands are busy, they can use voice input, and if they are in a quiet environment, they can use text input. Furthermore, by utilizing image input, information from paper documents can be digitized and managed efficiently. This allows the reception department to provide flexible input methods that meet the diverse needs of users, improving the overall usability of the system.

[0069] The generation unit uses generative AI to generate content based on information received by the reception unit. For example, the generation unit analyzes text data entered by a user and generates new content. The generation unit can also use generative AI to analyze user input and past content and generate new content. Specifically, the generative AI utilizes natural language processing technology to understand user input and generate new text in an appropriate context and style. For example, the generation unit inputs user data into the generative AI, and the generative AI generates new content. The generative AI has been trained on a large amount of text data in advance and excels at understanding grammar, vocabulary, and context, so it can generate natural and fluent sentences. The generation unit can also use the generative AI to suggest relevant topics based on user input data. For example, if the text entered by the user is about travel, the generative AI can generate information about recommended spots in the travel destination and travel preparations. Furthermore, the generation unit can analyze the user's past input data and generated content to generate personalized content based on the user's preferences and interests. This allows the generation unit to quickly generate and deliver high-quality content that meets the user's needs.

[0070] The delivery unit provides users with content generated by the generation unit. For example, the delivery unit provides the generated content to users through websites and applications. Specifically, the delivery unit displays the generated content on a webpage, making it accessible to users. The delivery unit can also send the generated content to users via email. For example, the delivery unit sends the generated content to the user's email address. This allows the user to receive the generated content via email and review it at any time. Furthermore, the delivery unit can provide users with printed copies of the generated content. For example, the delivery unit prints the generated content using a printer and provides it to the user. This allows the user to receive the generated content in paper form and use it offline. By supporting these diverse delivery methods, the delivery unit can improve user convenience and accommodate various situations. For example, users can view content via a smartphone application while on the go and use a website on their computer at home. They can also access content in environments without internet access by using email or printed materials. This allows the delivery unit to provide flexible delivery methods to meet diverse user needs and improve the overall usability of the system.

[0071] The editorial team edits the content generated by the generation team. For example, the editorial team revises the content of the generated content. Specifically, the editorial team corrects grammatical and spelling errors and improves the flow of the writing. The editorial team can also add new information to the generated content. For example, the editorial team can add new paragraphs to enrich the content. Furthermore, the editorial team can delete unnecessary parts of the generated content. For example, the editorial team can remove redundant parts to make the content easier to read. Through these editing tasks, the editorial team can improve the quality of the generated content. The editorial team can also improve the generated content based on user feedback. For example, they can review the content and expression based on comments and ratings from users. In addition, the editorial team can apply appropriate styles and formats according to the theme and topic of the generated content. This allows the editorial team to optimize the generated content to meet user needs and provide higher quality content.

[0072] The suggestion unit proposes topics based on the content generated by the generation unit. For example, the suggestion unit analyzes the content of the generated content and proposes related topics. Specifically, the suggestion unit extracts keywords from the generated content and proposes topics related to them. For example, if the generated content is about health, the suggestion unit can propose topics related to exercise, diet, and sleep. Furthermore, the suggestion unit can also propose new topics based on the content of the generated content. For example, the suggestion unit can propose new ideas related to the theme of the generated content. This allows users to gain new perspectives and ideas based on the generated content. The suggestion unit can also analyze the user's past input data and generated content to propose topics based on the user's interests and concerns. This allows the suggestion unit to provide personalized suggestions that meet the user's needs and improve user satisfaction.

[0073] The generation unit can evaluate the quality of the content using a generation AI. For example, the generation unit can evaluate the quality of the generated content using a generation AI. The generation unit can also use a generation AI to evaluate the grammar and structure of the generated content. For example, the generation unit inputs the generated content into a generation AI, and the generation AI evaluates the grammar and structure. The generation unit can also use a generation AI to evaluate the consistency of the content of the generated content. For example, the generation unit inputs the generated content into a generation AI, and the generation AI evaluates the consistency of the content. The generation unit can also use a generation AI to evaluate the accuracy of the information in the generated content. For example, the generation unit inputs the generated content into a generation AI, and the generation AI evaluates the accuracy of the information. By evaluating the quality of the content using a generation AI, high-quality content can be provided. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may not be performed using a generation AI. For example, the generation unit inputs the generated content into a generation AI, and the generation AI performs a quality evaluation.

