system

Abstract

An object of the system according to the embodiment is to quickly generate a high-quality landing page or blog article based on a description or specification of a product and apply an SEO countermeasure.SOLUTION: A system includes an input unit, a generation unit, and an optimization unit. The input unit receives a description and specifications of a product from a user. The generation part generates a landing page and a related blog article on the basis of the description and specifications of the article received by the input part. The optimization unit applies an SEO countermeasure to the landing page and the related blog article generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have the drawback of making it difficult to quickly generate high-quality landing pages and blog posts based on product descriptions and specifications and apply SEO strategies.

[0005] The system according to the embodiment aims to quickly generate high-quality landing pages and blog articles based on product descriptions and specifications, and to apply SEO measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, a generation unit, and an optimization unit. The input unit receives product descriptions and specifications from a user. The generation unit generates landing pages and related blog articles based on the product descriptions and specifications received by the input unit. The optimization unit applies SEO measures to the landing pages and related blog articles generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly generate high-quality landing pages and blog posts based on product descriptions and specifications, and apply SEO strategies. [Brief explanation of the drawings]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

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

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

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

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

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

[0028] (Example 1) The automatic generation system according to the embodiment of the present invention generates optimal landing pages and related blog articles for a product by simply inputting the product description and specifications, and applies SEO measures to the product. This allows the automatic generation system to save the user a lot of time and effort and quickly publish higher quality content.

[0029] An automatic generation system according to an embodiment includes an input unit, a generation unit, and an optimization unit. The input unit receives product descriptions and specifications from a user. For example, the user inputs the features, specifications, and price of a new smartphone. The generation unit generates landing pages and related blog posts based on the product descriptions and specifications received by the input unit. For example, the generation AI generates a landing page based on prompts containing the product descriptions and specifications, including a design that highlights the smartphone's features and the placement of a purchase button. The generation AI also generates related blog posts, such as smartphone usage guides, reviews, and comparison articles. The optimization unit applies SEO measures to the landing pages and related blog posts generated by the generation unit. For example, the generation AI selects appropriate keywords, sets meta tags, and optimizes internal links. This improves the ranking of the generated content in search engines and attracts more visitors. This allows the automatic generation system according to an embodiment to save users a lot of time and effort and quickly publish higher-quality content. For example, the user can review the landing pages and blog posts generated by the generation AI, make any necessary edits, and then publish them on their own blog or website. This allows users to quickly provide high-quality content.

[0030] The input unit can analyze user input in real time and provide auto-completion and suggestions based on the input. For example, when a user inputs smartphone specifications, the generation AI analyzes the input in real time and auto-completion of missing fields. For example, it automatically suggests camera resolution and battery capacity. When a user inputs a description of a new home appliance, the generation AI refers to past data and suggests descriptions of similar products. For example, it automatically completes information about a refrigerator's energy efficiency and capacity. When a user inputs the specifications of a fashion item, the generation AI analyzes trend information and suggests the latest fashion terms and styles. For example, it automatically completes details about materials and designs. This reduces the effort required for input by analyzing user input in real time and providing auto-completion and suggestions, enabling information to be provided efficiently.

[0031] The input unit can refer to a database of similar products from the past and present optimal input examples for the product description and specifications entered by the user. For example, when a user enters a description of a new smartphone, the generation AI refers to a database of past smartphones and presents optimal input examples. For example, it refers to descriptions of popular smartphones. When a user enters the specifications of a new home appliance, the generation AI refers to a database of past home appliances and presents optimal input examples. For example, it provides information on the functions and features of air conditioners. When a user enters a description of a new fashion item, the generation AI refers to a database of past fashion items and presents optimal input examples. For example, it provides information on the design and material of a dress. In this way, by referring to a database of similar products from the past and presenting optimal input examples, the user can enter more effective product descriptions and specifications.

[0032] The input unit can input product descriptions and specifications using voice input and image recognition technology, allowing users to provide information more intuitively. For example, when a user uses voice input to describe a new smartphone, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the specs and features via voice. In addition, when a user uses image recognition technology to input the specifications of a new fashion item, the generation AI analyzes the image and converts it into text. For example, simply uploading a photo of a dress automatically inputs the material and design. In addition, when a user uses voice input to describe a new home appliance, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the functions and features via voice. In this way, by using voice input and image recognition technology, users can input product descriptions and specifications more intuitively.

[0033] The input unit supports input in different languages, making it possible to provide an input interface that can accommodate international users. For example, when a user inputs a description of a new smartphone in a different language, the generation AI performs automatic translation and analyzes the input content. For example, a description input in Japanese is translated into English and analyzed. In addition, when a user inputs specifications of a new fashion item in a different language, the generation AI performs automatic translation and analyzes the input content. For example, a description input in French is translated into English and analyzed. In addition, when a user inputs a description of a new home appliance in a different language, the generation AI performs automatic translation and analyzes the input content. For example, a description input in Chinese is translated into English and analyzed. This allows for input in different languages ​​to be supported, making it possible to provide an input interface that can accommodate international users.

[0034] The generation unit can learn data from the user's past landing pages and customize them to suit the user's preferences. For example, the generation AI of the generation unit learns data from the user's past landing pages and proposes designs and layouts that suit the user's preferences. For example, it automatically applies colors and fonts that were used in the past. The generation unit also learns data from the user's past landing pages and proposes content placement that suits the user's preferences. For example, it automatically applies the order of sections that were used in the past. The generation unit also learns data from the user's past landing pages and proposes the selection of images and videos that suit the user's preferences. For example, it automatically applies media that was used in the past. In this way, the generation AI can learn data from the user's past landing pages and customize them to suit the user's preferences, thereby generating more effective landing pages.

[0035] The generation unit can analyze competitors' landing pages and, based on that, generate a landing page that is optimal for the user's product. For example, the generation AI in the generation unit analyzes competitors' landing pages and proposes the optimal design and layout for the user's product. For example, it refers to competitors' success stories. The generation unit also analyzes competitors' landing pages and proposes the optimal content placement for the user's product. For example, it refers to effective section placements of competitors. The generation unit also analyzes competitors' landing pages and proposes the optimal image and video selection for the user's product. For example, it refers to attractive media from competitors. In this way, by analyzing competitors' landing pages and generating the optimal landing page based on that, it is possible to provide competitive content.

