system

Abstract

The system according to this embodiment aims to create campaign landing pages quickly and efficiently. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and an output unit. The reception unit receives input from past reference landing pages, landing pages indicating the desired style, and an overview of the campaign. The generation unit analyzes the information input by the reception unit and generates a landing page. The output unit outputs the landing page 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 method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of creating a landing page (LP) for a campaign is complicated and time-consuming.

[0005] The system according to the embodiment aims to create a landing page for a campaign quickly and efficiently.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and an output unit. The reception unit inputs past LPs for reference, LPs from which the desired taste can be understood, and an outline of the campaign. The generation unit analyzes the information input by the reception unit and generates an LP. The output unit outputs the LP generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can quickly and efficiently create campaign landing pages. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The landing page (LP) automatic generation system according to an embodiment of the present invention is a system that utilizes a generation AI to quickly create campaign LPs. This system eliminates the hassle of the conventional LP creation process, such as orientation to designers, delivery of materials, confirmation of manuscripts, and back-and-forth for additions and revisions, and can complete an LP in one day. Specifically, by providing the generation AI with reference past LPs, LPs that show the desired style, and an overview of the campaign, the generation AI generates an LP based on this information. In this process, the generation AI creates not only text information but also images and videos. This eliminates communication with designers and allows for the rapid completion of LPs. For example, a user inputs "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI. The generation AI analyzes this information and automatically generates an LP that matches the desired style. The generated LP includes not only text information but also images and videos, which the user can use as is. This mechanism significantly reduces the time required for LP creation and eliminates delays caused by communication with designers. Furthermore, because the generation AI automatically generates the LP, consistent quality can be ensured. For example, by generating landing pages (LPs) based on past success stories, it becomes possible to quickly deploy effective campaigns. This allows LP automated generation systems to rapidly create LPs using generation AI.

[0029] The LP automatic generation system according to the embodiment comprises a reception unit, a generation unit, and an output unit. The reception unit receives input from past reference LPs, LPs indicating the desired style, and a campaign overview. For example, the user can input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI in the reception unit. The generation unit analyzes the information input by the reception unit and generates an LP. The generation unit uses the generation AI to generate not only text information but also images and videos. For example, the generation AI automatically generates an LP that matches the desired style based on examples of past successful campaign LPs and the overview of the current campaign. The generation unit can generate not only text information but also images and videos using the generation AI. The output unit outputs the LP generated by the generation unit. For example, the output unit provides the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, or printable data. As a result, the LP automatic generation system according to the embodiment can quickly create LPs using the generation AI.

[0030] The reception section allows users to input reference past landing pages, landing pages that indicate their desired style, and a campaign overview. Specifically, users can input "examples of successful past campaign landing pages" and "an overview of the current campaign" to the generation AI. The reception section provides an intuitive interface for users, featuring text input fields and file upload functions. For example, users can enter URLs of past landing pages or upload PDF files. There are also checkboxes and dropdown menus to select desired styles and design directions. Furthermore, a dedicated form is provided for entering detailed information such as target audience, main message, campaign period, and budget for the campaign overview. This ensures that users can input all the necessary information without omission, providing the generation AI with the foundational data to create accurate landing pages.

[0031] The generation unit analyzes the information entered by the reception unit and generates landing pages (LPs). Using a generation AI, the generation unit generates not only text information but also images and videos. Specifically, the generation AI utilizes natural language processing technology to analyze the campaign overview entered by the user and generates appropriate taglines and descriptions. Furthermore, the image generation AI generates visually appealing images and videos, referencing examples of successful past campaign LPs. For example, the generation AI adjusts colors, layouts, and font styles based on the user's desired aesthetic to create a cohesive design. The generation AI also learns from past data to deliver optimal content to the user's specified target audience and delivers effective messaging. The generation unit combines the generated text, images, and videos to automatically produce a high-quality LP. This allows the generation unit to quickly create high-quality LPs that meet user requirements.

[0032] The output unit outputs the landing page (LP) generated by the generation unit. Specifically, it provides the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, and print-ready data. For example, if the user selects the web page format, the output unit generates a web page that can be immediately published using web technologies such as HTML, CSS, and JavaScript (registered trademark). If the user selects the PDF format, the output unit generates a high-resolution PDF file and provides a download link. Furthermore, if the user selects print-ready data, the output unit outputs the data in a standard format used by printing companies, allowing the user to easily place a print order. The output unit also includes email sending and cloud storage upload functions to allow users to easily share the generated LP. In this way, the output unit makes the generated LP available to users in various formats, helping them to deploy campaigns quickly and efficiently.

