system

Abstract

The system according to this embodiment aims to automate the generation, distribution, engagement monitoring, and improvement of content. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a distribution unit, a monitoring unit, and an improvement unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates content based on the analysis results obtained by the analysis unit. The distribution unit distributes the content generated by the generation unit. The monitoring unit monitors the engagement of the content distributed by the distribution unit. The improvement unit improves the content based on the engagement data obtained by the monitoring unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to consistently automate the generation, distribution, monitoring, and improvement of content, and there is room for improvement in performing efficient marketing activities.

[0005] The system according to the embodiment aims to automate the generation, distribution, monitoring, and improvement of content.

Means for Solving the Problems

[0006] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a distribution unit, a monitoring unit, and an improvement unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The generation unit generates content based on the analysis results obtained by the analysis unit. The distribution unit distributes the content generated by the generation unit. The monitoring unit monitors the engagement of the content distributed by the distribution unit. The improvement unit improves the content based on the engagement data obtained by the monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate content generation, distribution, engagement monitoring, and improvement. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The content generation agent according to an embodiment of the present invention is a system for supporting a company's marketing team. This system has the function of automatically generating, verifying, and improving content such as blog posts and social media posts. The content generation agent works in conjunction with crawlers and API tools to collect the latest information from the internet and instantly grasp hot topics and emerging trends related to marketing. The collected information is analyzed using natural language processing technology to suggest content topics that are optimal for a specific period and target audience. This includes competitor activity and keyword analysis across the industry. Next, the generation AI creates blog posts and social media posts based on the specified topics. The generated content is templated according to specific formats and brand guidelines to maintain consistency in the company's branding standards. The created content is automatically published and distributed via API integration with platforms such as WordPress, HubSpot, and Hootsuite. This process also includes analysis of the optimal distribution time, ensuring distribution targets the active hours of the target audience. After distribution, hashtag strategies and collaboration suggestions are also provided to encourage engagement and interaction in a short period of time. After content is published, the agent monitors engagement data such as clicks, shares, comments, and likes in real time. This allows them to understand the content's performance and compare it to predictions. Through performance measurement, they identify key elements for success and build a system that enables the agent to reflect this in future content creation. Furthermore, using past content performance data, the agent continuously learns through machine learning algorithms. In particular, they analyze the differences between successful and unsuccessful cases and incorporate improvements for future content creation. Continuous improvement leads to more accurate targeting and higher content quality, supporting the achievement of long-term marketing goals.In this way, content generation agents are innovative tools that support companies' marketing activities, utilizing the latest technology and data analysis to grasp trends in real time and automatically generate appropriate content. This enables high-quality output without human intervention, maximizing marketing effectiveness. Thus, content generation agents can streamline and maximize the effectiveness of companies' marketing activities.

[0029] The content generation agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a distribution unit, a monitoring unit, and an improvement unit. The collection unit collects information. The collection unit can collect the latest information from the internet, for example, using a crawler or API tool. The collection unit can collect information from a specific website, for example, using a web crawler. The collection unit can also obtain information from a specific database using an API tool. Furthermore, the collection unit can analyze the content of a web page using a scraping tool and extract the necessary information. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information, for example, using natural language processing technology. The analysis unit can, for example, divide text data using morphological analysis and analyze the meaning of each word. Furthermore, the analysis unit can analyze the structure of a sentence using grammatical analysis and understand its meaning. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis and extract specific topics. The generation unit generates content based on the analysis results obtained by the analysis unit. The generation unit can, for example, create blog posts or social media posts using generation AI. The generation unit generates high-quality text content using, for example, text generation AI (e.g., GPT-3). The generation unit can also generate visually appealing image content using image generation AI. Furthermore, the generation unit can generate content combining text and images using multimodal generation AI. The distribution unit distributes the content generated by the generation unit. The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via API. For example, the distribution unit can automatically publish blog posts using the WordPress API. It can also automatically distribute marketing emails using the HubSpot API. Furthermore, it can automatically distribute social media posts using the Hootsuite API. The monitoring unit monitors the engagement of content distributed by the distribution unit.The monitoring unit can monitor engagement data such as clicks, shares, comments, and likes in real time. For example, the monitoring unit can monitor clicks to understand which content is being clicked the most. It can also monitor shares to understand which content is being shared the most. Furthermore, it can monitor comments to understand which content is being commented on the most. The improvement unit improves the content based on the engagement data obtained by the monitoring unit. For example, the improvement unit can continuously learn using machine learning algorithms based on past content performance data. For example, the improvement unit can analyze the differences between successful and unsuccessful examples and incorporate improvements for future content creation. It can also identify which elements are key to success based on engagement data and reflect this in future content creation. As a result, the content creation agent according to the embodiment can streamline marketing activities and maximize their effectiveness by automatically collecting, analyzing, generating, distributing, monitoring, and improving information.

[0030] The data collection unit collects information. For example, the data collection unit can collect the latest information from the internet using crawlers or API tools. Specifically, a web crawler visits specific websites and retrieves page content. This allows the data collection unit to automatically collect the latest information from news sites, blogs, forums, etc. The web crawler starts crawling based on a specified list of URLs, following links and collecting relevant pages one after another. The collected page content is saved as text data, ready for processing in the subsequent analysis unit. The data collection unit can also retrieve information from specific databases using API tools. For example, it can use a news API to retrieve the latest news articles or a social media API to collect posts related to specific hashtags. The API tool sends a request to a specified endpoint and retrieves the data returned as a response. This allows the data collection unit to efficiently collect information that is updated in real time. Furthermore, the data collection unit can also analyze the content of web pages using scraping tools and extract the necessary information. Scraping tools analyze the HTML structure and extract data based on specific tags and classes. For example, it can collect reviews about specific products from product review sites, or collect job postings for specific job types from job search sites. This allows the data collection unit to efficiently gather necessary data from diverse sources, strengthening the information infrastructure of the entire system.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using natural language processing techniques. Specifically, it can divide text data using morphological analysis and analyze the meaning of each word. Morphological analysis is a technique that divides text into word units and identifies the part of speech and meaning of each word. This allows the analysis unit to understand the basic structure of the text data and prepare for subsequent processing. The analysis unit can also analyze the structure of sentences and understand their meaning using grammatical analysis. Grammatical analysis is a technique that identifies the constituent elements of a sentence (subject, predicate, object, etc.) and analyzes their relationships. This allows the analysis unit to understand the context of the text data and perform more advanced semantic analysis. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis and extract specific topics. Semantic analysis is a technique that understands the content of text and extracts information related to specific themes or topics. For example, it can extract information about a specific incident from a news article or information about a specific trend from a social media post. This allows the analysis unit to efficiently analyze the collected information and improve the overall information processing capabilities of the system. Furthermore, the analysis unit can also analyze patterns and trends in the collected data using machine learning algorithms. This enables the analysis unit to learn from past data and predict future trends and patterns.

[0032] The generation unit generates content based on the analysis results obtained by the analysis unit. For example, the generation unit can create blog posts and social media posts using generation AI. Specifically, it generates high-quality text content using text generation AI. Text generation AI is a technology that generates natural-sounding sentences based on collected information and analysis results. For example, it can automatically generate summaries of news articles or blog posts on specific topics. The generation unit can also generate visually appealing image content using image generation AI. Image generation AI is a technology that generates images that match a specific theme or style based on collected information. For example, it can automatically generate banner images for social media or illustrations to accompany blog posts. Furthermore, the generation unit can generate content that combines text and images using multimodal generation AI. Multimodal generation AI is a technology that understands both text and images and combines them to generate consistent content. For example, it can automatically generate product introduction pages or event announcement posters. This allows the generation unit to efficiently generate diverse formats of content and improve the overall content generation capability of the system. Furthermore, the generation unit can evaluate the quality of the generated content and make corrections or improvements as needed. This allows the generation unit to consistently provide high-quality content and meet user needs.

[0033] The distribution unit distributes content generated by the generation unit. The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via APIs. Specifically, it can automatically publish blog posts using the WordPress API. The WordPress API allows for programmatic execution of tasks such as posting, editing, and deleting blog posts. This enables the distribution unit to automatically publish and update generated blog posts. The distribution unit can also automatically distribute marketing emails using the HubSpot API. The HubSpot API allows for programmatic execution of tasks such as creating, sending, and tracking emails. This allows the distribution unit to automatically distribute generated marketing emails and track their effectiveness. Furthermore, the distribution unit can automatically distribute social media posts using the Hootsuite API. The Hootsuite API allows for programmatic execution of tasks such as posting, scheduling, and analyzing social media posts. This enables the distribution unit to automatically distribute generated social media posts and analyze engagement. This allows the distribution unit to efficiently distribute content across diverse platforms and improve the overall distribution capabilities of the system. Furthermore, the distribution department can manage the distribution schedule and deliver content at the optimal time. This allows the distribution department to maximize user engagement and enhance the effectiveness of the content.