[0036] The generation unit can automatically generate landing pages optimized for different devices (smartphones, tablets, PCs). In the generation unit, for example, the generation AI automatically generates a landing page optimized for smartphones. For example, it proposes a design and layout that matches the screen size of a smartphone. In addition, the generation unit automatically generates a landing page optimized for tablets. For example, it proposes a design and layout that matches the screen size of a tablet. In addition, the generation unit automatically generates a landing page optimized for PCs. For example, it proposes a design and layout that matches the screen size of a PC. In this way, by automatically generating landing pages optimized for different devices, users can be provided with content that is compatible with a variety of devices.

[0037] The generation unit can link with the user's social media accounts to generate landing pages for social media. For example, the generation AI of the generation unit links with the user's Facebook account to generate a landing page for Facebook. For example, it proposes a design and layout that matches the Facebook format. Furthermore, the generation unit links with the user's Instagram account to generate a landing page for Instagram. For example, it proposes a design and layout that matches the Instagram format. Furthermore, the generation unit links with the user's Twitter account to generate a landing page for Twitter. For example, it proposes a design and layout that matches the Twitter format. In this way, by generating a landing page for social media, the user can effectively advertise their products on social media.

[0038] The generation unit can learn from the user's past blog posts and generate articles that match the user's writing style and tone. For example, the generation unit uses a generation AI to learn from the user's past blog posts and generate new articles that match the user's writing style and tone. For example, it refers to the wording and expressions in past posts to create articles with a consistent feel. The generation unit also uses a generation AI to learn from the user's past blog posts and generate articles that match the user's writing style and tone. For example, it creates articles on new topics while maintaining the style of past posts. The generation unit also uses a generation AI to learn from the user's past blog posts and generate articles that match the user's writing style and tone. For example, it refers to the structure and paragraph arrangement of past posts to create articles that are easy to read. In this way, it is possible to provide content with a consistent feel by learning from the user's past blog posts and generating articles that match the writing style and tone.

[0039] The generation unit can refer to the latest industry news and trends and generate related blog posts based on them. For example, the generation AI collects the latest industry news and generates related blog posts based on it. For example, it creates articles about the latest technology trends and market changes. The generation unit also collects the latest trend information and generates related blog posts based on it. For example, it creates articles about popular products and services. The generation unit also refers to the latest industry news and trends and generates related blog posts based on them. For example, it creates articles that incorporate the opinions of industry leaders and influencers. This makes it possible to provide timely and relevant content by referring to the latest industry news and trends and generating related blog posts based on them.

[0040] The generation unit can generate content compatible with different media formats (videos, podcasts) and link them with blog articles. In the generation unit, for example, the generation AI generates video content related to a blog article and links it with the article. For example, it creates an explanatory video or demo video that complements the content of the article. In addition, the generation unit can generate podcasts related to a blog article and link them with the article. For example, it can create a podcast that explains the content of the article in audio. In addition, the generation unit can generate infographics related to the blog article and link them with the article. For example, it can create graphics that visually represent the content of the article. In this way, by generating content compatible with different media formats and linking it with blog articles, it is possible to provide readers with a variety of means of providing information.

[0041] The generation unit can link with the user's social media account and generate short articles and posts for social media. For example, the generation AI in the generation unit links with the user's Twitter account and generates short tweets based on the content of a blog article. For example, it creates a tweet that summarizes the main points of the article in 140 characters or less. The generation unit also links with the user's Facebook account and generates posts based on the content of the blog article. For example, it creates a post that includes an outline of the article and a link. The generation unit also links with the user's Instagram account and generates posts with images based on the content of the blog article. For example, it creates an image and caption that visually represents the content of the article. In this way, by generating short articles and posts for social media, users can effectively disseminate information on social media.

[0042] The optimization unit can analyze the SEO strategies of competing sites and, based on that, propose optimal SEO measures. For example, the optimization unit uses a generation AI to analyze the SEO strategies of competing sites and propose the optimal keywords and meta tags for the user's site. For example, it refers to effective keywords used on competing sites. The optimization unit also uses a generation AI to analyze the SEO strategies of competing sites and propose the optimal internal link placement for the user's site. For example, it refers to the link structure effectively used on competing sites. The optimization unit also uses a generation AI to analyze the SEO strategies of competing sites and propose the optimal content update frequency for the user's site. For example, it refers to the content update patterns that are effective on competing sites. In this way, by analyzing the SEO strategies of competing sites and proposing optimal SEO measures based on that, the search engine ranking of the user's site can be improved.

[0043] The optimization unit can automatically generate SEO measures optimized for different search engines (Google, Bing, Yahoo). In the optimization unit, for example, a generation AI automatically generates SEO measures optimized for Google. For example, it suggests keywords and meta tags based on Google's algorithm. In addition, the optimization unit automatically generates SEO measures optimized for Bing. For example, it suggests keywords and meta tags based on Bing's algorithm. In addition, the optimization unit automatically generates SEO measures optimized for Yahoo. For example, it suggests keywords and meta tags based on Yahoo's algorithm. In this way, by automatically generating SEO measures optimized for different search engines, rankings on multiple search engines are improved.

[0044] The optimization unit can work in conjunction with the user's social media accounts to propose SEO measures for social media. For example, the optimization unit's generation AI works in conjunction with the user's Twitter account to propose SEO measures for Twitter. For example, it optimizes keywords and hashtags for tweets. The optimization unit's generation AI also works in conjunction with the user's Facebook account to propose SEO measures for Facebook. For example, it optimizes keywords and meta tags for posts. The optimization unit's generation AI also works in conjunction with the user's Instagram account to propose SEO measures for Instagram. For example, it optimizes keywords and hashtags for posts. In this way, by proposing SEO measures for social media, the search engine ranking on social media is improved.

[0045] The optimization unit can learn the user's publishing schedule and suggest the optimal publishing timing. In this optimization unit, for example, the generation AI learns the user's past publishing schedule and suggests the optimal publishing timing. For example, it sets a new publishing timing based on time periods that have shown high performance in the past. The optimization unit also learns the user's publishing schedule and suggests the optimal publishing timing. For example, publishing on specific days of the week or time periods can maximize the number of visitors. The optimization unit also learns the user's publishing schedule and suggests the optimal publishing timing. For example, publishing in conjunction with specific events or campaigns can maximize effectiveness. In this way, the effectiveness of content can be maximized by learning the user's publishing schedule and suggesting the optimal publishing timing.