[0033] The generation unit can generate not only text information but also images and videos using a generation AI. For example, the generation unit uses a generation AI to generate not only text information but also images and videos. The generation unit uses a generation AI to automatically generate an LP that matches the desired style, based on examples of past successful campaign LPs and an overview of the current campaign. The generation unit can use a generation AI to generate not only text information but also images and videos. This improves the completeness of the LP by having the generation AI generate not only text information but also images and videos. Some or all of the above-described processes in the generation unit may be performed using a generation AI or without using a generation AI. For example, the generation unit inputs examples of past successful campaign LPs and an overview of the current campaign to the generation AI, and the generation AI generates text information, images, and videos based on this information.

[0034] The reception desk allows users to input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI. For example, the reception desk allows users to input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI. The reception desk allows the generation AI to generate a more appropriate LP by providing specific information to the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk allows users to input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI, and the generation AI generates an LP based on this information.

[0035] The generation unit can automatically generate landing pages (LPs) that match the desired style using a generation AI. For example, the generation unit uses a generation AI to automatically generate LPs that match the desired style. The generation unit uses a generation AI to automatically generate LPs that match the desired style based on examples of past successful campaign LPs and an overview of the current campaign. The generation unit can use a generation AI to automatically generate LPs that match the desired style. As a result, user satisfaction improves as the generation AI automatically generates LPs that match the desired style. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs examples of past successful campaign LPs and an overview of the current campaign to the generation AI, and the generation AI generates LPs that match the desired style based on this information.

[0036] The output unit can provide the generated LP to the user. For example, the output unit can provide the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, or print data. By providing the generated LP to the user, the output unit can quickly launch the campaign. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit outputs the generated LP in formats such as PDF, web page, or print data and provides it to the user.

[0037] The reception desk can analyze past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk analyzes past input history and suggests the optimal input method.

[0038] The reception desk can filter the user's current projects and areas of interest during input. For example, the reception desk can prioritize displaying information related to the user's current projects. The reception desk can filter and display highly relevant information based on the user's areas of interest. The reception desk can suggest relevant information based on areas the user has shown interest in in the past. This allows the reception desk to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk filters the user's current projects and areas of interest during input.

[0039] The reception desk can prioritize retrieving highly relevant information based on the user's geographical location information during input. For example, the reception desk can prioritize displaying relevant information based on the user's current location. The reception desk can provide region-specific information based on the user's geographical location information. The reception desk can suggest relevant information based on places the user has visited in the past. This allows for the provision of more appropriate information by providing highly relevant information based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk prioritizes retrieving highly relevant information based on the user's geographical location information during input.

[0040] The reception desk can analyze the user's social media activity and obtain relevant information during input. For example, the reception desk can suggest relevant information based on information the user has shared on social media. The reception desk can analyze the user's social media activity history and provide information of interest. The reception desk can display relevant information based on information about accounts the user follows. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk analyzes the user's social media activity and obtains relevant information during input.

[0041] The generation unit can optimize its generation algorithm by referring to past successes during generation. For example, the generation unit can generate an optimal design based on the design of past successful landing pages. The generation unit can generate an effective landing page using an algorithm learned from past successes. The generation unit can analyze past successes and generate a landing page with the optimal combination of elements. In this way, by referring to past successes, the generation algorithm can be optimized and an effective landing page can be generated. Some or all of the above 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 optimizes its generation algorithm by referring to past successes during generation.

[0042] The generation unit can apply different generation algorithms depending on the campaign category during generation. For example, the generation unit uses different generation algorithms for product promotion LPs and event announcement LPs. The generation unit can select the optimal generation algorithm according to the campaign objective. The generation unit can generate LPs that combine different design elements for each category. This allows for the generation of more effective LPs by applying different generation algorithms depending on the campaign category. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may not be performed using generation AI. For example, the generation unit applies different generation algorithms depending on the campaign category during generation.

[0043] The generation unit can determine the generation priority based on the campaign submission deadlines during generation. For example, the generation unit can prioritize generating LPs for campaigns with approaching deadlines. The generation unit can postpone generating LPs for campaigns with ample time before submission. The generation unit can automatically adjust the generation schedule based on the submission deadlines. This allows for efficient generation of LPs by determining the generation priority based on the campaign submission deadlines. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can determine the generation priority based on the campaign submission deadlines during generation.