[0074] The reception desk can estimate the user's emotions and prioritize input based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize important input and provide quick feedback. If the user is relaxed, the reception desk can also prioritize detailed input and provide careful feedback. For example, if the user is relaxed, the reception desk can provide feedback that includes detailed explanations. If the user is in a hurry, the reception desk can also prioritize concise input and provide quick results. For example, if the user is in a hurry, the reception desk can provide concise feedback. This allows for more appropriate feedback by prioritizing input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the reception desk may be performed using, for example, generative AI, or not using generative AI. For example, the reception desk inputs user data into a generating AI, which then estimates the user's emotions.

[0075] The reception desk can analyze the user's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. The reception desk can also suggest relevant input methods based on the content the user has previously entered. For example, the reception desk can analyze the content the user has previously entered and suggest relevant input methods. The reception desk can also predict and suggest input methods to be used during specific time periods based on the user's past input history. For example, the reception desk can suggest input methods that the user frequently uses during specific time periods. In this way, the optimal input method can be suggested by analyzing the user's past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input history data into AI, and the AI ​​can suggest the optimal input method.

[0076] The reception desk can provide real-time feedback to the user based on the input content. For example, the reception desk can immediately display a confirmation message for the content entered by the user. The reception desk can also provide relevant information in real time based on the content entered by the user. For example, the reception desk can immediately display information related to the content entered by the user. The reception desk can also immediately suggest corrections to the content entered by the user. For example, the reception desk can point out areas that need correction in the content entered by the user. This improves user convenience by providing real-time feedback based on the input content. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk inputs the user's input content into the AI, and the AI ​​provides real-time feedback.

[0077] The reception unit can estimate the user's emotions and adjust the design of the input interface based on the estimated emotions. For example, if the user is tense, the reception unit can provide an interface with calming colors to reduce visual stress. If the user is having fun, the reception unit can provide an interface with bright colors to make the input process more enjoyable. For example, if the user is having fun, the reception unit can provide a colorful interface. If the user is tired, the reception unit can provide a simple and highly visible interface to make the input process easier. For example, if the user is tired, the reception unit can provide an interface with large, easy-to-read text. In this way, user stress can be reduced by adjusting the design of the input interface based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit inputs the user's emotion data into a generative AI, and the generative AI estimates the emotion.

[0078] The reception desk can automatically suggest relevant topics based on the user's geographical location. For example, if the user is in a specific region, the reception desk will suggest topics related to that region. If the user is traveling, the reception desk can also suggest topics related to their travel destination. For example, if the user is traveling, the reception desk will suggest tourist information and event information for their destination. If the user is participating in a specific event, the reception desk can also suggest topics related to that event. For example, the reception desk will suggest the latest information and related news about the event the user is participating in. This allows for the provision of content tailored to the user's interests by suggesting relevant topics based on the user's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk inputs the user's geographical location into the AI, and the AI ​​suggests relevant topics.

[0079] The reception desk can analyze a user's social media activity and automatically complete relevant input content. For example, the reception desk can suggest relevant input content based on what the user has shared on social media. The reception desk can also suggest relevant input content based on posts from accounts the user follows on social media. For example, the reception desk can suggest relevant topics based on the latest posts from accounts the user follows. The reception desk can also suggest relevant input content based on the activities of groups the user participates in on social media. For example, the reception desk can suggest relevant topics based on the discussions of groups the user participates in. In this way, by analyzing the user's social media activity, relevant input content can be automatically completed. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk inputs the user's social media activity data into AI, and the AI ​​suggests relevant input content.

[0080] The generation unit can estimate the user's emotions and adjust the tone of the content it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate content with a calm tone. If the user is excited, the generation unit can also generate content with an energetic tone. For example, if the user is excited, the generation unit can generate content with a lively tone. If the user is sad, the generation unit can also generate content with a comforting tone. For example, if the user is sad, the generation unit can generate content with a gentle tone. This allows for the generation of more appropriate content by adjusting the tone of the content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, for example, 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 a generation AI, or not. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the tone of the content.