[0046] The optimization unit can analyze a user's past publishing performance and suggest the optimal publishing platform. In the optimization unit, for example, the generation AI analyzes a user's past publishing performance and suggests the optimal publishing platform. For example, it selects a new publishing destination based on a platform that has performed well in the past. In addition, the optimization unit analyzes a user's publishing performance and suggests the optimal publishing platform. For example, it selects a publishing destination based on the number of visitors and engagement rate on a particular platform. In addition, the optimization unit analyzes a user's publishing performance and suggests the optimal publishing platform. For example, it selects a platform that is suitable for a particular content type. In this way, the effectiveness of content can be maximized by analyzing a user's past publishing performance and suggesting the optimal publishing platform.

[0047] The optimization unit can propose publishing methods optimized for different platforms (blogs, SNS, email newsletters). For example, the optimization unit uses the generation AI to propose a publishing method optimized for blogs. For example, it proposes content structure and layout that matches the blog format. The optimization unit also uses the generation AI to propose a publishing method optimized for SNS. For example, it proposes the selection of short sentences and images that match the SNS format. The optimization unit also uses the generation AI to propose a publishing method optimized for email newsletters. For example, it proposes content structure and layout that matches the email format. In this way, by proposing publishing methods optimized for different platforms, the effectiveness of the content on each platform can be maximized.

[0048] The optimization unit can learn the behavior patterns of a user's target audience and suggest the optimal publication timing. For example, the optimization unit's generation AI learns the behavior patterns of a user's target audience and suggests the optimal publication timing. For example, it sets the publication timing based on the time of day when the audience is most active. The optimization unit also learns the behavior patterns of the target audience and suggests the optimal publication timing. For example, publishing on a specific day of the week or time of day maximizes audience engagement. The optimization unit also learns the behavior patterns of the target audience and suggests the optimal publication timing. For example, publishing in conjunction with a specific event or campaign attracts the audience's attention. In this way, the effectiveness of the content can be maximized by learning the behavior patterns of the user's target audience and suggesting the optimal publication timing.

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

[0050] The input unit can refer to a database of similar products from the past to present optimal input examples for the product description and specifications entered by the user. For example, when a user enters a description of a new smartphone, the generation AI refers to a database of past smartphones to present optimal input examples. For example, it refers to descriptions of popular smartphones. When a user enters the specifications of a new home appliance, the generation AI refers to a database of past home appliances to present optimal input examples. For example, it provides information on the functions and features of air conditioners. When a user enters a description of a new fashion item, the generation AI refers to a database of past fashion items to present optimal input examples. For example, it provides information on the design and material of a dress. In this way, by referring to a database of similar products from the past and presenting optimal input examples, the user can enter more effective product descriptions and specifications.

[0051] The input unit can analyze user input in real time and provide auto-completion and suggestions based on the input. For example, when a user enters the specifications of a smartphone, the generation AI analyzes in real time and auto-completion of missing fields. For example, it automatically suggests camera resolution and battery capacity. When a user enters a description of a new home appliance, the generation AI references past data and suggests descriptions of similar products. For example, it automatically completes information about a refrigerator's energy efficiency and capacity. When a user enters the specifications of a fashion item, the generation AI analyzes trend information and suggests the latest fashion terms and styles. For example, it automatically completes details about materials and designs. This allows the system to analyze user input in real time and provide auto-completion and suggestions, reducing the effort required for input and providing information efficiently.

[0052] The input unit can use voice input and image recognition technology to input product descriptions and specifications, allowing users to provide information more intuitively. For example, when a user uses voice input to describe a new smartphone, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the specs and features via voice. In addition, when a user uses image recognition technology to input the specifications of a new fashion item, the generation AI analyzes the image and converts it into text. For example, simply uploading a photo of a dress automatically inputs the material and design. In addition, when a user uses voice input to describe a new home appliance, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the functions and features via voice. In this way, using voice input and image recognition technology allows users to input product descriptions and specifications more intuitively.

[0053] The input unit supports input in different languages, allowing for an input interface that can accommodate international users. For example, when a user inputs a description of a new smartphone in a different language, the generation AI automatically translates and analyzes the input content. For example, a description input in Japanese may be translated into English and analyzed. In addition, when a user inputs specifications for a new fashion item in a different language, the generation AI automatically translates and analyzes the input content. For example, a description input in French may be translated into English and analyzed. In addition, when a user inputs a description of a new home appliance in a different language, the generation AI automatically translates and analyzes the input content. For example, a description input in Chinese may be translated into English and analyzed. This allows for input in different languages, allowing for an input interface that can accommodate international users.

[0054] The generation unit learns data from the user's past landing pages and can customize them to suit the user's preferences. For example, the generation AI learns data from the user's past landing pages and suggests designs and layouts that suit the user's preferences. For example, it automatically applies colors and fonts that were used in the past. The generation unit also learns data from the user's past landing pages and suggests content placement that suits the user's preferences. For example, it automatically applies the order of sections that was used in the past. The generation unit also learns data from the user's past landing pages and suggests the selection of images and videos that suit the user's preferences. For example, it automatically applies media that was used in the past. In this way, the generation AI learns data from the user's past landing pages and customizes them to suit the user's preferences, making it possible to generate more effective landing pages.

[0055] The generation unit can analyze competitors' landing pages and, based on that, generate a landing page that is optimal for the user's product. For example, the generation AI analyzes competitors' landing pages and proposes the optimal design and layout for the user's product. For example, it refers to competitors' success stories. The generation unit also analyzes competitors' landing pages and proposes the optimal content placement for the user's product. For example, it refers to competitors' effective section placements. The generation unit also analyzes competitors' landing pages and proposes the optimal image and video selection for the user's product. For example, it refers to competitors' attractive media. This makes it possible to provide competitive content by analyzing competitors' landing pages and generating the optimal landing page based on them.