[0044] The generation unit can adjust the generation order based on the relevance of campaigns during generation. For example, the generation unit can prioritize generating landing pages (LPs) for campaigns with high relevance. The generation unit can postpone generating LPs for campaigns with low relevance. The generation unit can automatically adjust the generation order based on relevance. This allows for the generation of more effective LPs by adjusting the generation order based on campaign relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit adjusts the generation order based on the relevance of campaigns during generation.

[0045] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit may prioritize providing display methods that the user has previously preferred. The output unit may suggest the optimal display method based on the user's past operation history. The output unit may provide a customized display method based on display methods that the user has previously used. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit may select the optimal display method by referring to the user's past operation history when outputting.

[0046] The output unit can evaluate the quality of the generated LP during output and suggest corrections as needed. For example, the output unit can automatically detect typographical errors in the text of the generated LP and suggest corrections. The output unit can evaluate the consistency of the design of the generated LP and suggest corrections as needed. The output unit can evaluate the quality of images and videos in the generated LP and suggest corrections as needed. This allows for the provision of higher-quality LPs by evaluating the quality of the generated LP. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit evaluates the quality of the generated LP during output and suggests corrections as needed.

[0047] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. If the user is using a tablet, the output unit can provide a display method optimized for a larger screen. If the user is using a desktop, the output unit can provide a display method that includes detailed information. This allows for the provision of more appropriate information by providing the optimal display method based on the user's device information. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit selects the optimal display method when outputting, taking into account the user's device information.

[0048] The output unit can improve the accuracy of the output by referring to related literature for the generated LP during output. For example, the output unit can verify the accuracy of the generated LP content by comparing it with related literature. The output unit can suggest the optimal design by comparing the design of the generated LP with related literature. The output unit can verify the consistency of the content by comparing the text of the generated LP with related literature. In this way, the accuracy of the generated LP can be improved by referring to related literature. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can improve the accuracy of the output by referring to related literature for the generated LP during output.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The reception desk can analyze a user's past campaign data and suggest the optimal template for generating landing pages (LPs). For example, the reception desk can automatically select the most effective template based on data from a user's past successful campaigns. Furthermore, the reception desk can extract specific elements from templates the user has used in the past and suggest a customized template. In addition, the reception desk can suggest the latest templates based on the user's industry and market trends. This allows users to quickly create LPs that leverage past successes while staying up-to-date with the latest trends.

[0051] The generation unit can customize the landing page (LP) during generation, taking into account the demographic information of the user's target audience. For example, if the target audience is young people, it can generate an LP with a pop and casual design. If the target audience is business professionals, it can generate an LP with a professional and simple design. If the target audience is elderly, it can generate an LP using highly visible fonts and colors. This allows for the generation of LPs optimized for the target audience, maximizing the effectiveness of the campaign.

[0052] The generation unit can optimize landing pages (LPs) by considering past user feedback during the generation process. For example, it can generate LPs that reflect improvements previously pointed out by users. Furthermore, it can generate LPs that incorporate elements that users have previously given high ratings to. It can also analyze past user feedback, extract common areas for improvement, and improve overall quality. This allows for the generation of more satisfying LPs by leveraging past user feedback.

[0053] The generation unit can automatically apply the user's brand guidelines during the generation process. For example, it can generate landing pages (LPs) that automatically incorporate the user's brand colors and fonts. Furthermore, it can select text and images based on the user's brand tone and style guide. It can also generate consistent LPs by placing the user's brand logo in the appropriate position. This allows for the rapid generation of LPs that comply with the user's brand guidelines.

[0054] The generation unit can analyze the user's competitors' landing pages (LPs) during the generation process and create differentiated LPs. For example, it can analyze the design and content of competitors and propose a unique design. Furthermore, it can consider the strengths and weaknesses of competitors and incorporate the most suitable elements for the user's LP. It can also refer to competitors' campaign strategies and propose the most suitable approach for the user's campaign. This allows for differentiation from competitors and the creation of more effective LPs.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The reception desk inputs reference past landing pages, landing pages that indicate the desired style, and an overview of the campaign. For example, the user can input "examples of past successful campaign landing pages" and "an overview of the current campaign" to the generation AI. Step 2: The generation unit analyzes the information entered by the reception unit and generates the landing page (LP). The generation unit uses a generation AI to generate not only text information but also images and videos. For example, the generation AI automatically generates an LP that matches the desired style based on examples of past successful campaign LPs and an overview of the current campaign. Step 3: The output unit outputs the LP generated by the generation unit. For example, it provides the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, or printable data.