[0081] The generation unit can learn the style of the user's past content during generation and generate consistent content. For example, the generation unit can learn the style of blog posts the user has created in the past and generate new articles in a similar style. The generation unit can also learn the content of social media posts the user has made in the past and generate consistent posts. For example, the generation unit can generate new posts based on content the user has posted in the past. The generation unit can also learn the style of product descriptions the user has created in the past and generate new descriptions in a similar style. For example, the generation unit can generate new product descriptions based on product descriptions the user has created in the past. In this way, consistent content can be generated by learning the style of the user's past content. Some or all of the above processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit inputs the user's past content data into a generation AI, and the generation AI generates consistent content.

[0082] The generation unit can adjust the content during generation to emphasize specific keywords or phrases. For example, the generation unit adjusts the structure of the text to emphasize keywords specified by the user. The generation unit can also adjust the content to repeatedly use phrases that the user considers important. For example, the generation unit uses a user-specified phrase multiple times. The generation unit can also adjust the content to include keywords specified by the user in the title or headings. For example, the generation unit includes keywords specified by the user in the title. This allows for the generation of content that aligns with the user's intent by emphasizing specific keywords or phrases. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit inputs keywords specified by the user into a generation AI, and the generation AI generates content to emphasize those keywords.

[0083] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise content. If the user is relaxed, the generation unit can also generate longer content with detailed explanations. For example, if the user is relaxed, the generation unit can generate long-form content with detailed explanations. If the user is excited, the generation unit can also generate content with visually stimulating effects. For example, if the user is excited, the generation unit can generate content that makes extensive use of images and videos. By adjusting the length of the content based on the user's emotions, content tailored to the user's needs can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, for example, 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 a generation AI, or not. For example, the generation unit inputs user emotion data into the generation AI, and the generation AI adjusts the length of the content.

[0084] The generation unit can automatically incorporate the user's industry-specific terminology during generation. For example, the generation unit can learn the specialized terminology of the user's industry and incorporate it into the content. The generation unit can also adjust the sentence structure to emphasize industry-specific terminology specified by the user. For example, the generation unit can use the industry terminology specified by the user multiple times. The generation unit can also generate new content based on industry-specific terminology previously used by the user. For example, the generation unit can learn the industry terminology previously used by the user and incorporate it into new content. This allows for the generation of more specialized content by incorporating the user's industry-specific terminology. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit inputs the user's industry-specific terminology data into a generation AI, and the generation AI generates content incorporating the industry terminology.

[0085] The generation unit can adjust the content format to match the user's target audience during generation. For example, if the user's target audience is young people, the generation unit will generate content in a casual format. If the user's target audience is business professionals, the generation unit can also generate content in a formal format. For example, if the user's target audience is business professionals, the generation unit will generate formal text using a lot of technical jargon. If the user's target audience is international, the generation unit can also generate content in a multilingual format. For example, if the user's target audience is international, the generation unit will generate content in multiple languages. This allows for the provision of more effective content by adjusting the content format to match the user's target audience. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not using a generation AI. For example, the generation unit inputs the user's target audience information into the generation AI, and the generation AI generates content in an appropriate format.

[0086] The service provider can estimate the user's emotions and adjust how the content is displayed based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and highly visible display method. If the user is relaxed, the service provider can also provide a display method that includes detailed information. For example, if the user is relaxed, the service provider can provide a display method that includes detailed explanations. If the user is in a hurry, the service provider can also provide a display method that gets straight to the point. For example, if the user is in a hurry, the service provider can provide a display method that highlights important information. This improves user convenience by adjusting how the content is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the service provider may be performed using a generative AI, for example, or without a generative AI. For example, the service provider inputs the user's emotion data into a generative AI, and the generative AI adjusts the display method.

[0087] The content delivery unit can select the optimal delivery timing by referring to the user's past browsing history at the time of delivery. For example, if the user has previously viewed content during a specific time period, the delivery unit will deliver the content according to that time period. The delivery unit can also deliver content according to a specific day of the week if the user has previously viewed content on that day of the week. For example, if the user has frequently viewed content on a specific day of the week in the past, the delivery unit will deliver the content according to that day of the week. The delivery unit can also predict the optimal delivery timing from the user's past browsing history and deliver the content accordingly. For example, the delivery unit analyzes the user's past browsing history and selects the optimal delivery timing. This allows the content to be delivered at the optimal time by referring to the user's past browsing history. Some or all of the above processes in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit inputs the user's past browsing history data into AI, and the AI ​​selects the optimal delivery timing.