[0034] The monitoring department monitors the engagement of content distributed by the distribution department. The monitoring department can monitor engagement data such as clicks, shares, comments, and likes in real time. Specifically, it monitors clicks to understand which content is receiving the most clicks. Clicks are an important indicator of user interest in the content. The monitoring department can also monitor shares to understand which content is being shared the most. Shares are an indicator of how widely the content is being shared and are important for measuring the content's impact. Furthermore, the monitoring department can monitor comments to understand which content is receiving the most comments. Comments are an indicator of user engagement with the content and are important for measuring content engagement. By collecting and analyzing this engagement data in real time, the monitoring department can evaluate the performance of the content. Additionally, the monitoring department can visualize the engagement data and provide it as dashboards and reports. This allows the monitoring department to intuitively understand the content's performance and support rapid decision-making.

[0035] The Improvement Department improves content based on engagement data obtained by the Monitoring Department. For example, the Improvement Department can continuously learn using machine learning algorithms based on past content performance data. Specifically, it analyzes the differences between successful and unsuccessful examples and incorporates improvements for future content creation. For instance, it analyzes the differences between content with high and low click-through rates to identify key elements for success. The Improvement Department can also identify key elements for success based on engagement data and reflect this in future content creation. For example, if specific topics or keywords are factors that increase engagement, these elements will be actively incorporated in future content creation. The Improvement Department can also collect user feedback and incorporate it into content improvements. For example, it can analyze user comments and survey results to improve the content and format. This allows the Improvement Department to consistently provide high-quality content and meet user needs. Furthermore, the Improvement Department can use machine learning algorithms to predict content performance and develop optimal content strategies. This allows the Improvement Department to improve the overall content creation capabilities of the system and maximize the effectiveness of marketing activities.

[0036] The data collection unit can collect the latest information from the internet in conjunction with crawlers and API tools. For example, the data collection unit can collect information from specific websites using a web crawler. For example, the data collection unit can collect the latest news articles from a specific news site. The data collection unit can also retrieve information from specific databases using API tools. For example, the data collection unit can retrieve the latest posts from a specific social media platform. Furthermore, the data collection unit can analyze the content of web pages using scraping tools and extract the necessary information. For example, the data collection unit can extract the latest blog posts from a specific blog site. This enables timely responses in marketing activities by quickly collecting the latest information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the information collected using crawlers and API tools into a generating AI, and have the generating AI perform the information collection.

[0037] The analysis unit can analyze collected information using natural language processing techniques and propose content topics that are optimal for a specific period or target audience. For example, the analysis unit can segment text data using morphological analysis and analyze the meaning of each word. For example, the analysis unit can morphologically analyze the text data of a news article and extract important keywords. The analysis unit can also analyze the structure of a sentence using grammatical analysis and understand its meaning. For example, the analysis unit can grammatically analyze a blog post and analyze its sentence structure. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis and extract specific topics. For example, the analysis unit can semantically analyze social media posts and extract specific topics. This can enhance marketing effectiveness by proposing content topics that are optimal for the target audience. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected information into a generating AI and have the generating AI perform the analysis of the information.

[0038] The generation unit can create blog posts and social media posts using a generation AI based on a specified topic. The generation unit can generate high-quality text content using, for example, a text generation AI (e.g., GPT-3). The generation unit can also generate visually appealing image content using an image generation AI. For example, the generation unit can generate image posts for social media based on a specified topic. Furthermore, the generation unit can generate content combining text and images using a multimodal generation AI. For example, the generation unit can generate content combining blog posts and images based on a specified topic. This allows for the efficient generation of high-quality content using a generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a specified topic into the generation AI and have the generation AI perform content generation.

[0039] The generation unit can template content according to specific formats and brand guidelines. For example, the generation unit can generate content using a blog post template. For example, the generation unit can generate blog posts according to a specific format. The generation unit can also generate content using a social media post template. For example, the generation unit can generate social media posts according to a specific format. Furthermore, the generation unit can generate content according to brand guidelines. For example, the generation unit can generate content according to a company's logo and color specifications. This allows for the generation of consistent content that adheres to brand guidelines. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input specific formats and brand guidelines into a generation AI and have the generation AI perform the templating.

[0040] The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via API. For example, the distribution unit can automatically publish blog posts using the WordPress API. The distribution unit can also automatically distribute marketing emails using the HubSpot API. Furthermore, the distribution unit can automatically distribute social media posts using the Hootsuite API. This allows for the automatic distribution of content to multiple platforms through API integration. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input API integrations into a generating AI, which can then execute the distribution.

[0041] The monitoring unit can monitor engagement data such as clicks, shares, comments, and likes in real time. For example, the monitoring unit can monitor clicks to determine which content is being clicked the most. The monitoring unit can also monitor shares to determine which content is being shared the most. For example, the monitoring unit can monitor shares to determine which content is being shared the most. Furthermore, the monitoring unit can monitor comments to determine which content is being commented on the most. For example, the monitoring unit can monitor comments to determine which content is being commented on the most. This allows for immediate understanding of the effectiveness of content by monitoring engagement data in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input engagement data into a generating AI and have the generating AI perform the monitoring.

[0042] The improvement unit can continuously learn using machine learning algorithms based on performance data from past content. For example, the improvement unit can analyze the differences between successful and unsuccessful examples and incorporate improvements for future content creation. The improvement unit can also identify key elements for success based on engagement data and reflect these in future content creation. This allows for continuous learning by machine learning algorithms, thereby improving the quality of content. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input performance data into a generation AI and have the generation AI perform the improvements.

[0043] The data collection unit can evaluate the reliability of the information to be collected and prioritize the collection of highly reliable information. For example, the data collection unit can score the reliability of information sources and prioritize the collection of information with high scores. The data collection unit can also verify the source of the information and prioritize information from official websites and reliable media. Furthermore, the data collection unit can cross-check the content of the information and prioritize the collection of matching information. By prioritizing the collection of highly reliable information, accurate information can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the information into a generating AI and have the generating AI perform the reliability evaluation.

[0044] The data collection unit can dynamically change the categories of information it collects, enabling it to gather information aligned with marketing trends. For example, the data collection unit can analyze trends in real time and prioritize the collection of information related to those trends. The data collection unit can also dynamically change the necessary information categories according to the progress of a marketing campaign. For example, the data collection unit can dynamically change the necessary information categories according to the progress of a marketing campaign. Furthermore, the data collection unit can monitor the activities of competitors and prioritize the collection of information related to them. For example, the data collection unit can monitor the activities of competitors and prioritize the collection of information related to them. This allows for the collection of information aligned with marketing trends, thereby supporting effective marketing activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information categories into a generating AI, which can then perform the category changes.

[0045] The data collection unit can prioritize the collection of information relevant to a specific region, taking into account the geographical distribution of the information to be collected. For example, the data collection unit can analyze trends in a target region and prioritize the collection of information relevant to that region. The data collection unit can also collect necessary information in line with regional marketing campaigns. For example, the data collection unit can collect necessary information in line with regional marketing campaigns. Furthermore, the data collection unit can monitor regional events and news and prioritize the collection of relevant information. For example, the data collection unit can monitor regional events and news and prioritize the collection of relevant information. This allows for the effective support of regional marketing activities by prioritizing the collection of information relevant to a specific region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical distribution of information into a generating AI and have the generating AI perform the data collection.

[0046] The data collection unit can analyze social media trends in real time and collect relevant information. For example, the data collection unit can analyze social media hashtags in real time and collect information related to the trends. The data collection unit can also monitor posts from social media influencers and collect influential information. Furthermore, the data collection unit can analyze social media engagement data and collect information related to popular topics. This allows for real-time analysis of social media trends, enabling the provision of information based on the latest trends. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media trends into a generating AI, which can then perform the data collection.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific marketing analysis algorithm to marketing information. The analysis unit can also apply a competitive analysis algorithm to competitor information. For example, the analysis unit can apply a competitive analysis algorithm to competitor information. Furthermore, the analysis unit can apply a trend analysis algorithm to trend information. For example, the analysis unit can apply a trend analysis algorithm to trend information. By applying different analysis algorithms depending on the category of information, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0049] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis. For example, the analysis unit can prioritize the analysis of the latest information. The analysis unit can also lower the priority of analysis for older information. Furthermore, the analysis unit can adjust the analysis schedule according to the information submission timing. This allows the analysis unit to prioritize the analysis of the latest information by determining the priority of analysis based on the information submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission timing into a generating AI, and the generating AI can determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also prioritize the analysis of less relevant information. Furthermore, the analysis unit can postpone the analysis of less relevant information. In addition, the analysis unit can adjust the analysis schedule according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into a generating AI, and the generating AI can adjust the order of analysis.