[0057] (Example of form 2) The landing page (LP) automatic generation system according to an embodiment of the present invention is a system that utilizes a generation AI to quickly create campaign LPs. This system eliminates the hassle of the conventional LP creation process, such as orientation to designers, delivery of materials, confirmation of manuscripts, and back-and-forth for additions and revisions, and can complete an LP in one day. Specifically, by providing the generation AI with reference past LPs, LPs that show the desired style, and an overview of the campaign, the generation AI generates an LP based on this information. In this process, the generation AI creates not only text information but also images and videos. This eliminates communication with designers and allows for the rapid completion of LPs. For example, a user inputs "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI. The generation AI analyzes this information and automatically generates an LP that matches the desired style. The generated LP includes not only text information but also images and videos, which the user can use as is. This mechanism significantly reduces the time required for LP creation and eliminates delays caused by communication with designers. Furthermore, because the generation AI automatically generates the LP, consistent quality can be ensured. For example, by generating landing pages (LPs) based on past success stories, it becomes possible to quickly deploy effective campaigns. This allows LP automated generation systems to rapidly create LPs using generation AI.

[0058] The LP automatic generation system according to the embodiment comprises a reception unit, a generation unit, and an output unit. The reception unit receives input from past reference LPs, LPs indicating the desired style, and a campaign overview. For example, the user can input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI in the reception unit. The generation unit analyzes the information input by the reception unit and generates an LP. The generation unit uses the generation AI to generate not only text information but also images and videos. For example, the generation AI automatically generates an LP that matches the desired style based on examples of past successful campaign LPs and the overview of the current campaign. The generation unit can generate not only text information but also images and videos using the generation AI. The output unit outputs the LP generated by the generation unit. For example, the output unit provides the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, or printable data. As a result, the LP automatic generation system according to the embodiment can quickly create LPs using the generation AI.

[0059] The reception section allows users to input reference past landing pages, landing pages that indicate their desired style, and a campaign overview. Specifically, users can input "examples of successful past campaign landing pages" and "an overview of the current campaign" to the generation AI. The reception section provides an intuitive interface for users, featuring text input fields and file upload functions. For example, users can enter URLs of past landing pages or upload PDF files. There are also checkboxes and dropdown menus to select desired styles and design directions. Furthermore, a dedicated form is provided for entering detailed information such as target audience, main message, campaign period, and budget for the campaign overview. This ensures that users can input all the necessary information without omission, providing the generation AI with the foundational data to create accurate landing pages.

[0060] The generation unit analyzes the information entered by the reception unit and generates landing pages (LPs). Using a generation AI, the generation unit generates not only text information but also images and videos. Specifically, the generation AI utilizes natural language processing technology to analyze the campaign overview entered by the user and generates appropriate taglines and descriptions. Furthermore, the image generation AI generates visually appealing images and videos, referencing examples of successful past campaign LPs. For example, the generation AI adjusts colors, layouts, and font styles based on the user's desired aesthetic to create a cohesive design. The generation AI also learns from past data to deliver optimal content to the user's specified target audience and delivers effective messaging. The generation unit combines the generated text, images, and videos to automatically produce a high-quality LP. This allows the generation unit to quickly create high-quality LPs that meet user requirements.

[0061] The output unit outputs the landing page (LP) generated by the generation unit. Specifically, it provides the generated LP to the user. The output unit can output the generated LP in various formats, including PDF, web page, and print-ready data. For example, if the user selects the web page format, the output unit generates a web page that can be immediately published using web technologies such as HTML, CSS, and JavaScript. If the user selects the PDF format, the output unit generates a high-resolution PDF file and provides a download link. Furthermore, if the user selects print-ready data, the output unit outputs the data in a standard format used by printing companies, allowing the user to easily place a print order. The output unit also includes email sending and cloud storage upload functions to allow users to easily share the generated LP. In this way, the output unit makes the generated LP available to users in various formats, helping them to deploy campaigns quickly and efficiently.