[0088] The service provider can collect user feedback in real time at the time of delivery and reflect it in the next delivery. For example, the service provider can enable users to provide real-time feedback on the delivered content. The service provider can also reflect improvements in the next content delivery based on user feedback. For example, the service provider collects user feedback and reflects it in the next content delivery. The service provider can also collect user feedback in real time and respond immediately. For example, the service provider collects user feedback in real time and makes corrections immediately. This allows the service provider to reflect user feedback in the next delivery by collecting it in real time. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider inputs user feedback data into AI, and the AI ​​reflects it in the next delivery.

[0089] The content delivery unit can estimate the user's emotions and adjust the order of the content delivered based on the estimated emotions. For example, if the user is stressed, the delivery unit may prioritize displaying important content. If the user is relaxed, the delivery unit may also prioritize displaying detailed content. For example, if the user is relaxed, the delivery unit may prioritize displaying content containing detailed information. If the user is in a hurry, the delivery unit may also prioritize displaying content that gets straight to the point. For example, if the user is in a hurry, the delivery unit may prioritize displaying content that highlights important information. This improves user convenience by adjusting the order of content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not using a generative AI. For example, the delivery unit inputs user emotion data into a generative AI, and the generative AI adjusts the order of the content.

[0090] The service provider can select the optimal display format based on the user's device information at the time of delivery. For example, if the user is using a smartphone, the service provider will provide a display format that matches the screen size. If the user is using a tablet, the service provider can also provide a display format optimized for a larger screen. For example, if the user is using a tablet, the service provider will provide a display format that matches the screen size. If the user is using a smartwatch, the service provider can also provide a concise and highly visible display format. For example, if the user is using a smartwatch, the service provider will provide a format that displays concise information. By selecting the optimal display format based on the user's device information, user convenience is improved. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider inputs the user's device information into the AI, and the AI ​​selects the optimal display format.

[0091] The service provider can analyze the user's social media activity at the time of delivery and prioritize providing relevant content. For example, the service provider can provide relevant content based on what the user has shared on social media. The service provider can also provide relevant content based on the posts of accounts the user follows on social media. For example, the service provider can provide relevant content based on the latest posts of accounts the user follows. The service provider can also provide relevant content based on the activities of groups the user participates in on social media. For example, the service provider can provide relevant content based on the discussions of groups the user participates in. This allows the service provider to prioritize providing relevant content by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into AI, and the AI ​​can provide relevant content.

[0092] The editorial team can estimate the user's emotions and make editing suggestions based on those emotions. For example, if the user is relaxed, the editorial team can make detailed editing suggestions. If the user is in a hurry, the editorial team can also make concise editing suggestions. For example, if the user is in a hurry, the editorial team can make concise editing suggestions. If the user is excited, the editorial team can also make visually stimulating editing suggestions. For example, if the user is excited, the editorial team can make editing suggestions with a colorful design. This allows for more appropriate editing by making editing suggestions based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the editorial team may be performed using generative AI, or not. For example, the editorial team inputs user emotion data into a generative AI, and the generative AI makes editing suggestions.

[0093] The editorial team can suggest the optimal editing method by referring to the user's past editing history during editing. For example, the editorial team can suggest a similar editing method based on the user's past editing history. The editorial team can also prioritize suggesting editing tools that the user has used in the past. For example, the editorial team can suggest a similar tool based on the user's past editing history. The editorial team can also predict and suggest specific editing patterns from the user's past editing history. For example, the editorial team can analyze the user's past editing history and suggest specific editing patterns. This allows the editorial team to suggest the optimal editing method by referring to the user's past editing history. Some or all of the above processes in the editorial team may be performed using AI, for example, or not. For example, the editorial team can input the user's past editing history data into AI, and the AI ​​can suggest the optimal editing method.

[0094] The editorial team can adjust the edited content to emphasize specific keywords or phrases during the editing process. For example, the editorial team can adjust the structure of the text to emphasize keywords specified by the user. The editorial team can also adjust the edited content to repeatedly use phrases that the user considers important. For example, the editorial team can use a phrase specified by the user multiple times. The editorial team can also adjust the edited content to include keywords specified by the user in the title or headings. For example, the editorial team can include keywords specified by the user in the title. This allows for editing that aligns with the user's intent by emphasizing specific keywords or phrases. Some or all of the above processes performed by the editorial team may be carried out using AI, for example, or not. For example, the editorial team can input keywords specified by the user into the AI, and the AI ​​can adjust the edited content to emphasize those keywords.