[0056] The generation unit can learn from a user's past blog posts and generate articles that match the user's writing style and tone. For example, the generation AI learns from a user's past blog posts and generates new articles that match the user's writing style and tone. For example, it refers to the wording and expressions in past posts to create articles with a consistent feel. The generation unit also learns from a user's past blog posts and generates articles that match the user's writing style and tone. For example, it creates articles on new topics while maintaining the style of past posts. The generation unit also learns from a user's past blog posts and generates articles that match the user's writing style and tone. For example, it refers to the structure and paragraph arrangement of past posts to create articles that are easy to read. In this way, it is possible to provide content with a consistent feel by learning from a user's past blog posts and generating articles that match the writing style and tone.

[0057] The generation unit can refer to the latest industry news and trends and generate related blog posts based on them. For example, the generation AI collects the latest industry news and generates related blog posts based on it. For example, it creates articles about the latest technological trends and market changes. The generation unit also collects the latest trend information and generates related blog posts based on it. For example, it creates articles about popular products and services. The generation unit also refers to the latest industry news and trends and generates related blog posts based on them. For example, it creates articles that incorporate the opinions of industry leaders and influencers. This makes it possible to provide timely and relevant content by referring to the latest industry news and trends and generating related blog posts based on them.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The input unit accepts product descriptions and specifications from the user. For example, the user inputs the features, specs, and price of a new smartphone. Step 2: The generation unit generates landing pages and related blog articles based on the product description and specifications received by the input unit. For example, based on prompts including the product description and specifications, the generation AI generates a landing page that includes a design that highlights the smartphone's features and the placement of a purchase button. The generation AI also generates related blog articles such as smartphone usage guides, reviews, and comparison articles. Step 3: The optimization part applies SEO measures to the landing pages and related blog posts generated by the generation part. For example, the generation AI selects appropriate keywords, sets meta tags, and optimizes internal links. This improves the ranking of the generated content in search engines and attracts more visitors.

[0060] (Example 2) The automatic generation system according to the embodiment of the present invention generates optimal landing pages and related blog articles for a product by simply inputting the product description and specifications, and applies SEO measures to the product. This allows the automatic generation system to save the user a lot of time and effort and quickly publish higher quality content.

[0061] An automatic generation system according to an embodiment includes an input unit, a generation unit, and an optimization unit. The input unit receives product descriptions and specifications from a user. For example, the user inputs the features, specifications, and price of a new smartphone. The generation unit generates landing pages and related blog posts based on the product descriptions and specifications received by the input unit. For example, the generation AI generates a landing page based on prompts containing the product descriptions and specifications, including a design that highlights the smartphone's features and the placement of a purchase button. The generation AI also generates related blog posts, such as smartphone usage guides, reviews, and comparison articles. The optimization unit applies SEO measures to the landing pages and related blog posts generated by the generation unit. For example, the generation AI selects appropriate keywords, sets meta tags, and optimizes internal links. This improves the ranking of the generated content in search engines and attracts more visitors. This allows the automatic generation system according to an embodiment to save users a lot of time and effort and quickly publish higher-quality content. For example, the user can review the landing pages and blog posts generated by the generation AI, make any necessary edits, and then publish them on their own blog or website. This allows users to quickly provide high-quality content.

[0062] The input unit can analyze user input in real time and provide auto-completion and suggestions based on the input. For example, when a user inputs smartphone specifications, the generation AI analyzes the input in real time and auto-completion of missing fields. For example, it automatically suggests camera resolution and battery capacity. When a user inputs a description of a new home appliance, the generation AI refers to past data and suggests descriptions of similar products. For example, it automatically completes information about a refrigerator's energy efficiency and capacity. When a user inputs the specifications of a fashion item, the generation AI analyzes trend information and suggests the latest fashion terms and styles. For example, it automatically completes details about materials and designs. This reduces the effort required for input by analyzing user input in real time and providing auto-completion and suggestions, enabling information to be provided efficiently.

[0063] The input unit can refer to a database of similar products from the past and present optimal input examples for the product description and specifications entered by the user. For example, when a user enters a description of a new smartphone, the generation AI refers to a database of past smartphones and presents optimal input examples. For example, it refers to descriptions of popular smartphones. When a user enters the specifications of a new home appliance, the generation AI refers to a database of past home appliances and presents optimal input examples. For example, it provides information on the functions and features of air conditioners. When a user enters a description of a new fashion item, the generation AI refers to a database of past fashion items and presents optimal input examples. For example, it provides information on the design and material of a dress. In this way, by referring to a database of similar products from the past and presenting optimal input examples, the user can enter more effective product descriptions and specifications.

[0064] The input unit can use the emotion estimation function to analyze the user's emotions when inputting information and provide an input guide to elicit positive emotions. For example, when a user inputs a description of a new gadget, the emotion estimation function analyzes the user's emotions and provides a guide to elicit positive emotions. For example, the input unit presents encouraging messages and success stories. Furthermore, when a user inputs specifications for a new fashion item, the emotion estimation function analyzes the user's emotions and provides a guide to elicit positive emotions. For example, the input unit suggests trend information and styles. Furthermore, when a user inputs a description of a new home appliance, the emotion estimation function analyzes the user's emotions and provides a guide to elicit positive emotions. For example, the input unit highlights the product's advantages and user satisfaction. This improves the user's input experience by analyzing the user's emotions and providing an input guide to elicit positive emotions.

[0065] The input unit can input product descriptions and specifications using voice input and image recognition technology, allowing users to provide information more intuitively. For example, when a user uses voice input to describe a new smartphone, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the specs and features via voice. In addition, when a user uses image recognition technology to input the specifications of a new fashion item, the generation AI analyzes the image and converts it into text. For example, simply uploading a photo of a dress automatically inputs the material and design. In addition, when a user uses voice input to describe a new home appliance, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the functions and features via voice. In this way, by using voice input and image recognition technology, users can input product descriptions and specifications more intuitively.

[0066] The input unit supports input in different languages, making it possible to provide an input interface that can accommodate international users. For example, when a user inputs a description of a new smartphone in a different language, the generation AI performs automatic translation and analyzes the input content. For example, a description input in Japanese is translated into English and analyzed. In addition, when a user inputs specifications of a new fashion item in a different language, the generation AI performs automatic translation and analyzes the input content. For example, a description input in French is translated into English and analyzed. In addition, when a user inputs a description of a new home appliance in a different language, the generation AI performs automatic translation and analyzes the input content. For example, a description input in Chinese is translated into English and analyzed. This allows for input in different languages ​​to be supported, making it possible to provide an input interface that can accommodate international users.