[0062] The generation unit can generate not only text information but also images and videos using a generation AI. For example, the generation unit uses a generation AI to generate not only text information but also images and videos. The generation unit uses a generation AI to automatically generate an LP that matches the desired style, based on examples of past successful campaign LPs and an overview of the current campaign. The generation unit can use a generation AI to generate not only text information but also images and videos. This improves the completeness of the LP by having the generation AI generate not only text information but also images and videos. Some or all of the above-described processes in the generation unit may be performed using a generation AI or without using a generation AI. For example, the generation unit inputs examples of past successful campaign LPs and an overview of the current campaign to the generation AI, and the generation AI generates text information, images, and videos based on this information.

[0063] The reception desk allows users to input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI. For example, the reception desk allows users to input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI. The reception desk allows the generation AI to generate a more appropriate LP by providing specific information to the user. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk allows users to input "examples of past successful campaign LPs" and "an overview of the current campaign" to the generation AI, and the generation AI generates an LP based on this information.

[0064] The generation unit can automatically generate landing pages (LPs) that match the desired style using a generation AI. For example, the generation unit uses a generation AI to automatically generate LPs that match the desired style. The generation unit uses a generation AI to automatically generate LPs that match the desired style based on examples of past successful campaign LPs and an overview of the current campaign. The generation unit can use a generation AI to automatically generate LPs that match the desired style. As a result, user satisfaction improves as the generation AI automatically generates LPs that match the desired style. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit inputs examples of past successful campaign LPs and an overview of the current campaign to the generation AI, and the generation AI generates LPs that match the desired style based on this information.

[0065] The output unit can provide the generated LP to the user. For example, the output unit can provide the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, or print data. By providing the generated LP to the user, the output unit can quickly launch the campaign. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit outputs the generated LP in formats such as PDF, web page, or print data and provides it to the user.

[0066] The reception desk can estimate the user's emotions and adjust the priority of input content based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize inputting the most important information and postpone other information. If the user is relaxed, the reception desk can allow them to input detailed information in a sequential manner. If the user is in a hurry, the reception desk can allow them to input only minimal information and allow them to input additional information later. This allows for the input of more appropriate information by adjusting the priority of input content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk estimates the user's emotions and adjusts the priority of input content based on the estimated emotions.

[0067] The reception desk can analyze past input history and suggest the optimal input method. For example, the reception desk can automatically display information that the user has frequently entered in the past as a suggestion. The reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can predict and suggest information that the user will use at a specific time of day based on their past input history. In this way, by analyzing past input history, the reception desk can suggest the optimal input method for the user. Some or all of the above processes in the reception desk may be performed using AI or not. For example, the reception desk analyzes past input history and suggests the optimal input method.

[0068] The reception desk can filter the user's current projects and areas of interest during input. For example, the reception desk can prioritize displaying information related to the user's current projects. The reception desk can filter and display highly relevant information based on the user's areas of interest. The reception desk can suggest relevant information based on areas the user has shown interest in in the past. This allows the reception desk to provide highly relevant information by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk filters the user's current projects and areas of interest during input.

[0069] The reception unit can estimate the user's emotions and adjust how the input content is displayed based on the estimated emotions. For example, if the user is nervous, the reception unit can provide a simple and highly visible display method. If the user is relaxed, the reception unit can provide a display method that includes detailed information. If the user is in a hurry, the reception unit can provide a display method that gets straight to the point. This allows for the provision of more appropriate information by adjusting how the input content is displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit estimates the user's emotions and adjusts how the input content is displayed based on the estimated emotions.

[0070] The reception desk can prioritize retrieving highly relevant information based on the user's geographical location information during input. For example, the reception desk can prioritize displaying relevant information based on the user's current location. The reception desk can provide region-specific information based on the user's geographical location information. The reception desk can suggest relevant information based on places the user has visited in the past. This allows for the provision of more appropriate information by providing highly relevant information based on the user's geographical location information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk prioritizes retrieving highly relevant information based on the user's geographical location information during input.

[0071] The reception desk can analyze the user's social media activity and obtain relevant information during input. For example, the reception desk can suggest relevant information based on information the user has shared on social media. The reception desk can analyze the user's social media activity history and provide information of interest. The reception desk can display relevant information based on information about accounts the user follows. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant information. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk analyzes the user's social media activity and obtains relevant information during input.

[0072] The generation unit can estimate the user's emotions and adjust the presentation of the generated landing page (LP) based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a relaxed-designed LP. If the user is in a hurry, the generation unit can generate a simple, to-the-point LP. If the user is excited, the generation unit can generate a visually stimulating LP. This allows for the generation of more appropriate LPs by adjusting the presentation of the LP according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit estimates the user's emotions and adjusts the presentation of the generated LP based on the estimated emotions.