[0095] The editorial team can estimate the user's emotions and determine editing priorities based on those emotions. For example, if the user is stressed, the editorial team will prioritize important editing. If the user is relaxed, the editorial team can also prioritize detailed editing. For example, if the user is relaxed, the editorial team will prioritize detailed editing. If the user is in a hurry, the editorial team can also prioritize concise editing. For example, if the user is in a hurry, the editorial team will prioritize concise editing. This allows for more appropriate editing by determining editing priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, 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 editorial team may be performed using a generative AI, or not. For example, the editorial team inputs user emotion data into a generative AI, and the generative AI determines the editing priorities.

[0096] The editorial team can automatically incorporate industry-specific terminology from the user's industry during the editing process. For example, the editorial team can learn the specialized terminology of the user's industry and incorporate it into the edited content. The editorial team can also adjust the structure of the text to emphasize industry-specific terminology specified by the user. For example, the editorial team can use the industry terminology specified by the user multiple times. The editorial team can also generate new edited content based on industry-specific terminology previously used by the user. For example, the editorial team can learn the industry terminology previously used by the user and incorporate it into new edited content. This allows for more specialized editing by incorporating the user's industry-specific terminology. Some or all of the above processes in the editorial team may be performed using AI, or not. For example, the editorial team can input the user's industry-specific terminology data into an AI, which can then generate edited content incorporating the industry terminology.

[0097] The editorial team can adjust the content to suit the user's target audience during the editing process. For example, if the target audience is young people, the editorial team can provide casual content. If the target audience is business professionals, the editorial team can also provide formal content. For example, if the target audience is business professionals, the editorial team can provide formal writing that uses a lot of technical jargon. If the target audience is international, the editorial team can also provide multilingual content. For example, if the target audience is international, the editorial team can provide content in multiple languages. By adjusting the content to suit the user's target audience, more effective editing becomes possible. Some or all of the above processes in the editorial team may be performed using AI, or not. For example, the editorial team can input the user's target audience information into AI, and the AI ​​can provide appropriate content.

[0098] The suggestion unit can estimate the user's emotions and determine the priority of suggested topics based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will prioritize suggesting detailed topics. If the user is in a hurry, the suggestion unit may also prioritize suggesting concise topics. For example, if the user is in a hurry, the suggestion unit will prioritize suggesting topics that get straight to the point. If the user is excited, the suggestion unit may also prioritize suggesting visually stimulating topics. For example, if the user is excited, the suggestion unit will prioritize suggesting topics with a colorful design. This allows for the suggestion of more appropriate topics by prioritizing topics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department inputs user sentiment data into a generating AI, which then determines the priority of topics.

[0099] The suggestion unit can analyze the user's past content trends and propose the most suitable topic when making a suggestion. For example, the suggestion unit can analyze the trends of content the user has created in the past and propose similar topics. The suggestion unit can also analyze the content of social media posts the user has made in the past and propose relevant topics. For example, the suggestion unit can propose relevant topics based on content the user has posted in the past. The suggestion unit can also analyze the trends of product descriptions the user has created in the past and propose relevant topics. For example, the suggestion unit can propose relevant topics based on product descriptions the user has created in the past. In this way, the suggestion unit can propose the most suitable topic by analyzing the user's past content trends. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the user's past content data into AI, and the AI ​​can propose the most suitable topic.

[0100] The suggestion function can prioritize suggesting topics that contain specific keywords or phrases. For example, it can prioritize suggesting topics that contain keywords specified by the user. The suggestion function can also prioritize suggesting topics that contain phrases that the user considers important. For example, it can prioritize suggesting topics that contain phrases specified by the user. The suggestion function can also prioritize suggesting topics that contain keywords specified by the user in their titles or headings. For example, it can suggest topics that contain keywords specified by the user in their titles. By prioritizing the suggestion of topics that contain specific keywords or phrases, the suggestion function can suggest topics that align with the user's intent. Some or all of the above processing in the suggestion function may be performed using AI, or not. For example, the suggestion function inputs keywords specified by the user into the AI, and the AI ​​suggests topics that contain those keywords.