[0067] The input unit uses the emotion estimation function to provide real-time feedback on the emotions of the user when inputting information, thereby optimizing the input content based on the emotions. For example, when a user inputs a description of a new gadget, the emotion estimation function provides real-time feedback on the user's emotions and optimizes the input content. For example, suggestions are made to elicit positive emotions. Furthermore, when a user inputs specifications for a new fashion item, the emotion estimation function provides real-time feedback on the user's emotions and optimizes the input content. For example, trend information and style suggestions are made. Furthermore, when a user inputs a description of a new home appliance, the emotion estimation function provides real-time feedback on the user's emotions and optimizes the input content. For example, the advantages of the product and the user's satisfaction are emphasized. In this way, the user's input experience is improved by providing real-time feedback on the user's emotions and optimizing the input content based on the emotions.

[0068] The generation unit can learn data from the user's past landing pages and customize them to suit the user's preferences. For example, the generation AI of the generation unit learns data from the user's past landing pages and proposes designs and layouts that suit the user's preferences. For example, it automatically applies colors and fonts that were used in the past. The generation unit also learns data from the user's past landing pages and proposes content placement that suits the user's preferences. For example, it automatically applies the order of sections that were used in the past. The generation unit also learns data from the user's past landing pages and proposes the selection of images and videos that suit the user's preferences. For example, it automatically applies media that was used in the past. In this way, the generation AI can learn data from the user's past landing pages and customize them to suit the user's preferences, thereby generating more effective landing pages.

[0069] The generation unit can analyze competitors' landing pages and, based on that, generate a landing page that is optimal for the user's product. For example, the generation AI in the generation unit analyzes competitors' landing pages and proposes the optimal design and layout for the user's product. For example, it refers to competitors' success stories. The generation unit also analyzes competitors' landing pages and proposes the optimal content placement for the user's product. For example, it refers to effective section placements of competitors. The generation unit also analyzes competitors' landing pages and proposes the optimal image and video selection for the user's product. For example, it refers to attractive media from competitors. In this way, by analyzing competitors' landing pages and generating the optimal landing page based on that, it is possible to provide competitive content.

[0070] The generation unit can automatically generate landing pages optimized for different devices (smartphones, tablets, PCs). In the generation unit, for example, the generation AI automatically generates a landing page optimized for smartphones. For example, it proposes a design and layout that matches the screen size of a smartphone. In addition, the generation unit automatically generates a landing page optimized for tablets. For example, it proposes a design and layout that matches the screen size of a tablet. In addition, the generation unit automatically generates a landing page optimized for PCs. For example, it proposes a design and layout that matches the screen size of a PC. In this way, by automatically generating landing pages optimized for different devices, users can be provided with content that is compatible with a variety of devices.

[0071] The generation unit can link with the user's social media accounts to generate landing pages for social media. For example, the generation AI of the generation unit links with the user's Facebook account to generate a landing page for Facebook. For example, it proposes a design and layout that matches the Facebook format. Furthermore, the generation unit links with the user's Instagram account to generate a landing page for Instagram. For example, it proposes a design and layout that matches the Instagram format. Furthermore, the generation unit links with the user's Twitter account to generate a landing page for Twitter. For example, it proposes a design and layout that matches the Twitter format. In this way, by generating a landing page for social media, the user can effectively advertise their products on social media.

[0072] The generation unit can use the emotion estimation function to monitor visitors' emotional responses to the landing page generated by the user in real time and continuously suggest optimal pages. For example, the generation unit uses a generation AI to monitor visitors' emotional responses to the landing page in real time and continuously suggest optimal designs and layouts. For example, the page is adjusted based on the visitor's emotional data. The generation unit also uses a generation AI to monitor visitors' emotional responses to the landing page in real time and continuously suggest optimal content placements. For example, the order of sections is adjusted based on the visitor's emotional data. The generation unit also uses a generation AI to monitor visitors' emotional responses to the landing page in real time and continuously suggest optimal image and video selections. For example, the generation AI selects media based on the visitor's emotional data. In this way, the generation unit improves visitor engagement by monitoring visitors' emotional responses in real time and continuously suggesting optimal pages.

[0073] The generation unit can learn from the user's past blog posts and generate articles that match the user's writing style and tone. For example, the generation unit uses a generation AI to learn from the user's past blog posts and generate new articles that match the user's writing style and tone. For example, it refers to the wording and expressions in past posts to create articles with a consistent feel. The generation unit also uses a generation AI to learn from the user's past blog posts and generate articles that match the user's writing style and tone. For example, it creates articles on new topics while maintaining the style of past posts. The generation unit also uses a generation AI to learn from the user's past blog posts and generate articles that match the user's writing style and tone. For example, it refers to the structure and paragraph arrangement of past posts to create articles that are easy to read. In this way, it is possible to provide content with a consistent feel by learning from the user's past blog posts and generating articles that match the writing style and tone.

[0074] The generation unit can refer to the latest industry news and trends and generate related blog posts based on them. For example, the generation AI collects the latest industry news and generates related blog posts based on it. For example, it creates articles about the latest technology trends and market changes. The generation unit also collects the latest trend information and generates related blog posts based on it. For example, it creates articles about popular products and services. The generation unit also refers to the latest industry news and trends and generates related blog posts based on them. For example, it creates articles that incorporate the opinions of industry leaders and influencers. This makes it possible to provide timely and relevant content by referring to the latest industry news and trends and generating related blog posts based on them.

[0075] The generation unit can use the emotion estimation function to analyze the emotional reactions to blog posts generated by users and suggest content that elicits positive emotions. For example, the generation unit uses a generation AI to analyze readers' emotional reactions to blog posts and suggest content that elicits positive emotions. For example, the generation unit adjusts the tone and expression of the post based on the reader's emotional data. The generation unit also analyzes readers' emotional reactions to blog posts and makes specific suggestions to elicit positive emotions. For example, the generation AI incorporates topics and keywords that readers like into the post. The generation unit also analyzes readers' emotional reactions to blog posts and provides feedback to elicit positive emotions. For example, the generation AI improves the structure and content of the post based on the reader's emotional data. This allows the generation AI to analyze readers' emotional reactions to blog posts generated by users and suggest content that elicits positive emotions, thereby improving reader engagement.