[0073] The generation unit can optimize its generation algorithm by referring to past successes during generation. For example, the generation unit can generate an optimal design based on the design of past successful landing pages. The generation unit can generate an effective landing page using an algorithm learned from past successes. The generation unit can analyze past successes and generate a landing page with the optimal combination of elements. In this way, by referring to past successes, the generation algorithm can be optimized and an effective landing page can be generated. Some or all of the above 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 optimizes its generation algorithm by referring to past successes during generation.

[0074] The generation unit can apply different generation algorithms depending on the campaign category during generation. For example, the generation unit uses different generation algorithms for product promotion LPs and event announcement LPs. The generation unit can select the optimal generation algorithm according to the campaign objective. The generation unit can generate LPs that combine different design elements for each category. This allows for the generation of more effective LPs by applying different generation algorithms depending on the campaign category. Some or all of the above-described processes in the generation unit may be performed using generation AI, or they may not be performed using generation AI. For example, the generation unit applies different generation algorithms depending on the campaign category during generation.

[0075] The generation unit can estimate the user's emotions and adjust the length of the generated landing page (LP) based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate a short, concise LP. If the user is relaxed, the generation unit can generate a longer LP containing detailed information. If the user is excited, the generation unit can generate an LP with many visually stimulating elements. By adjusting the length of the LP according to the user's emotions, a more appropriate LP can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit estimates the user's emotions and adjusts the length of the generated LP based on the estimated emotions.

[0076] The generation unit can determine the generation priority based on the campaign submission deadlines during generation. For example, the generation unit can prioritize generating LPs for campaigns with approaching deadlines. The generation unit can postpone generating LPs for campaigns with ample time before submission. The generation unit can automatically adjust the generation schedule based on the submission deadlines. This allows for efficient generation of LPs by determining the generation priority based on the campaign submission deadlines. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can determine the generation priority based on the campaign submission deadlines during generation.

[0077] The generation unit can adjust the generation order based on the relevance of campaigns during generation. For example, the generation unit can prioritize generating landing pages (LPs) for campaigns with high relevance. The generation unit can postpone generating LPs for campaigns with low relevance. The generation unit can automatically adjust the generation order based on relevance. This allows for the generation of more effective LPs by adjusting the generation order based on campaign relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit adjusts the generation order based on the relevance of campaigns during generation.

[0078] The output unit can estimate the user's emotions and adjust the display method of the landing page (LP) based on the estimated emotions. For example, if the user is nervous, the output unit can provide a simple and highly visible display method. If the user is relaxed, the output unit can provide a display method that includes detailed information. If the user is in a hurry, the output unit can provide a display method that gets straight to the point. By adjusting the display method of the LP according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit estimates the user's emotions and adjusts the display method of the landing page (LP) based on the estimated emotions.

[0079] The output unit can select the optimal display method by referring to the user's past operation history when outputting. For example, the output unit may prioritize providing display methods that the user has previously preferred. The output unit may suggest the optimal display method based on the user's past operation history. The output unit may provide a customized display method based on display methods that the user has previously used. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit may select the optimal display method by referring to the user's past operation history when outputting.

[0080] The output unit can evaluate the quality of the generated LP during output and suggest corrections as needed. For example, the output unit can automatically detect typographical errors in the text of the generated LP and suggest corrections. The output unit can evaluate the consistency of the design of the generated LP and suggest corrections as needed. The output unit can evaluate the quality of images and videos in the generated LP and suggest corrections as needed. This allows for the provision of higher-quality LPs by evaluating the quality of the generated LP. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit evaluates the quality of the generated LP during output and suggests corrections as needed.

[0081] The output unit can estimate the user's emotions and adjust the LP's operating procedures based on the estimated emotions. For example, if the user is nervous, the output unit can provide simple and intuitive operating procedures. If the user is relaxed, the output unit can provide detailed operating procedures. If the user is in a hurry, the output unit can output the LP with minimal operating procedures. This allows for more appropriate operating procedures to be provided by adjusting the LP's operating procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit estimates the user's emotions and adjusts the LP's operating procedures based on the estimated emotions.