[0101] The suggestion section can estimate the user's emotions and adjust how suggested topics are displayed based on the estimated emotions. For example, if the user is stressed, the suggestion section may provide a simple and highly visible display method. If the user is relaxed, the suggestion section may also provide a display method that includes detailed information. For example, if the user is relaxed, the suggestion section may provide a display method that includes detailed explanations. If the user is in a hurry, the suggestion section may also provide a display method that highlights the key points. For example, if the user is in a hurry, the suggestion section may provide a display method that emphasizes important information. This improves user convenience by adjusting how topics are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion section may be performed using a generative AI, or not using a generative AI. For example, the suggestion section inputs the user's emotion data into a generative AI, and the generative AI adjusts the display method.

[0102] The suggestion unit can suggest relevant topics based on the user's geographical location information when making suggestions. For example, if the user is in a specific region, the suggestion unit will suggest topics related to that region. If the user is traveling, the suggestion unit can also suggest topics related to their travel destination. For example, if the user is traveling, the suggestion unit will suggest tourist information and event information for their destination. If the user is participating in a specific event, the suggestion unit can also suggest topics related to that event. For example, the suggestion unit will suggest the latest information and related news about the event the user is participating in. In this way, by suggesting relevant topics based on the user's geographical location information, topics tailored to the user's interests can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit inputs the user's geographical location information into the AI, and the AI ​​suggests relevant topics.

[0103] The suggestion unit can analyze the user's social media activity and prioritize suggesting relevant topics when making suggestions. For example, the suggestion unit can suggest relevant topics based on what the user has shared on social media. The suggestion unit can also suggest relevant topics based on the content of posts from accounts the user follows on social media. For example, the suggestion unit can suggest relevant topics based on the latest posts from accounts the user follows. The suggestion unit can also suggest relevant topics based on the activities of groups the user participates in on social media. For example, the suggestion unit can suggest relevant topics based on the discussions in groups the user participates in. This allows the suggestion unit to prioritize suggesting relevant topics by analyzing the user's social media activity. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input the user's social media activity data into AI, and the AI ​​can suggest relevant topics.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The reception desk can automatically search for and provide relevant past content based on user input. For example, if a user inputs information about a specific topic, the reception desk will search for and present relevant content generated in the past. This allows users to create new content while referring to past content. The reception desk can also search for and provide relevant external information sources based on user input. For example, if a user inputs information about a specific topic, the reception desk will search for relevant articles and data on the internet and present them to the user. This allows users to create content while referring to external information. Furthermore, the reception desk can also search for and provide relevant images and videos based on user input. For example, if a user inputs information about a specific topic, the reception desk will search for and present relevant images and videos to the user. This allows users to create content that incorporates visual elements.

[0106] The generation unit can automatically perform SEO (Search Engine Optimization) on the generated content. For example, the generation unit inserts appropriate keywords into the generated content to improve its ranking in search engines. This increases the likelihood that the generated content will be viewed by more users. The generation unit can also automatically generate metadata (title, description, tags, etc.) for the generated content to enhance SEO. For example, the generation unit generates appropriate titles and descriptions for the generated content to improve its ranking in search engines. Furthermore, the generation unit can automatically set up internal links within the generated content to enhance SEO. For example, the generation unit inserts links to other related content within the generated content to encourage users to navigate the site. This improves the SEO effectiveness of the generated content and increases the likelihood that it will be viewed by more users.

[0107] The reception desk can estimate the user's emotions and adjust the feedback method based on those emotions. For example, if the user is stressed, the reception desk can provide concise and clear feedback to reduce the user's burden. If the user is relaxed, the reception desk can also provide detailed feedback to help the user understand more deeply. For example, if the user is relaxed, the reception desk can provide feedback that includes detailed explanations and additional information. If the user is in a hurry, the reception desk can also provide quick feedback to save the user's time. For example, if the user is in a hurry, the reception desk can provide concise feedback that gets straight to the point. This allows for more appropriate support by adjusting the feedback method based on the user's emotions.

[0108] The input system can analyze a user's past input history and suggest optimal input completion. For example, it can prioritize suggesting phrases and words that the user has frequently used in the past. This allows the user to streamline their input process. The input system can also suggest relevant input completion based on the user's past input. For example, it can analyze the user's past input and suggest related phrases and words. This allows the user to make new inputs while referring to past input. Furthermore, the input system can suggest contextual input completion based on the user's past input history. For example, if the user is typing about a specific topic, it can suggest phrases and words related to that topic. This allows the user to receive contextually appropriate input completion.