[0051] The generation unit can adjust the level of detail of the generated content based on its importance. For example, the generation unit can generate content with detailed information for high-importance content. The generation unit can also generate simplified content for low-importance content. Furthermore, the generation unit can determine the generation priority according to the importance of the content. This allows for efficient content generation by adjusting the level of detail based on the importance of the content. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the content into the generation AI, and the generation AI can adjust the level of detail of the generated content.

[0052] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a specific marketing generation algorithm to marketing content. The generation unit can also apply a competitive generation algorithm to competitive content. For example, the generation unit can apply a competitive generation algorithm to competitive content. Furthermore, the generation unit can apply a trend generation algorithm to trend content. For example, the generation unit can apply a trend generation algorithm to trend content. By applying different generation algorithms depending on the content category, more accurate content can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the content category into the generation AI and have the generation AI apply a generation algorithm.

[0053] The generation unit can determine the generation priority based on the content submission date during generation. For example, the generation unit can prioritize the generation of the latest content. The generation unit can also lower the generation priority of older content. Furthermore, the generation unit can adjust the generation schedule according to the content submission date. This allows for the prioritization of the latest content by determining the generation priority based on the content submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the content submission date into the generation AI, and the generation AI can determine the generation priority.

[0054] The generation unit can adjust the generation order based on the relevance of the content during generation. For example, the generation unit can prioritize the generation of highly relevant content. The generation unit can also postpone the generation of less relevant content. For example, the generation unit can postpone the generation of less relevant content. Furthermore, the generation unit can adjust the generation schedule according to the relevance of the content. For example, the generation unit can adjust the generation schedule according to the relevance of the content. This allows for the prioritization of highly relevant content by adjusting the generation order based on the relevance of the content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the content into a generation AI, and the generation AI can adjust the generation order.

[0055] The distribution unit can select the optimal distribution time by considering the target audience's active time. For example, the distribution unit can analyze the target audience's active time and distribute during that time. The distribution unit can also select the optimal distribution time by considering the target audience's lifestyle patterns. For example, the distribution unit can select the optimal distribution time by considering the target audience's lifestyle patterns. Furthermore, the distribution unit can predict the optimal distribution time based on the target audience's past engagement data. For example, the distribution unit can predict the optimal distribution time based on the target audience's past engagement data. This allows for maximizing engagement by selecting the optimal distribution time by considering the target audience's active time. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the target audience's active time into a generating AI, which can then perform the task of selecting the distribution time.

[0056] The distribution unit can apply different distribution strategies depending on the content category during distribution. For example, the distribution unit can apply a specific marketing distribution strategy to marketing content. The distribution unit can also apply a competitive distribution strategy to competitive content. For example, the distribution unit can apply a competitive distribution strategy to competitive content. Furthermore, the distribution unit can apply a trend distribution strategy to trending content. For example, the distribution unit can apply a trend distribution strategy to trending content. By applying different distribution strategies depending on the content category, more effective distribution can be achieved. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the content category into a generating AI and have the generating AI execute the application of distribution strategies.

[0057] The distribution unit can distribute content while considering the geographical distribution of the target audience. For example, the distribution unit can analyze trends in the target region and prioritize the distribution of content relevant to that region. The distribution unit can also distribute content as needed to match regional marketing campaigns. For example, the distribution unit can distribute content as needed to match regional marketing campaigns. Furthermore, the distribution unit can monitor regional events and news and prioritize the distribution of relevant content. For example, the distribution unit can monitor regional events and news and prioritize the distribution of relevant content. This allows for effective support of regional marketing activities by considering the geographical distribution of the target audience when distributing content. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input the geographical distribution of the target audience into a generating AI and have the generating AI execute the distribution.

[0058] The distribution department can adjust its distribution strategy at the time of distribution, taking into account social media trends. For example, the distribution department can analyze social media hashtags in real time and distribute content related to the trends. The distribution department can also monitor posts from social media influencers and distribute influential content. Furthermore, the distribution department can analyze social media engagement data and distribute content related to popular topics. By adjusting the distribution strategy to take social media trends into account, the distribution department can perform effective distribution that is in line with trends. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input social media trends into a generating AI and have the generating AI perform the adjustment of the distribution strategy.

[0059] The monitoring unit can adjust the level of detail of its monitoring based on the importance of the engagement data. For example, the monitoring unit can perform detailed monitoring of engagement data with high importance. The monitoring unit can also perform simplified monitoring of engagement data with low importance. For example, the monitoring unit can perform simplified monitoring of engagement data with low importance. Furthermore, the monitoring unit can determine the priority of monitoring based on the importance of the engagement data. For example, the monitoring unit can determine the priority of monitoring based on the importance of the engagement data. This allows for efficient monitoring by adjusting the level of detail of monitoring based on the importance of the engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the importance of the engagement data into a generating AI, and the generating AI can adjust the level of detail of monitoring.

[0060] The monitoring unit can apply different monitoring algorithms depending on the category of engagement data during monitoring. For example, the monitoring unit can apply a specific click monitoring algorithm to the number of clicks. The monitoring unit can also apply a share monitoring algorithm to the number of shares. For example, the monitoring unit can apply a share monitoring algorithm to the number of shares. Furthermore, the monitoring unit can also apply a comment monitoring algorithm to the number of comments. For example, the monitoring unit can apply a comment monitoring algorithm to the number of comments. This allows for more accurate monitoring by applying different monitoring algorithms depending on the category of engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the categories of engagement data into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0061] The monitoring unit can determine monitoring priorities based on the submission timing of engagement data. For example, the monitoring unit can prioritize monitoring the latest engagement data. The monitoring unit can also lower the monitoring priority of older engagement data. Furthermore, the monitoring unit can adjust the monitoring schedule according to the submission timing of engagement data. This allows for priority monitoring of the latest data by determining monitoring priorities based on the submission timing of engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the submission timing of engagement data into a generating AI, which can then determine the monitoring priorities.

[0062] The monitoring unit can adjust the monitoring order based on the relevance of the engagement data during monitoring. For example, the monitoring unit can prioritize monitoring highly relevant engagement data. The monitoring unit can also prioritize monitoring highly relevant engagement data. For example, the monitoring unit can prioritize monitoring highly relevant engagement data. The monitoring unit can also postpone monitoring less relevant engagement data. For example, the monitoring unit can postpone monitoring less relevant engagement data. Furthermore, the monitoring unit can adjust the monitoring schedule according to the relevance of the engagement data. For example, the monitoring unit can adjust the monitoring schedule according to the relevance of the engagement data. This allows for prioritizing the monitoring of highly relevant data by adjusting the monitoring order based on the relevance of the engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the relevance of the engagement data into a generating AI, and the generating AI can adjust the monitoring order.

[0063] The improvement unit can analyze past engagement data to select the optimal improvement method during the improvement process. For example, the improvement unit can analyze past successes and apply similar improvement methods. The improvement unit can also analyze past failures and modify improvement methods. Furthermore, the improvement unit can select the optimal improvement method based on past engagement data. This allows for effective improvement by analyzing past engagement data and selecting the optimal improvement method. Some or all of the above processes in the improvement unit may be performed using AI, or not. For example, the improvement unit can input past engagement data into a generating AI, and the generating AI can select the optimal improvement method.

[0064] The improvement unit can apply different improvement algorithms depending on the content category during the improvement process. For example, the improvement unit can apply a specific marketing improvement algorithm to marketing content. The improvement unit can also apply a competitive improvement algorithm to competitive content. For example, the improvement unit can apply a competitive improvement algorithm to competitive content. Furthermore, the improvement unit can apply a trend improvement algorithm to trend content. For example, the improvement unit can apply a trend improvement algorithm to trend content. By applying different improvement algorithms depending on the content category, more accurate improvements can be made. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the content category into a generating AI and have the generating AI execute the application of the improvement algorithm.

[0065] The improvement unit can make improvements while considering the geographical distribution of engagement data. For example, the improvement unit can analyze trends in target regions and make improvements relevant to those regions. The improvement unit can also make necessary improvements in line with regional marketing campaigns. For example, the improvement unit can make necessary improvements in line with regional marketing campaigns. Furthermore, the improvement unit can monitor regional events and news and make related improvements. For example, the improvement unit can monitor regional events and news and make related improvements. This allows for effective support of regional marketing activities by making improvements while considering the geographical distribution of engagement data. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not. For example, the improvement unit can input the geographical distribution of engagement data into a generating AI and have the generating AI execute the improvements.