[0082] The output unit can select the optimal display method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can provide a display method that matches the screen size. If the user is using a tablet, the output unit can provide a display method optimized for a larger screen. If the user is using a desktop, the output unit can provide a display method that includes detailed information. This allows for the provision of more appropriate information by providing the optimal display method based on the user's device information. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit selects the optimal display method when outputting, taking into account the user's device information.

[0083] The output unit can improve the accuracy of the output by referring to related literature for the generated LP during output. For example, the output unit can verify the accuracy of the generated LP content by comparing it with related literature. The output unit can suggest the optimal design by comparing the design of the generated LP with related literature. The output unit can verify the consistency of the content by comparing the text of the generated LP with related literature. In this way, the accuracy of the generated LP can be improved by referring to related literature. Some or all of the above processing in the output unit may be performed using AI or not. For example, the output unit can improve the accuracy of the output by referring to related literature for the generated LP during output.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The reception desk can analyze a user's past campaign data and suggest the optimal template for generating landing pages (LPs). For example, the reception desk can automatically select the most effective template based on data from a user's past successful campaigns. Furthermore, the reception desk can extract specific elements from templates the user has used in the past and suggest a customized template. In addition, the reception desk can suggest the latest templates based on the user's industry and market trends. This allows users to quickly create LPs that leverage past successes while staying up-to-date with the latest trends.

[0086] The generation unit can estimate the user's emotions and adjust the colors of the generated landing page (LP) based on the estimated emotions. For example, if the user is relaxed, it can generate an LP with calming colors. If the user is excited, it can generate an LP with vibrant and visually stimulating colors. If the user is stressed, it can use simple and highly visible colors. In this way, by adjusting the colors according to the user's emotions, a more effective LP can be generated. Emotion estimation is achieved using an emotion engine or generation AI, etc.

[0087] The generation unit can customize the landing page (LP) during generation, taking into account the demographic information of the user's target audience. For example, if the target audience is young people, it can generate an LP with a pop and casual design. If the target audience is business professionals, it can generate an LP with a professional and simple design. If the target audience is elderly, it can generate an LP using highly visible fonts and colors. This allows for the generation of LPs optimized for the target audience, maximizing the effectiveness of the campaign.

[0088] The reception desk can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is nervous, it can provide a simple and intuitive interface. If the user is relaxed, it can provide an interface that allows for detailed information input. If the user is in a hurry, it can provide an interface that requires only minimal information input. By adjusting the input interface according to the user's emotions, a smoother input experience can be provided. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0089] The generation unit can optimize landing pages (LPs) by considering past user feedback during the generation process. For example, it can generate LPs that reflect improvements previously pointed out by users. Furthermore, it can generate LPs that incorporate elements that users have previously given high ratings to. It can also analyze past user feedback, extract common areas for improvement, and improve overall quality. This allows for the generation of more satisfying LPs by leveraging past user feedback.

[0090] The output unit can estimate the user's emotions and adjust the font size of the landing page based on the estimated emotions. For example, if the user is relaxed, a standard font size is used. If the user is stressed, a larger font size can be used to improve readability. Also, if the user is in a hurry, the font size can be increased only for specific parts to emphasize key points. This allows for more effective information transmission by adjusting the font size according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0091] The generation unit can automatically apply the user's brand guidelines during the generation process. For example, it can generate landing pages (LPs) that automatically incorporate the user's brand colors and fonts. Furthermore, it can select text and images based on the user's brand tone and style guide. It can also generate consistent LPs by placing the user's brand logo in the appropriate position. This allows for the rapid generation of LPs that comply with the user's brand guidelines.

[0092] The reception desk can estimate the user's emotions and adjust the input verification process based on those emotions. For example, if the user is stressed, a simplified verification process can be provided. If the user is relaxed, a more detailed verification process can be offered. Furthermore, if the user is in a hurry, the system can verify the input with the fewest possible steps. This allows for a smoother input experience by adjusting the verification process according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0093] The generation unit can analyze the user's competitors' landing pages (LPs) during the generation process and create differentiated LPs. For example, it can analyze the design and content of competitors and propose a unique design. Furthermore, it can consider the strengths and weaknesses of competitors and incorporate the most suitable elements for the user's LP. It can also refer to competitors' campaign strategies and propose the most suitable approach for the user's campaign. This allows for differentiation from competitors and the creation of more effective LPs.