[0109] The reception desk can provide users with appropriate guidelines and templates based on their input. For example, if a user is creating a blog post, the reception desk will provide guidelines on the structure and writing style of the blog post. This allows the user to create the blog post efficiently. The reception desk can also provide templates suitable for content in a specific format if the user is creating content in that format. For example, if a user is creating a product description, the reception desk will provide a product description template. This allows the user to create content efficiently using the template. Furthermore, the reception desk can provide relevant reference materials and samples based on the user's input. For example, if a user is inputting information about a specific topic, the reception desk will provide reference materials and samples related to that topic. This allows the user to create content using the reference materials and samples.

[0110] The reception desk can estimate the user's emotions and adjust the way it confirms input based on those emotions. For example, if the user is nervous, the reception desk can provide a concise and clear confirmation message to alleviate the user's anxiety. If the user is relaxed, the reception desk can provide a detailed confirmation message to help the user feel more at ease confirming their input. For example, if the user is relaxed, the reception desk can provide a confirmation message that includes detailed explanations and additional information. If the user is in a hurry, the reception desk can provide a quick confirmation message to save the user's time. For example, if the user is in a hurry, the reception desk can provide a concise and to-the-point confirmation message. By adjusting the confirmation method based on the user's emotions, more appropriate support can be provided.

[0111] The reception desk can automatically suggest relevant local events and news based on the user's geographical location. For example, if the user is in a specific region, it can suggest events and the latest news happening in that region. This allows the user to easily access local information. The reception desk can also suggest tourist information and event information related to the user's travel destination if the user is traveling. For example, if the user is traveling, it can suggest tourist attractions and event information in the destination. This allows the user to easily access information about their travel destination. Furthermore, if the reception desk is participating in a specific event, it can suggest the latest information and related news related to that event. For example, it can suggest the latest information and related news about the event the user is attending. This allows the user to easily access information related to the event.

[0112] The reception desk can analyze a user's social media activity and automatically suggest relevant topics and hashtags. For example, it can suggest relevant topics and hashtags based on what the user has shared on social media. This allows users to post more effectively on social media. The reception desk can also suggest relevant topics and hashtags based on the content of accounts the user follows. For example, it can suggest relevant topics and hashtags based on the latest posts from accounts the user follows. This allows users to post content that is highly relevant to the accounts they follow. Furthermore, the reception desk can suggest relevant topics and hashtags based on the activities of groups the user participates in. For example, it can suggest relevant topics and hashtags based on the discussions in groups the user participates in. This allows users to post content that is highly relevant to the group's activities.

[0113] The generation unit can estimate the user's emotions and adjust the style of the content it generates based on those emotions. For example, if the user is relaxed, it can generate casual style content. If the user is excited, it can also generate energetic style content. For example, if the user is excited, it can generate lively style content. If the user is sad, it can also generate comforting style content. For example, if the user is sad, it can generate gentle style content. By adjusting the style of content based on the user's emotions, it is possible to generate more appropriate content.

[0114] The generation unit can analyze the performance data of a user's past content during the generation process to generate optimal content. For example, it can analyze the performance data of blog posts a user has previously created and generate new articles with similar performance. This allows users to create content based on past successes. The generation unit can also analyze the performance data of social media posts a user has previously made and generate consistent posts. For example, it can generate new posts based on the performance data of content a user has previously posted. This allows users to create social media posts based on past successes. Furthermore, the generation unit can analyze the performance data of product descriptions a user has previously created and generate new descriptions with similar performance. For example, it can generate new product descriptions based on the performance data of product descriptions a user has previously created. This allows users to create product descriptions based on past successes.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The reception desk receives user input. User input includes text input, voice input, and image input. For example, the reception desk receives text data entered by the user. It can also receive voice input and convert it into text data using speech recognition technology. Furthermore, it can receive image input and convert it into text data using image recognition technology. Step 2: The generation unit uses a generation AI to generate content based on the information received by the reception unit. For example, it analyzes text data entered by the user and generates new content. The generation unit can also use the generation AI to analyze user input and past content and generate new content. Step 3: The provider delivers the content generated by the generator to the user. For example, the provider delivers the generated content to the user through a website or application. The provider can also send the generated content to the user via email. Furthermore, the provider can print the generated content and deliver it to the user. Step 4: The editorial team edits the content generated by the generation team. For example, they may revise the content, add new information, or delete unnecessary parts. Step 5: The suggestion unit proposes topics based on the content generated by the generation unit. For example, it analyzes the content of the generated material and proposes relevant topics or new ideas.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, editing unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives text or voice input from the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates new content using generation AI. The provision unit is implemented by the output device 40 of the smart device 14 and provides the generated content to the user. The editing unit is implemented by the specific processing unit 290 of the data processing unit 12 and edits the generated content. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a new topic based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, editing unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives voice input from the user. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates new content using generation AI. The provision unit is implemented, for example, by the speaker 240 of the smart glasses 214 and provides the generated content to the user. The editing unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and edits the generated content. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes a new topic based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