[0076] The generation unit can generate content compatible with different media formats (videos, podcasts) and link them with blog articles. In the generation unit, for example, the generation AI generates video content related to a blog article and links it with the article. For example, it creates an explanatory video or demo video that complements the content of the article. In addition, the generation unit can generate podcasts related to a blog article and link them with the article. For example, it can create a podcast that explains the content of the article in audio. In addition, the generation unit can generate infographics related to the blog article and link them with the article. For example, it can create graphics that visually represent the content of the article. In this way, by generating content compatible with different media formats and linking it with blog articles, it is possible to provide readers with a variety of means of providing information.

[0077] The generation unit can link with the user's social media account and generate short articles and posts for social media. For example, the generation AI in the generation unit links with the user's Twitter account and generates short tweets based on the content of a blog article. For example, it creates a tweet that summarizes the main points of the article in 140 characters or less. The generation unit also links with the user's Facebook account and generates posts based on the content of the blog article. For example, it creates a post that includes an outline of the article and a link. The generation unit also links with the user's Instagram account and generates posts with images based on the content of the blog article. For example, it creates an image and caption that visually represents the content of the article. In this way, by generating short articles and posts for social media, users can effectively disseminate information on social media.

[0078] The generation unit uses the emotion estimation function to monitor readers' emotional reactions to blog articles generated by users in real time and continuously suggest optimal articles. For example, the generation unit uses a generation AI to monitor readers' emotional reactions to blog articles in real time and continuously suggest optimal articles. For example, the generation unit adjusts the topic and content of the article based on the reader's emotional data. The generation unit also uses a generation AI to monitor readers' emotional reactions to blog articles in real time and make specific suggestions to elicit positive emotions. For example, the generation unit incorporates styles and expressions preferred by readers into the article. The generation unit also uses a generation AI to monitor readers' emotional reactions to blog articles in real time and provide feedback to continuously improve optimal articles. For example, the generation unit improves the structure and content of the article based on the reader's emotional data. In this way, the generation unit improves reader engagement by monitoring readers' emotional reactions in real time and continuously suggesting optimal articles.

[0079] The optimization unit can analyze the SEO strategies of competing sites and, based on that, propose optimal SEO measures. For example, the optimization unit uses a generation AI to analyze the SEO strategies of competing sites and propose the optimal keywords and meta tags for the user's site. For example, it refers to effective keywords used on competing sites. The optimization unit also uses a generation AI to analyze the SEO strategies of competing sites and propose the optimal internal link placement for the user's site. For example, it refers to the link structure effectively used on competing sites. The optimization unit also uses a generation AI to analyze the SEO strategies of competing sites and propose the optimal content update frequency for the user's site. For example, it refers to the content update patterns that are effective on competing sites. In this way, by analyzing the SEO strategies of competing sites and proposing optimal SEO measures based on that, the search engine ranking of the user's site can be improved.

[0080] The optimization unit can use the emotion estimation function to emotionally analyze search engine evaluations of content generated by users and propose SEO measures that will elicit positive evaluations. For example, the optimization unit uses a generation AI to emotionally analyze search engine evaluations of content and propose keywords and meta tags that will elicit positive evaluations. For example, it selects expressions preferred by search engines based on the emotion data. The optimization unit also uses a generation AI to emotionally analyze search engine evaluations of content and proposes the placement of internal links that will elicit positive evaluations. For example, it designs a link structure preferred by search engines based on the emotion data. The optimization unit also uses a generation AI to emotionally analyze search engine evaluations of content and proposes the frequency of content updates that will elicit positive evaluations. For example, it sets an update pattern preferred by search engines based on the emotion data. In this way, the optimization unit emotionally analyzes search engine evaluations and proposes SEO measures that will elicit positive evaluations, thereby improving the search engine rankings.

[0081] The optimization unit can automatically generate SEO measures optimized for different search engines (Google, Bing, Yahoo). In the optimization unit, for example, a generation AI automatically generates SEO measures optimized for Google. For example, it suggests keywords and meta tags based on Google's algorithm. In addition, the optimization unit automatically generates SEO measures optimized for Bing. For example, it suggests keywords and meta tags based on Bing's algorithm. In addition, the optimization unit automatically generates SEO measures optimized for Yahoo. For example, it suggests keywords and meta tags based on Yahoo's algorithm. In this way, by automatically generating SEO measures optimized for different search engines, rankings on multiple search engines are improved.

[0082] The optimization unit can work in conjunction with the user's social media accounts to propose SEO measures for social media. For example, the optimization unit's generation AI works in conjunction with the user's Twitter account to propose SEO measures for Twitter. For example, it optimizes keywords and hashtags for tweets. The optimization unit's generation AI also works in conjunction with the user's Facebook account to propose SEO measures for Facebook. For example, it optimizes keywords and meta tags for posts. The optimization unit's generation AI also works in conjunction with the user's Instagram account to propose SEO measures for Instagram. For example, it optimizes keywords and hashtags for posts. In this way, by proposing SEO measures for social media, the search engine ranking on social media is improved.

[0083] The optimization unit can use the emotion estimation function to monitor visitors' emotional responses to user-generated content in real time and continuously suggest optimal SEO measures. In the optimization unit, for example, the generation AI monitors visitors' emotional responses to content in real time and continuously suggests optimal SEO measures. For example, it adjusts keywords and meta tags based on the visitors' emotional data. In addition, the optimization unit monitors visitors' emotional responses to content in real time and continuously suggests optimal internal link placement. For example, it adjusts the link structure based on the visitors' emotional data. In addition, the optimization unit monitors visitors' emotional responses to content in real time and continuously suggests optimal content update frequency. For example, it adjusts the update pattern based on the visitors' emotional data. In this way, the generation AI monitors visitors' emotional responses in real time and continuously suggests optimal SEO measures, thereby improving search engine rankings.