[0066] The improvement unit can adjust its improvement strategy while considering social media trends. For example, the improvement unit can analyze social media hashtags in real time and make improvements related to trends. The improvement unit can also monitor posts from social media influencers and make influential improvements. Furthermore, the improvement unit can analyze social media engagement data and make improvements related to popular topics. By adjusting the improvement strategy while considering social media trends, it is possible to make effective improvements that are in line with trends. Some or all of the above processes in the improvement unit may be performed using AI, for example, or not. For example, the improvement unit can input social media trends into a generating AI and have the generating AI perform adjustments to the improvement strategy.

[0067] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0068] The data collection unit can evaluate the reliability of the information it collects and prioritize the collection of highly reliable information. For example, it can score the reliability of information sources and prioritize the collection of information with high scores. It can also verify the source of information and prioritize information from official websites and reliable media. Furthermore, it can cross-check the content of the information and prioritize the collection of matching information. By prioritizing the collection of highly reliable information, it can provide accurate information.

[0069] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a simplified analysis on less important information. Furthermore, it can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail based on the importance of the information.

[0070] The generation unit can adjust the level of detail generated based on the importance of the content during generation. For example, it can generate content with detailed information for highly important content, and simplified content for less important content. Furthermore, it can determine the generation priority according to the importance of the content. This allows for efficient content generation by adjusting the level of detail based on the importance of the content.

[0071] The distribution team can select the optimal distribution time by considering the target audience's active time. For example, it can analyze the target audience's active time and distribute content during those times. It can also select the optimal distribution time by considering the target audience's lifestyle patterns. Furthermore, it can predict the optimal distribution time based on the target audience's past engagement data. By selecting the optimal distribution time considering the target audience's active time, engagement can be maximized.

[0072] The monitoring unit can adjust the level of detail of the monitoring based on the importance of the engagement data. For example, highly important engagement data can be monitored in detail, while less important engagement data can be monitored in a simplified manner. Furthermore, the monitoring priority can be determined according to the importance of the engagement data. This allows for efficient monitoring by adjusting the level of detail based on the importance of the engagement data.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The collection unit collects information. The collection unit can collect the latest information from the internet, for example, using crawlers or API tools. For example, the collection unit can collect information from specific websites using a web crawler. The collection unit can also retrieve information from specific databases using API tools. Furthermore, the collection unit can analyze the content of web pages using scraping tools and extract the necessary information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, natural language processing techniques. For example, the analysis unit can segment the text data using morphological analysis and analyze the meaning of each word. The analysis unit can also analyze the structure of sentences using grammatical analysis and understand their meaning. Furthermore, the analysis unit can analyze the meaning of the text data using semantic analysis and extract specific topics. Step 3: The generation unit generates content based on the analysis results obtained by the analysis unit. The generation unit can, for example, create blog posts or social media posts using a generation AI. The generation unit can, for example, generate high-quality text content using a text generation AI (e.g., GPT-3). The generation unit can also generate visually appealing image content using an image generation AI. Furthermore, the generation unit can generate content that combines text and images using a multimodal generation AI. Step 4: The distribution unit distributes the content generated by the generation unit. The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via API. For example, the distribution unit can automatically publish blog posts using the WordPress API. It can also automatically distribute marketing emails using the HubSpot API. Furthermore, it can automatically distribute social media posts using the Hootsuite API. Step 5: The monitoring unit monitors the engagement of content distributed by the distribution unit. The monitoring unit can monitor engagement data in real time, such as clicks, shares, comments, and likes. For example, the monitoring unit can monitor clicks to understand which content is being clicked the most. The monitoring unit can also monitor shares to understand which content is being shared the most. Furthermore, the monitoring unit can monitor comments to understand which content is being commented on the most. Step 6: The Improvement Department improves the content based on the engagement data obtained by the Monitoring Department. The Improvement Department can, for example, continuously learn using machine learning algorithms based on past content performance data. The Improvement Department can, for example, analyze the differences between successful and unsuccessful examples and incorporate improvements for future content creation. The Improvement Department can also identify which elements are key to success based on the engagement data and reflect this in future content creation.

[0075] (Example of form 2) The content generation agent according to an embodiment of the present invention is a system for supporting a company's marketing team. This system has the function of automatically generating, verifying, and improving content such as blog posts and social media posts. The content generation agent works in conjunction with crawlers and API tools to collect the latest information from the internet and instantly grasp hot topics and emerging trends related to marketing. The collected information is analyzed using natural language processing technology to suggest content topics that are optimal for a specific period and target audience. This includes competitor activity and keyword analysis across the industry. Next, the generation AI creates blog posts and social media posts based on the specified topics. The generated content is templated according to specific formats and brand guidelines to maintain consistency in the company's branding standards. The created content is automatically published and distributed via API integration with platforms such as WordPress, HubSpot, and Hootsuite. This process also includes analysis of the optimal distribution time, ensuring distribution targets the active hours of the target audience. After distribution, hashtag strategies and collaboration suggestions are also provided to encourage engagement and interaction in a short period of time. After content is published, the agent monitors engagement data such as clicks, shares, comments, and likes in real time. This allows them to understand the content's performance and compare it to predictions. Through performance measurement, they identify key elements for success and build a system that enables the agent to reflect this in future content creation. Furthermore, using past content performance data, the agent continuously learns through machine learning algorithms. In particular, they analyze the differences between successful and unsuccessful cases and incorporate improvements for future content creation. Continuous improvement leads to more accurate targeting and higher content quality, supporting the achievement of long-term marketing goals.In this way, content generation agents are innovative tools that support companies' marketing activities, utilizing the latest technology and data analysis to grasp trends in real time and automatically generate appropriate content. This enables high-quality output without human intervention, maximizing marketing effectiveness. Thus, content generation agents can streamline and maximize the effectiveness of companies' marketing activities.

[0076] The content generation agent according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a distribution unit, a monitoring unit, and an improvement unit. The collection unit collects information. The collection unit can collect the latest information from the internet, for example, using a crawler or API tool. The collection unit can collect information from a specific website, for example, using a web crawler. The collection unit can also obtain information from a specific database using an API tool. Furthermore, the collection unit can analyze the content of a web page using a scraping tool and extract the necessary information. The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information, for example, using natural language processing technology. The analysis unit can, for example, divide text data using morphological analysis and analyze the meaning of each word. Furthermore, the analysis unit can analyze the structure of a sentence using grammatical analysis and understand its meaning. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis and extract specific topics. The generation unit generates content based on the analysis results obtained by the analysis unit. The generation unit can, for example, create blog posts or social media posts using generation AI. The generation unit generates high-quality text content using, for example, text generation AI (e.g., GPT-3). The generation unit can also generate visually appealing image content using image generation AI. Furthermore, the generation unit can generate content combining text and images using multimodal generation AI. The distribution unit distributes the content generated by the generation unit. The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via API. For example, the distribution unit can automatically publish blog posts using the WordPress API. It can also automatically distribute marketing emails using the HubSpot API. Furthermore, it can automatically distribute social media posts using the Hootsuite API. The monitoring unit monitors the engagement of content distributed by the distribution unit.The monitoring unit can monitor engagement data such as clicks, shares, comments, and likes in real time. For example, the monitoring unit can monitor clicks to understand which content is being clicked the most. It can also monitor shares to understand which content is being shared the most. Furthermore, it can monitor comments to understand which content is being commented on the most. The improvement unit improves the content based on the engagement data obtained by the monitoring unit. For example, the improvement unit can continuously learn using machine learning algorithms based on past content performance data. For example, the improvement unit can analyze the differences between successful and unsuccessful examples and incorporate improvements for future content creation. It can also identify which elements are key to success based on engagement data and reflect this in future content creation. As a result, the content creation agent according to the embodiment can streamline marketing activities and maximize their effectiveness by automatically collecting, analyzing, generating, distributing, monitoring, and improving information.

[0077] The data collection unit collects information. For example, the data collection unit can collect the latest information from the internet using crawlers or API tools. Specifically, a web crawler visits specific websites and retrieves page content. This allows the data collection unit to automatically collect the latest information from news sites, blogs, forums, etc. The web crawler starts crawling based on a specified list of URLs, following links and collecting relevant pages one after another. The collected page content is saved as text data, ready for processing in the subsequent analysis unit. The data collection unit can also retrieve information from specific databases using API tools. For example, it can use a news API to retrieve the latest news articles or a social media API to collect posts related to specific hashtags. The API tool sends a request to a specified endpoint and retrieves the data returned as a response. This allows the data collection unit to efficiently collect information that is updated in real time. Furthermore, the data collection unit can also analyze the content of web pages using scraping tools and extract the necessary information. Scraping tools analyze the HTML structure and extract data based on specific tags and classes. For example, it can collect reviews about specific products from product review sites, or collect job postings for specific job types from job search sites. This allows the data collection unit to efficiently gather necessary data from diverse sources, strengthening the information infrastructure of the entire system.