[0094] The output unit can estimate the user's emotions and adjust the interactive elements of the landing page (LP) based on the estimated emotions. For example, if the user is relaxed, it can provide an LP with detailed interactive elements. If the user is tense, it can provide simple and intuitive interactive elements. If the user is in a hurry, it can provide an LP that conveys the main points with minimal interactive elements. This allows for more effective information delivery by adjusting the interactive elements according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, among other methods.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The reception desk inputs reference past landing pages, landing pages that indicate the desired style, and an overview of the campaign. For example, the user can input "examples of past successful campaign landing pages" and "an overview of the current campaign" to the generation AI. Step 2: The generation unit analyzes the information entered by the reception unit and generates the landing page (LP). The generation unit uses a generation AI to generate not only text information but also images and videos. For example, the generation AI automatically generates an LP that matches the desired style based on examples of past successful campaign LPs and an overview of the current campaign. Step 3: The output unit outputs the LP generated by the generation unit. For example, it provides the generated LP to the user. The output unit can output the generated LP in formats such as PDF, web page, or printable data.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements described above, including the reception unit, generation unit, and output unit, is implemented, for example, by 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, where the user inputs examples of past successful campaign LPs and an overview of the current campaign. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, where it analyzes the input information using generation AI and automatically generates an LP that matches the desired taste. The output unit is implemented, for example, by the output device 40 of the smart device 14, where the generated LP is provided to the user in formats such as PDF, web page, or printable data. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, generation unit, and output 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, where the user inputs examples of past successful campaign LPs or an overview of the current campaign by voice. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and automatically generates an LP that matches the desired taste. The output unit is implemented, for example, by the speaker 240 of the smart glasses 214, where the generated LP is provided to the user by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, generation unit, and output unit, is implemented, for example, by 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, where the user inputs examples of past successful campaign LPs or an overview of the current campaign by voice. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and automatically generates an LP that matches the desired taste. The output unit is implemented, for example, by the display 343 of the headset terminal 314, where the generated LP is visually provided to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, generation unit, and output 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, where the user inputs examples of past successful campaign LPs or an overview of the current campaign by voice. The generation unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, which analyzes the input information using a generation AI and automatically generates an LP that matches the desired taste. The output unit is implemented by, for example, the speaker 240 of the robot 414, where the generated LP is provided to the user by voice. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The reception area allows you to input past landing pages for reference, landing pages that reflect your desired style, and an overview of the campaign. A generation unit analyzes the information input by the reception unit and generates LP, The system comprises an output unit that outputs the LP generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is The generation AI generates not only text information, but also images and videos. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is The user inputs "examples of past successful campaign landing pages" and "an overview of the current campaign" into the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The AI ​​generates landing pages that match your desired style. The system described in Appendix 1, characterized by the features described herein. (Note 5) The output unit is, Provide the generated landing page to the user. 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 priority of input content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When inputting information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts how the input content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When users input data, the system prioritizes retrieving highly relevant information based on their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is During input, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is We estimate the user's emotions and adjust the way the LP is presented 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 generation algorithm is optimized by referring to past successful examples. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, different generation algorithms are applied depending on the campaign category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is It estimates the user's emotions and adjusts the length of the generated landing page based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is During generation, the generation priority is determined based on the campaign submission date. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, adjust the generation order based on the relevance of the campaign. The system described in Appendix 1, characterized by the features described herein. (Note 18) The output unit is, We estimate the user's emotions and adjust the display method of the landing page based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The output unit is, When outputting data, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The output unit is, During output, the quality of the generated LP is evaluated, and corrections are suggested as needed. The system described in Appendix 1, characterized by the features described herein. (Note 21) The output unit is, We estimate the user's emotions and adjust the landing page's operation procedures based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The output unit is, When outputting, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The output unit is, During output, the accuracy of the output is improved by referring to related literature for the generated LP. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area allows you to input past landing pages for reference, landing pages that reflect your desired style, and an overview of the campaign. A generation unit analyzes the information input by the reception unit and generates LP, The system comprises an output unit that outputs the LP generated by the generation unit. A system characterized by the following features.

2. The generating unit is The AI ​​generates not only text information but also images and videos. The system according to feature 1.

3. The aforementioned reception unit is The user inputs examples of past successful campaign landing pages and an overview of the current campaign into the generating AI. The system according to feature 1.

4. The generating unit is The AI ​​generates landing pages that match your desired style. The system according to feature 1.

5. The output unit is, Provide the generated landing page to the user. The system according to feature 1.

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

7. The aforementioned reception unit is We analyze past input history and suggest the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When inputting information, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

Images