[0084] The optimization unit can learn the user's publishing schedule and suggest the optimal publishing timing. In this optimization unit, for example, the generation AI learns the user's past publishing schedule and suggests the optimal publishing timing. For example, it sets a new publishing timing based on time periods that have shown high performance in the past. The optimization unit also learns the user's publishing schedule and suggests the optimal publishing timing. For example, publishing on specific days of the week or time periods can maximize the number of visitors. The optimization unit also learns the user's publishing schedule and suggests the optimal publishing timing. For example, publishing in conjunction with specific events or campaigns can maximize effectiveness. In this way, the effectiveness of content can be maximized by learning the user's publishing schedule and suggesting the optimal publishing timing.

[0085] The optimization unit can analyze a user's past publishing performance and suggest the optimal publishing platform. In the optimization unit, for example, the generation AI analyzes a user's past publishing performance and suggests the optimal publishing platform. For example, it selects a new publishing destination based on a platform that has performed well in the past. In addition, the optimization unit analyzes a user's publishing performance and suggests the optimal publishing platform. For example, it selects a publishing destination based on the number of visitors and engagement rate on a particular platform. In addition, the optimization unit analyzes a user's publishing performance and suggests the optimal publishing platform. For example, it selects a platform that is suitable for a particular content type. In this way, the effectiveness of content can be maximized by analyzing a user's past publishing performance and suggesting the optimal publishing platform.

[0086] The optimization unit can use the emotion estimation function to analyze emotional responses to content published by users and suggest a publishing method that elicits positive emotions. The optimization unit, for example, analyzes emotional responses to content published by the generation AI and suggests a publishing method that elicits positive emotions. For example, it selects the optimal publishing timing and platform based on the emotion data. The optimization unit also analyzes emotional responses to content published by the generation AI and makes specific suggestions for eliciting positive emotions. For example, it adjusts the title and thumbnail of the content based on the emotion data. The optimization unit also analyzes emotional responses to content published by the generation AI and provides feedback to elicit positive emotions. For example, it improves the structure and content of the content based on the emotion data. In this way, the effectiveness of the content can be maximized by analyzing emotional responses to content published by users and suggesting a publishing method that elicits positive emotions.

[0087] The optimization unit can propose publishing methods optimized for different platforms (blogs, SNS, email newsletters). For example, the optimization unit uses the generation AI to propose a publishing method optimized for blogs. For example, it proposes content structure and layout that matches the blog format. The optimization unit also uses the generation AI to propose a publishing method optimized for SNS. For example, it proposes the selection of short sentences and images that match the SNS format. The optimization unit also uses the generation AI to propose a publishing method optimized for email newsletters. For example, it proposes content structure and layout that matches the email format. In this way, by proposing publishing methods optimized for different platforms, the effectiveness of the content on each platform can be maximized.

[0088] The optimization unit can learn the behavior patterns of a user's target audience and suggest the optimal publication timing. For example, the optimization unit's generation AI learns the behavior patterns of a user's target audience and suggests the optimal publication timing. For example, it sets the publication timing based on the time of day when the audience is most active. The optimization unit also learns the behavior patterns of the target audience and suggests the optimal publication timing. For example, publishing on a specific day of the week or time of day maximizes audience engagement. The optimization unit also learns the behavior patterns of the target audience and suggests the optimal publication timing. For example, publishing in conjunction with a specific event or campaign attracts the audience's attention. In this way, the effectiveness of the content can be maximized by learning the behavior patterns of the user's target audience and suggesting the optimal publication timing.

[0089] The optimization unit can use the emotion estimation function to monitor visitors' emotional responses to content published by users in real time and continuously suggest optimal publishing methods. For example, the optimization unit monitors visitors' emotional responses to content published by the generation AI in real time and continuously suggests optimal publishing methods. For example, it adjusts the publishing timing and platform based on the visitors' emotional data. The optimization unit also monitors visitors' emotional responses to content published by the generation AI in real time and makes specific suggestions to elicit positive emotions. For example, it adjusts the title and thumbnail of the content based on the visitors' emotional data. The optimization unit also monitors visitors' emotional responses to content published by the generation AI in real time and provides feedback to continuously improve the optimal publishing method. For example, it improves the structure and content of the content based on the visitors' emotional data. In this way, the effectiveness of the content can be maximized by monitoring visitors' emotional responses in real time and continuously suggesting optimal publishing methods.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The input unit can refer to a database of similar products from the past to present optimal input examples for the product description and specifications entered by the user. For example, when a user enters a description of a new smartphone, the generation AI refers to a database of past smartphones to present optimal input examples. For example, it refers to descriptions of popular smartphones. When a user enters the specifications of a new home appliance, the generation AI refers to a database of past home appliances to present optimal input examples. For example, it provides information on the functions and features of air conditioners. When a user enters a description of a new fashion item, the generation AI refers to a database of past fashion items to present optimal input examples. For example, it provides information on the design and material of a dress. In this way, by referring to a database of similar products from the past and presenting optimal input examples, the user can enter more effective product descriptions and specifications.

[0092] The input unit can analyze user input in real time and provide auto-completion and suggestions based on the input. For example, when a user enters the specifications of a smartphone, the generation AI analyzes in real time and auto-completion of missing fields. For example, it automatically suggests camera resolution and battery capacity. When a user enters a description of a new home appliance, the generation AI references past data and suggests descriptions of similar products. For example, it automatically completes information about a refrigerator's energy efficiency and capacity. When a user enters the specifications of a fashion item, the generation AI analyzes trend information and suggests the latest fashion terms and styles. For example, it automatically completes details about materials and designs. This allows the system to analyze user input in real time and provide auto-completion and suggestions, reducing the effort required for input and providing information efficiently.

[0093] The input unit can use the emotion estimation function to analyze the user's emotions when inputting information and provide input guidance to elicit positive emotions. For example, when a user inputs a description of a new gadget, the emotion estimation function analyzes the user's emotions and provides guidance to elicit positive emotions. For example, it may present encouraging messages or success stories. Furthermore, when a user inputs specifications for a new fashion item, the emotion estimation function analyzes the user's emotions and provides guidance to elicit positive emotions. For example, it may suggest trend information or styles. Furthermore, when a user inputs a description of a new home appliance, the emotion estimation function analyzes the user's emotions and provides guidance to elicit positive emotions. For example, it may highlight the product's benefits and user satisfaction. This improves the user's input experience by analyzing the user's emotions and providing input guidance to elicit positive emotions.