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

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

[0147] In 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, editing unit, and proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates new content using a generation AI. The provision unit is implemented by, for example, the speaker 240 of the headset terminal 314 and provides the generated content to the user. The editing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and edits the generated content. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a new topic based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.).

[0166] 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.

[0167] 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.

[0168] 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.

[0169] Each of the multiple elements described above, including the reception unit, generation unit, provision unit, editing unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives voice input from the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates new content using a generation AI. The provision unit is implemented by, for example, the speaker 240 of the robot 414 and provides the generated content to the user. The editing unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and edits the generated content. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes a new topic based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] (Note 1) A reception area that receives user input, A generation unit that generates content based on the information received by the reception unit, A provisioning unit that provides the content generated by the generation unit to the user, An editing unit that edits the content generated by the generation unit, The system comprises a suggestion unit that proposes topics based on the content generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Evaluating content quality using generation AI The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is Provide real-time feedback to the user based on their input. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Based on the user's geographical location, relevant topics are automatically suggested. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyzes users' social media activity and automatically completes relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is It estimates the user's emotions and adjusts the tone of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is During generation, the system learns the user's past content style to generate consistent content. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is During generation, the content is adjusted to emphasize specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is During generation, the system automatically incorporates industry-specific terminology used by the user. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the content format is adjusted to suit the user's target audience. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, It estimates the user's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing content, the system will refer to the user's past browsing history to select the optimal timing for delivery. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We collect user feedback in real time when providing the service and incorporate it into future offerings. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and adjusts the order of content delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, the optimal display format is selected based on the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing content, we analyze the user's social media activity and prioritize delivering relevant content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editorial department, It estimates the user's emotions and makes editing suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned editorial department, During editing, the system will refer to the user's past editing history to suggest the optimal editing method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned editorial department, When editing, adjust the content to emphasize specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned editorial department, It estimates the user's emotions and determines editing priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned editorial department, During editing, the system automatically incorporates industry-specific terminology used by the user. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned editorial department, When editing, adjust the content to match the user's target audience. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and prioritizes suggested topics based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, When making suggestions, we analyze the user's past content trends to propose the most suitable topics. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making suggestions, prioritize topics that include specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, It estimates the user's sentiment and adjusts how suggested topics are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, When making a proposal, we suggest relevant topics based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned proposal section is, When making suggestions, we analyze the user's social media activity and prioritize suggesting relevant topics. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0189] 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 receives user input, A generation unit that generates content based on the information received by the reception unit, A provisioning unit that provides the content generated by the generation unit to the user, An editing unit that edits the content generated by the generation unit, The system comprises a suggestion unit that proposes topics based on the content generated by the generation unit. A system characterized by the following features.

2. The generating unit is Evaluating content quality using generation AI The system according to feature 1.

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

4. The aforementioned reception unit is It analyzes the user's past input history and suggests the optimal input method. The system according to feature 1.

5. The aforementioned reception unit is Provide real-time feedback to the user based on their input. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and adjusts the input interface design based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Based on the user's geographical location, relevant topics are automatically suggested. The system according to feature 1.

8. The aforementioned reception unit is Analyzes users' social media activity and automatically completes relevant input. The system according to feature 1.

9. The generating unit is It estimates the user's emotions and adjusts the tone of the generated content based on those estimated emotions. The system according to feature 1.

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