[0078] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the collected information using natural language processing techniques. Specifically, it can divide text data using morphological analysis and analyze the meaning of each word. Morphological analysis is a technique that divides text into word units and identifies the part of speech and meaning of each word. This allows the analysis unit to understand the basic structure of the text data and prepare for subsequent processing. The analysis unit can also analyze the structure of sentences and understand their meaning using grammatical analysis. Grammatical analysis is a technique that identifies the constituent elements of a sentence (subject, predicate, object, etc.) and analyzes their relationships. This allows the analysis unit to understand the context of the text data and perform more advanced semantic analysis. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis and extract specific topics. Semantic analysis is a technique that understands the content of text and extracts information related to specific themes or topics. For example, it can extract information about a specific incident from a news article or information about a specific trend from a social media post. This allows the analysis unit to efficiently analyze the collected information and improve the overall information processing capabilities of the system. Furthermore, the analysis unit can also analyze patterns and trends in the collected data using machine learning algorithms. This enables the analysis unit to learn from past data and predict future trends and patterns.

[0079] The generation unit generates content based on the analysis results obtained by the analysis unit. For example, the generation unit can create blog posts and social media posts using generation AI. Specifically, it generates high-quality text content using text generation AI. Text generation AI is a technology that generates natural-sounding sentences based on collected information and analysis results. For example, it can automatically generate summaries of news articles or blog posts on specific topics. The generation unit can also generate visually appealing image content using image generation AI. Image generation AI is a technology that generates images that match a specific theme or style based on collected information. For example, it can automatically generate banner images for social media or illustrations to accompany blog posts. Furthermore, the generation unit can generate content that combines text and images using multimodal generation AI. Multimodal generation AI is a technology that understands both text and images and combines them to generate consistent content. For example, it can automatically generate product introduction pages or event announcement posters. This allows the generation unit to efficiently generate diverse formats of content and improve the overall content generation capability of the system. Furthermore, the generation unit can evaluate the quality of the generated content and make corrections or improvements as needed. This allows the generation unit to consistently provide high-quality content and meet user needs.

[0080] The distribution unit distributes content generated by the generation unit. The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via APIs. Specifically, it can automatically publish blog posts using the WordPress API. The WordPress API allows for programmatic execution of tasks such as posting, editing, and deleting blog posts. This enables the distribution unit to automatically publish and update generated blog posts. The distribution unit can also automatically distribute marketing emails using the HubSpot API. The HubSpot API allows for programmatic execution of tasks such as creating, sending, and tracking emails. This allows the distribution unit to automatically distribute generated marketing emails and track their effectiveness. Furthermore, the distribution unit can automatically distribute social media posts using the Hootsuite API. The Hootsuite API allows for programmatic execution of tasks such as posting, scheduling, and analyzing social media posts. This enables the distribution unit to automatically distribute generated social media posts and analyze engagement. This allows the distribution unit to efficiently distribute content across diverse platforms and improve the overall distribution capabilities of the system. Furthermore, the distribution department can manage the distribution schedule and deliver content at the optimal time. This allows the distribution department to maximize user engagement and enhance the effectiveness of the content.

[0081] The monitoring department monitors the engagement of content distributed by the distribution department. The monitoring department can monitor engagement data such as clicks, shares, comments, and likes in real time. Specifically, it monitors clicks to understand which content is receiving the most clicks. Clicks are an important indicator of user interest in the content. The monitoring department can also monitor shares to understand which content is being shared the most. Shares are an indicator of how widely the content is being shared and are important for measuring the content's impact. Furthermore, the monitoring department can monitor comments to understand which content is receiving the most comments. Comments are an indicator of user engagement with the content and are important for measuring content engagement. By collecting and analyzing this engagement data in real time, the monitoring department can evaluate the performance of the content. Additionally, the monitoring department can visualize the engagement data and provide it as dashboards and reports. This allows the monitoring department to intuitively understand the content's performance and support rapid decision-making.

[0082] The Improvement Department improves content based on engagement data obtained by the Monitoring Department. For example, the Improvement Department can continuously learn using machine learning algorithms based on past content performance data. Specifically, it analyzes the differences between successful and unsuccessful examples and incorporates improvements for future content creation. For instance, it analyzes the differences between content with high and low click-through rates to identify key elements for success. The Improvement Department can also identify key elements for success based on engagement data and reflect this in future content creation. For example, if specific topics or keywords are factors that increase engagement, these elements will be actively incorporated in future content creation. The Improvement Department can also collect user feedback and incorporate it into content improvements. For example, it can analyze user comments and survey results to improve the content and format. This allows the Improvement Department to consistently provide high-quality content and meet user needs. Furthermore, the Improvement Department can use machine learning algorithms to predict content performance and develop optimal content strategies. This allows the Improvement Department to improve the overall content creation capabilities of the system and maximize the effectiveness of marketing activities.

[0083] The data collection unit can collect the latest information from the internet in conjunction with crawlers and API tools. For example, the data collection unit can collect information from specific websites using a web crawler. For example, the data collection unit can collect the latest news articles from a specific news site. The data collection unit can also retrieve information from specific databases using API tools. For example, the data collection unit can retrieve the latest posts from a specific social media platform. Furthermore, the data collection unit can analyze the content of web pages using scraping tools and extract the necessary information. For example, the data collection unit can extract the latest blog posts from a specific blog site. This enables timely responses in marketing activities by quickly collecting the latest information. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the information collected using crawlers and API tools into a generating AI, and have the generating AI perform the information collection.

[0084] The analysis unit can analyze collected information using natural language processing techniques and propose content topics that are optimal for a specific period or target audience. For example, the analysis unit can segment text data using morphological analysis and analyze the meaning of each word. For example, the analysis unit can morphologically analyze the text data of a news article and extract important keywords. The analysis unit can also analyze the structure of a sentence using grammatical analysis and understand its meaning. For example, the analysis unit can grammatically analyze a blog post and analyze its sentence structure. Furthermore, the analysis unit can analyze the meaning of text data using semantic analysis and extract specific topics. For example, the analysis unit can semantically analyze social media posts and extract specific topics. This can enhance marketing effectiveness by proposing content topics that are optimal for the target audience. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected information into a generating AI and have the generating AI perform the analysis of the information.

[0085] The generation unit can create blog posts and social media posts using a generation AI based on a specified topic. The generation unit can generate high-quality text content using, for example, a text generation AI (e.g., GPT-3). The generation unit can also generate visually appealing image content using an image generation AI. For example, the generation unit can generate image posts for social media based on a specified topic. Furthermore, the generation unit can generate content combining text and images using a multimodal generation AI. For example, the generation unit can generate content combining blog posts and images based on a specified topic. This allows for the efficient generation of high-quality content using a generation AI. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input a specified topic into the generation AI and have the generation AI perform content generation.

[0086] The generation unit can template content according to specific formats and brand guidelines. For example, the generation unit can generate content using a blog post template. For example, the generation unit can generate blog posts according to a specific format. The generation unit can also generate content using a social media post template. For example, the generation unit can generate social media posts according to a specific format. Furthermore, the generation unit can generate content according to brand guidelines. For example, the generation unit can generate content according to a company's logo and color specifications. This allows for the generation of consistent content that adheres to brand guidelines. Some or all of the above processes in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input specific formats and brand guidelines into a generation AI and have the generation AI perform the templating.

[0087] The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via API. For example, the distribution unit can automatically publish blog posts using the WordPress API. The distribution unit can also automatically distribute marketing emails using the HubSpot API. Furthermore, the distribution unit can automatically distribute social media posts using the Hootsuite API. This allows for the automatic distribution of content to multiple platforms through API integration. Some or all of the above-described processes in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input API integrations into a generating AI, which can then execute the distribution.

[0088] The monitoring unit can monitor engagement data such as clicks, shares, comments, and likes in real time. For example, the monitoring unit can monitor clicks to determine which content is being clicked the most. The monitoring unit can also monitor shares to determine which content is being shared the most. For example, the monitoring unit can monitor shares to determine which content is being shared the most. Furthermore, the monitoring unit can monitor comments to determine which content is being commented on the most. For example, the monitoring unit can monitor comments to determine which content is being commented on the most. This allows for immediate understanding of the effectiveness of content by monitoring engagement data in real time. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input engagement data into a generating AI and have the generating AI perform the monitoring.