[0094] The input unit can use voice input and image recognition technology to input product descriptions and specifications, allowing users to provide information more intuitively. For example, when a user uses voice input to describe a new smartphone, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the specs and features via voice. In addition, when a user uses image recognition technology to input the specifications of a new fashion item, the generation AI analyzes the image and converts it into text. For example, simply uploading a photo of a dress automatically inputs the material and design. In addition, when a user uses voice input to describe a new home appliance, the generation AI analyzes the voice and converts it into text. For example, input is completed simply by describing the functions and features via voice. In this way, using voice input and image recognition technology allows users to input product descriptions and specifications more intuitively.

[0095] The input unit supports input in different languages, allowing for an input interface that can accommodate international users. For example, when a user inputs a description of a new smartphone in a different language, the generation AI automatically translates and analyzes the input content. For example, a description input in Japanese may be translated into English and analyzed. In addition, when a user inputs specifications for a new fashion item in a different language, the generation AI automatically translates and analyzes the input content. For example, a description input in French may be translated into English and analyzed. In addition, when a user inputs a description of a new home appliance in a different language, the generation AI automatically translates and analyzes the input content. For example, a description input in Chinese may be translated into English and analyzed. This allows for input in different languages, allowing for an input interface that can accommodate international users.

[0096] The generation unit learns data from the user's past landing pages and can customize them to suit the user's preferences. For example, the generation AI learns data from the user's past landing pages and suggests designs and layouts that suit the user's preferences. For example, it automatically applies colors and fonts that were used in the past. The generation unit also learns data from the user's past landing pages and suggests content placement that suits the user's preferences. For example, it automatically applies the order of sections that was used in the past. The generation unit also learns data from the user's past landing pages and suggests the selection of images and videos that suit the user's preferences. For example, it automatically applies media that was used in the past. In this way, the generation AI learns data from the user's past landing pages and customizes them to suit the user's preferences, making it possible to generate more effective landing pages.

[0097] The generation unit can analyze competitors' landing pages and, based on that, generate a landing page that is optimal for the user's product. For example, the generation AI analyzes competitors' landing pages and proposes the optimal design and layout for the user's product. For example, it refers to competitors' success stories. The generation unit also analyzes competitors' landing pages and proposes the optimal content placement for the user's product. For example, it refers to competitors' effective section placements. The generation unit also analyzes competitors' landing pages and proposes the optimal image and video selection for the user's product. For example, it refers to competitors' attractive media. This makes it possible to provide competitive content by analyzing competitors' landing pages and generating the optimal landing page based on them.

[0098] The generation unit can use the emotion estimation function to monitor visitors' emotional responses to the landing page generated by the user in real time and continuously suggest optimal pages. For example, the generation AI monitors visitors' emotional responses to the landing page in real time and continuously suggests optimal designs and layouts. For example, it adjusts the page based on the visitor's emotional data. The generation unit also monitors visitors' emotional responses to the landing page in real time and continuously suggests optimal content placement. For example, it adjusts the order of sections based on the visitor's emotional data. The generation unit also monitors visitors' emotional responses to the landing page in real time and continuously suggests optimal image and video selections. For example, it selects media based on the visitor's emotional data. In this way, the generation unit improves visitor engagement by monitoring visitors' emotional responses in real time and continuously suggesting optimal pages.

[0099] The generation unit can learn from a user's past blog posts and generate articles that match the user's writing style and tone. For example, the generation AI learns from a user's past blog posts and generates new articles that match the user's writing style and tone. For example, it refers to the wording and expressions in past posts to create articles with a consistent feel. The generation unit also learns from a user's past blog posts and generates articles that match the user's writing style and tone. For example, it creates articles on new topics while maintaining the style of past posts. The generation unit also learns from a user's past blog posts and generates articles that match the user's writing style and tone. For example, it refers to the structure and paragraph arrangement of past posts to create articles that are easy to read. In this way, it is possible to provide content with a consistent feel by learning from a user's past blog posts and generating articles that match the writing style and tone.

[0100] The generation unit can refer to the latest industry news and trends and generate related blog posts based on them. For example, the generation AI collects the latest industry news and generates related blog posts based on it. For example, it creates articles about the latest technological trends and market changes. The generation unit also collects the latest trend information and generates related blog posts based on it. For example, it creates articles about popular products and services. The generation unit also refers to the latest industry news and trends and generates related blog posts based on them. For example, it creates articles that incorporate the opinions of industry leaders and influencers. This makes it possible to provide timely and relevant content by referring to the latest industry news and trends and generating related blog posts based on them.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The input unit accepts product descriptions and specifications from the user. For example, the user inputs the features, specs, and price of a new smartphone. Step 2: The generation unit generates landing pages and related blog articles based on the product description and specifications received by the input unit. For example, based on prompts including the product description and specifications, the generation AI generates a landing page that includes a design that highlights the smartphone's features and the placement of a purchase button. The generation AI also generates related blog articles such as smartphone usage guides, reviews, and comparison articles. Step 3: The optimization part applies SEO measures to the landing pages and related blog posts generated by the generation part. For example, the generation AI selects appropriate keywords, sets meta tags, and optimizes internal links. This improves the ranking of the generated content in search engines and attracts more visitors.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0138] The data processing device 12 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

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

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

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

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

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

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an input unit that receives product descriptions and specifications from a user; a generation unit that generates a landing page and a related blog article based on the description and specifications of the product received by the input unit; an optimization unit that applies SEO measures to the landing page and the related blog article generated by the generation unit. A system characterized by:

2. The input unit Analyzes the user's input in real time and performs auto-completion and suggestions based on the input.

2. The system of claim 1.

3. The input unit The system refers to a database of similar past products and presents optimal input examples for the product description and specifications entered by the user.

2. The system of claim 1.

4. The input unit Analyze the emotions of the user when they input data and provide input guides to elicit positive emotions.

2. The system of claim 1.

5. The input unit Use voice input and image recognition technology to input product descriptions and specifications, allowing users to provide information more intuitively 2. The system of claim 1.

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