[0089] The improvement unit can continuously learn using machine learning algorithms based on performance data from past content. For example, the improvement unit can analyze the differences between successful and unsuccessful examples and incorporate improvements for future content creation. The improvement unit can also identify key elements for success based on engagement data and reflect these in future content creation. This allows for continuous learning by machine learning algorithms, thereby improving the quality of content. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input performance data into a generation AI and have the generation AI perform the improvements.

[0090] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to alleviate the user's burden. The data collection unit can also increase the frequency of information collection and provide more information if the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize the collection of important information and provide it quickly. In this way, the user's burden can be reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The generating AI may be a text generating AI (e.g., LLM) or a multimodal generating AI, but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using AI, or not using AI. For example, the collection unit may input user emotion data into the generating AI and have the generating AI perform emotion estimation.

[0091] The data collection unit can evaluate the reliability of the information to be collected and prioritize the collection of highly reliable information. For example, the data collection unit can score the reliability of information sources and prioritize the collection of information with high scores. The data collection unit can also verify the source of the information and prioritize information from official websites and reliable media. Furthermore, the data collection unit can cross-check the content of the information and prioritize the collection of matching information. By prioritizing the collection of highly reliable information, accurate information can be provided. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the reliability of the information into a generating AI and have the generating AI perform the reliability evaluation.

[0092] The data collection unit can dynamically change the categories of information it collects, enabling it to gather information aligned with marketing trends. For example, the data collection unit can analyze trends in real time and prioritize the collection of information related to those trends. The data collection unit can also dynamically change the necessary information categories according to the progress of a marketing campaign. For example, the data collection unit can dynamically change the necessary information categories according to the progress of a marketing campaign. Furthermore, the data collection unit can monitor the activities of competitors and prioritize the collection of information related to them. For example, the data collection unit can monitor the activities of competitors and prioritize the collection of information related to them. This allows for the collection of information aligned with marketing trends, thereby supporting effective marketing activities. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input information categories into a generating AI, which can then perform the category changes.

[0093] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting information of high importance. The data collection unit can also prioritize collecting information of high importance if the user is relaxed, increasing the user's options. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting information that is immediately useful. This allows the system to prioritize information that is important to the user by determining the priority of information to collect according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0094] The data collection unit can prioritize the collection of information relevant to a specific region, taking into account the geographical distribution of the information to be collected. For example, the data collection unit can analyze trends in a target region and prioritize the collection of information relevant to that region. The data collection unit can also collect necessary information in line with regional marketing campaigns. For example, the data collection unit can collect necessary information in line with regional marketing campaigns. Furthermore, the data collection unit can monitor regional events and news and prioritize the collection of relevant information. For example, the data collection unit can monitor regional events and news and prioritize the collection of relevant information. This allows for the effective support of regional marketing activities by prioritizing the collection of information relevant to a specific region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the geographical distribution of information into a generating AI and have the generating AI perform the data collection.

[0095] The data collection unit can analyze social media trends in real time and collect relevant information. For example, the data collection unit can analyze social media hashtags in real time and collect information related to the trends. The data collection unit can also monitor posts from social media influencers and collect influential information. Furthermore, the data collection unit can analyze social media engagement data and collect information related to popular topics. This allows for real-time analysis of social media trends, enabling the provision of information based on the latest trends. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media trends into a generating AI, which can then perform the data collection.

[0096] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple and easy-to-read display method. The analysis unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on information of high importance. The analysis unit can also perform a simplified analysis on information of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the information into a generating AI, and the generating AI can adjust the level of detail of the analysis.

[0098] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a specific marketing analysis algorithm to marketing information. The analysis unit can also apply a competitive analysis algorithm to competitor information. For example, the analysis unit can apply a competitive analysis algorithm to competitor information. Furthermore, the analysis unit can apply a trend analysis algorithm to trend information. For example, the analysis unit can apply a trend analysis algorithm to trend information. By applying different analysis algorithms depending on the category of information, more accurate analysis can be performed. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the category of information into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0099] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize displaying analysis results of high importance. The analysis unit can also prioritize displaying analysis results of high importance if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can prioritize displaying analysis results that are immediately useful. In this way, by prioritizing analysis results according to the user's emotions, it is possible to prioritize providing information that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0100] The analysis unit can determine the priority of analysis based on the information submission timing during the analysis. For example, the analysis unit can prioritize the analysis of the latest information. The analysis unit can also lower the priority of analysis for older information. Furthermore, the analysis unit can adjust the analysis schedule according to the information submission timing. This allows the analysis unit to prioritize the analysis of the latest information by determining the priority of analysis based on the information submission timing. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the information submission timing into a generating AI, and the generating AI can determine the priority of analysis.

[0101] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant information. The analysis unit can also prioritize the analysis of less relevant information. Furthermore, the analysis unit can postpone the analysis of less relevant information. In addition, the analysis unit can adjust the analysis schedule according to the relevance of the information. By adjusting the order of analysis based on the relevance of the information, highly relevant information can be prioritized. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the information into a generating AI, and the generating AI can adjust the order of analysis.

[0102] The generation unit can estimate the user's emotions and adjust the way the generated content is presented based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate content in a relaxed tone. The generation unit can also generate concise and to-the-point content if the user is in a hurry. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. This allows the system to provide the user with optimal content by adjusting the way the content is presented according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0103] The generation unit can adjust the level of detail of the generated content based on its importance. For example, the generation unit can generate content with detailed information for high-importance content. The generation unit can also generate simplified content for low-importance content. Furthermore, the generation unit can determine the generation priority according to the importance of the content. This allows for efficient content generation by adjusting the level of detail based on the importance of the content. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or not. For example, the generation unit can input the importance of the content into the generation AI, and the generation AI can adjust the level of detail of the generated content.

[0104] The generation unit can apply different generation algorithms depending on the content category during generation. For example, the generation unit can apply a specific marketing generation algorithm to marketing content. The generation unit can also apply a competitive generation algorithm to competitive content. For example, the generation unit can apply a competitive generation algorithm to competitive content. Furthermore, the generation unit can apply a trend generation algorithm to trend content. For example, the generation unit can apply a trend generation algorithm to trend content. By applying different generation algorithms depending on the content category, more accurate content can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the content category into the generation AI and have the generation AI apply a generation algorithm.

[0105] The generation unit can estimate the user's emotions and adjust the length of the content it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise content. The generation unit can also generate longer content with detailed explanations if the user is relaxed. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. This allows for the provision of optimal content to the user by adjusting the length of the content according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generation AI. Generation AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using a generation AI, for example, or without using a generation AI. For example, the generation unit can input user emotion data into a generation AI and have the generation AI perform emotion estimation.

[0106] The generation unit can determine the generation priority based on the content submission date during generation. For example, the generation unit can prioritize the generation of the latest content. The generation unit can also lower the generation priority of older content. Furthermore, the generation unit can adjust the generation schedule according to the content submission date. This allows for the prioritization of the latest content by determining the generation priority based on the content submission date. Some or all of the above processing in the generation unit may be performed using a generation AI, or without a generation AI. For example, the generation unit can input the content submission date into the generation AI, and the generation AI can determine the generation priority.

[0107] The generation unit can adjust the generation order based on the relevance of the content during generation. For example, the generation unit can prioritize the generation of highly relevant content. The generation unit can also postpone the generation of less relevant content. For example, the generation unit can postpone the generation of less relevant content. Furthermore, the generation unit can adjust the generation schedule according to the relevance of the content. For example, the generation unit can adjust the generation schedule according to the relevance of the content. This allows for the prioritization of highly relevant content by adjusting the generation order based on the relevance of the content. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the relevance of the content into a generation AI, and the generation AI can adjust the generation order.

[0108] The delivery unit can estimate the user's emotions and adjust the timing of deliveries based on those emotions. For example, if the user is stressed, the delivery unit can reduce the frequency of deliveries to lessen the user's burden. The delivery unit can also increase the frequency of deliveries and provide more information if the user is relaxed. Furthermore, if the user is in a hurry, the delivery unit can prioritize and quickly deliver important information. This allows content to be delivered at the optimal time for the user by adjusting the timing of deliveries according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the distribution unit may be performed using AI, or not using AI. For example, the distribution unit may input user emotion data into the generative AI and have the generative AI perform emotion estimation.

[0109] The distribution unit can select the optimal distribution time by considering the target audience's active time. For example, the distribution unit can analyze the target audience's active time and distribute during that time. The distribution unit can also select the optimal distribution time by considering the target audience's lifestyle patterns. For example, the distribution unit can select the optimal distribution time by considering the target audience's lifestyle patterns. Furthermore, the distribution unit can predict the optimal distribution time based on the target audience's past engagement data. For example, the distribution unit can predict the optimal distribution time based on the target audience's past engagement data. This allows for maximizing engagement by selecting the optimal distribution time by considering the target audience's active time. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the target audience's active time into a generating AI, which can then perform the task of selecting the distribution time.

[0110] The distribution unit can apply different distribution strategies depending on the content category during distribution. For example, the distribution unit can apply a specific marketing distribution strategy to marketing content. The distribution unit can also apply a competitive distribution strategy to competitive content. For example, the distribution unit can apply a competitive distribution strategy to competitive content. Furthermore, the distribution unit can apply a trend distribution strategy to trending content. For example, the distribution unit can apply a trend distribution strategy to trending content. By applying different distribution strategies depending on the content category, more effective distribution can be achieved. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the content category into a generating AI and have the generating AI execute the application of distribution strategies.

[0111] The delivery unit can estimate the user's emotions and determine the priority of the content to deliver based on the estimated emotions. For example, if the user is stressed, the delivery unit can prioritize delivering high-priority content. The delivery unit can also prioritize delivering high-priority content if the user is relaxed, increasing the user's choices. Furthermore, if the user is in a hurry, the delivery unit can prioritize delivering content that is immediately useful. For example, if the user is in a hurry, the delivery unit can prioritize delivering content that is immediately useful. In this way, by determining the priority of the content delivered according to the user's emotions, it is possible to prioritize the delivery of content that is important to the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0112] The distribution unit can distribute content while considering the geographical distribution of the target audience. For example, the distribution unit can analyze trends in the target region and prioritize the distribution of content relevant to that region. The distribution unit can also distribute content as needed to match regional marketing campaigns. For example, the distribution unit can distribute content as needed to match regional marketing campaigns. Furthermore, the distribution unit can monitor regional events and news and prioritize the distribution of relevant content. For example, the distribution unit can monitor regional events and news and prioritize the distribution of relevant content. This allows for effective support of regional marketing activities by considering the geographical distribution of the target audience when distributing content. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input the geographical distribution of the target audience into a generating AI and have the generating AI execute the distribution.

[0113] The distribution department can adjust its distribution strategy at the time of distribution, taking into account social media trends. For example, the distribution department can analyze social media hashtags in real time and distribute content related to the trends. The distribution department can also monitor posts from social media influencers and distribute influential content. Furthermore, the distribution department can analyze social media engagement data and distribute content related to popular topics. By adjusting the distribution strategy to take social media trends into account, the distribution department can perform effective distribution that is in line with trends. Some or all of the above processing in the distribution department may be performed using AI, for example, or not. For example, the distribution department can input social media trends into a generating AI and have the generating AI perform the adjustment of the distribution strategy.

[0114] The monitoring unit can estimate the user's emotions and adjust the way engagement data is displayed based on the estimated emotions. For example, if the user is nervous, the monitoring unit can provide a simple and easy-to-read display method. The monitoring unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the user is relaxed, the monitoring unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the monitoring unit can provide a display method that gets straight to the point. For example, if the user is in a hurry, the monitoring unit can provide a display method that gets straight to the point. By adjusting the way engagement data is displayed according to the user's emotions, a display method that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0115] The monitoring unit can adjust the level of detail of its monitoring based on the importance of the engagement data. For example, the monitoring unit can perform detailed monitoring of engagement data with high importance. The monitoring unit can also perform simplified monitoring of engagement data with low importance. For example, the monitoring unit can perform simplified monitoring of engagement data with low importance. Furthermore, the monitoring unit can determine the priority of monitoring based on the importance of the engagement data. For example, the monitoring unit can determine the priority of monitoring based on the importance of the engagement data. This allows for efficient monitoring by adjusting the level of detail of monitoring based on the importance of the engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the importance of the engagement data into a generating AI, and the generating AI can adjust the level of detail of monitoring.

[0116] The monitoring unit can apply different monitoring algorithms depending on the category of engagement data during monitoring. For example, the monitoring unit can apply a specific click monitoring algorithm to the number of clicks. The monitoring unit can also apply a share monitoring algorithm to the number of shares. For example, the monitoring unit can apply a share monitoring algorithm to the number of shares. Furthermore, the monitoring unit can also apply a comment monitoring algorithm to the number of comments. For example, the monitoring unit can apply a comment monitoring algorithm to the number of comments. This allows for more accurate monitoring by applying different monitoring algorithms depending on the category of engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the categories of engagement data into a generating AI and have the generating AI execute the application of the monitoring algorithm.

[0117] The monitoring unit can estimate the user's emotions and prioritize engagement data based on the estimated emotions. For example, if the user is stressed, the monitoring unit will prioritize displaying high-priority engagement data. The monitoring unit can also prioritize displaying high-priority engagement data if the user is stressed. For example, if the user is relaxed, the monitoring unit can display a wide range of engagement data. Furthermore, if the user is in a hurry, the monitoring unit can prioritize displaying engagement data that is immediately useful. For example, if the user is in a hurry, the monitoring unit can prioritize displaying engagement data that is immediately useful. In this way, by prioritizing engagement data according to the user's emotions, it is possible to prioritize displaying data that is important to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processes in the monitoring unit may be performed using AI, or not using AI. For example, the monitoring unit may input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0118] The monitoring unit can determine monitoring priorities based on the submission timing of engagement data. For example, the monitoring unit can prioritize monitoring the latest engagement data. The monitoring unit can also lower the monitoring priority of older engagement data. Furthermore, the monitoring unit can adjust the monitoring schedule according to the submission timing of engagement data. This allows for priority monitoring of the latest data by determining monitoring priorities based on the submission timing of engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the submission timing of engagement data into a generating AI, which can then determine the monitoring priorities.

[0119] The monitoring unit can adjust the monitoring order based on the relevance of the engagement data during monitoring. For example, the monitoring unit can prioritize monitoring highly relevant engagement data. The monitoring unit can also prioritize monitoring highly relevant engagement data. For example, the monitoring unit can prioritize monitoring highly relevant engagement data. The monitoring unit can also postpone monitoring less relevant engagement data. For example, the monitoring unit can postpone monitoring less relevant engagement data. Furthermore, the monitoring unit can adjust the monitoring schedule according to the relevance of the engagement data. For example, the monitoring unit can adjust the monitoring schedule according to the relevance of the engagement data. This allows for prioritizing the monitoring of highly relevant data by adjusting the monitoring order based on the relevance of the engagement data. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input the relevance of the engagement data into a generating AI, and the generating AI can adjust the monitoring order.

[0120] The improvement unit can estimate the user's emotions and adjust the improvement method based on the estimated user emotions. For example, if the user is feeling stressed, the improvement unit can suggest a simple improvement method. For example, if the user is feeling stressed, the improvement unit can suggest a simple improvement method. For example, if the user is feeling stressed, the improvement unit can suggest a simple improvement method. The improvement unit can also suggest a detailed improvement method if the user is relaxed. For example, if the user is relaxed, the improvement unit can suggest a detailed improvement method. Furthermore, if the user is in a hurry, the improvement unit can suggest an improvement method that can be implemented quickly. For example, if the user is in a hurry, the improvement unit can suggest an improvement method that can be implemented quickly. In this way, by adjusting the improvement method according to the user's emotions, the optimal improvement method for the user can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user emotion data into a generating AI, which can then perform emotion estimation.

[0121] The improvement unit can analyze past engagement data to select the optimal improvement method during the improvement process. For example, the improvement unit can analyze past successes and apply similar improvement methods. The improvement unit can also analyze past failures and modify improvement methods. Furthermore, the improvement unit can select the optimal improvement method based on past engagement data. This allows for effective improvement by analyzing past engagement data and selecting the optimal improvement method. Some or all of the above processes in the improvement unit may be performed using AI, or not. For example, the improvement unit can input past engagement data into a generating AI, and the generating AI can select the optimal improvement method.

[0122] The improvement unit can apply different improvement algorithms depending on the content category during the improvement process. For example, the improvement unit can apply a specific marketing improvement algorithm to marketing content. The improvement unit can also apply a competitive improvement algorithm to competitive content. For example, the improvement unit can apply a competitive improvement algorithm to competitive content. Furthermore, the improvement unit can apply a trend improvement algorithm to trend content. For example, the improvement unit can apply a trend improvement algorithm to trend content. By applying different improvement algorithms depending on the content category, more accurate improvements can be made. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the content category into a generating AI and have the generating AI execute the application of the improvement algorithm.

[0123] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated emotions. For example, if the user is stressed, the improvement unit can prioritize high-priority improvements. The improvement unit can also prioritize high-priority improvements if the user is relaxed. Furthermore, if the user is in a hurry, the improvement unit can prioritize improvements that are immediately useful. In this way, by determining the priority of improvements according to the user's emotions, improvements that are important to the user can be prioritized. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0124] The improvement unit can make improvements while considering the geographical distribution of engagement data. For example, the improvement unit can analyze trends in target regions and make improvements relevant to those regions. The improvement unit can also make necessary improvements in line with regional marketing campaigns. For example, the improvement unit can make necessary improvements in line with regional marketing campaigns. Furthermore, the improvement unit can monitor regional events and news and make related improvements. For example, the improvement unit can monitor regional events and news and make related improvements. This allows for effective support of regional marketing activities by making improvements while considering the geographical distribution of engagement data. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not. For example, the improvement unit can input the geographical distribution of engagement data into a generating AI and have the generating AI execute the improvements.

[0125] The improvement unit can adjust its improvement strategy while considering social media trends. For example, the improvement unit can analyze social media hashtags in real time and make improvements related to trends. The improvement unit can also monitor posts from social media influencers and make influential improvements. Furthermore, the improvement unit can analyze social media engagement data and make improvements related to popular topics. By adjusting the improvement strategy while considering social media trends, it is possible to make effective improvements that are in line with trends. Some or all of the above processes in the improvement unit may be performed using AI, for example, or not. For example, the improvement unit can input social media trends into a generating AI and have the generating AI perform adjustments to the improvement strategy.

[0126] The improvement unit can estimate the user's emotions and determine the priority of improvements based on the estimated emotions. For example, if the user is stressed, the improvement unit can prioritize high-priority improvements. The improvement unit can also prioritize high-priority improvements if the user is relaxed. Furthermore, if the user is in a hurry, the improvement unit can prioritize improvements that are immediately useful. In this way, by determining the priority of improvements according to the user's emotions, improvements that are important to the user can be prioritized. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input user emotion data into a generating AI and have the generating AI perform emotion estimation.

[0127] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0128] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is nervous, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the essentials can be provided. In this way, by adjusting the display method of the analysis results according to the user's emotions, a display method that is easy for the user to understand can be provided.

[0129] The generation unit can estimate the user's emotions and adjust the way the generated content is presented based on those emotions. For example, if the user is relaxed, the content can be generated in a calm tone. If the user is in a hurry, the content can be generated in a concise and to-the-point manner. Furthermore, if the user is excited, the content can be generated with visually stimulating effects. By adjusting the way the content is presented according to the user's emotions, the system can provide the user with the most suitable content.

[0130] The distribution system can estimate the user's emotions and adjust the timing of deliveries based on those estimates. For example, if a user is stressed, the frequency of deliveries can be reduced to lessen the user's burden. Conversely, if a user is relaxed, the frequency of deliveries can be increased to provide more information. Furthermore, if a user is in a hurry, important information can be prioritized and delivered quickly. In this way, by adjusting the timing of deliveries according to the user's emotions, content can be delivered at the optimal time for the user.

[0131] The monitoring unit can estimate the user's emotions and adjust how engagement data is displayed based on those emotions. For example, if the user is stressed, it can provide a simple and easy-to-read display. If the user is relaxed, it can provide a display that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display that gets straight to the point. By adjusting how engagement data is displayed according to the user's emotions, it is possible to provide a display that is easy for the user to understand.

[0132] The improvement unit can estimate the user's emotions and adjust the improvement method based on those emotions. For example, if the user is stressed, it can suggest a simple improvement method. If the user is relaxed, it can suggest a more detailed improvement method. Furthermore, if the user is in a hurry, it can suggest an improvement method that can be implemented quickly. In this way, by adjusting the improvement method according to the user's emotions, it can provide the user with the most optimal improvement method.

[0133] The data collection unit can evaluate the reliability of the information it collects and prioritize the collection of highly reliable information. For example, it can score the reliability of information sources and prioritize the collection of information with high scores. It can also verify the source of information and prioritize information from official websites and reliable media. Furthermore, it can cross-check the content of the information and prioritize the collection of matching information. By prioritizing the collection of highly reliable information, it can provide accurate information.

[0134] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, it can perform a detailed analysis on highly important information, and a simplified analysis on less important information. Furthermore, it can determine the priority of the analysis according to the importance of the information. This allows for efficient analysis by adjusting the level of detail based on the importance of the information.

[0135] The generation unit can adjust the level of detail generated based on the importance of the content during generation. For example, it can generate content with detailed information for highly important content, and simplified content for less important content. Furthermore, it can determine the generation priority according to the importance of the content. This allows for efficient content generation by adjusting the level of detail based on the importance of the content.

[0136] The distribution team can select the optimal distribution time by considering the target audience's active time. For example, it can analyze the target audience's active time and distribute content during those times. It can also select the optimal distribution time by considering the target audience's lifestyle patterns. Furthermore, it can predict the optimal distribution time based on the target audience's past engagement data. By selecting the optimal distribution time considering the target audience's active time, engagement can be maximized.

[0137] The monitoring unit can adjust the level of detail of the monitoring based on the importance of the engagement data. For example, highly important engagement data can be monitored in detail, while less important engagement data can be monitored in a simplified manner. Furthermore, the monitoring priority can be determined according to the importance of the engagement data. This allows for efficient monitoring by adjusting the level of detail based on the importance of the engagement data.

[0138] The following briefly describes the processing flow for example form 2.

[0139] Step 1: The collection unit collects information. The collection unit can collect the latest information from the internet, for example, using crawlers or API tools. For example, the collection unit can collect information from specific websites using a web crawler. The collection unit can also retrieve information from specific databases using API tools. Furthermore, the collection unit can analyze the content of web pages using scraping tools and extract the necessary information. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can analyze the collected information using, for example, natural language processing techniques. For example, the analysis unit can segment the text data using morphological analysis and analyze the meaning of each word. The analysis unit can also analyze the structure of sentences using grammatical analysis and understand their meaning. Furthermore, the analysis unit can analyze the meaning of the text data using semantic analysis and extract specific topics. Step 3: The generation unit generates content based on the analysis results obtained by the analysis unit. The generation unit can, for example, create blog posts or social media posts using a generation AI. The generation unit can, for example, generate high-quality text content using a text generation AI (e.g., GPT-3). The generation unit can also generate visually appealing image content using an image generation AI. Furthermore, the generation unit can generate content that combines text and images using a multimodal generation AI. Step 4: The distribution unit distributes the content generated by the generation unit. The distribution unit can automatically publish and distribute content by integrating with platforms such as WordPress, HubSpot, and Hootsuite via API. For example, the distribution unit can automatically publish blog posts using the WordPress API. It can also automatically distribute marketing emails using the HubSpot API. Furthermore, it can automatically distribute social media posts using the Hootsuite API. Step 5: The monitoring unit monitors the engagement of content distributed by the distribution unit. The monitoring unit can monitor engagement data in real time, such as clicks, shares, comments, and likes. For example, the monitoring unit can monitor clicks to understand which content is being clicked the most. The monitoring unit can also monitor shares to understand which content is being shared the most. Furthermore, the monitoring unit can monitor comments to understand which content is being commented on the most. Step 6: The Improvement Department improves the content based on the engagement data obtained by the Monitoring Department. The Improvement Department can, for example, continuously learn using machine learning algorithms based on past content performance data. The Improvement Department can, for example, analyze the differences between successful and unsuccessful examples and incorporate improvements for future content creation. The Improvement Department can also identify which elements are key to success based on the engagement data and reflect this in future content creation.

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0141] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0142] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, distribution unit, monitoring unit, and improvement unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit can collect the latest information from the internet using the crawler or API tools of the smart device 14. The analysis unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 using natural language processing technology. The generation unit can create blog posts or social media posts using the generation AI of the smart device 14. The distribution unit can automatically publish and distribute content using the API integration of the smart device 14. The monitoring unit can monitor the engagement data of the smart device 14 in real time. The improvement unit can improve content based on the engagement data using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0144] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0145] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, distribution unit, monitoring unit, and improvement unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit can collect the latest information from the internet using the crawler or API tools of the smart glasses 214. The analysis unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 using natural language processing technology. The generation unit can create blog posts or social media posts using the generation AI of the smart glasses 214. The distribution unit can automatically publish and distribute content using the API integration of the smart glasses 214. The monitoring unit can monitor the engagement data of the smart glasses 214 in real time. The improvement unit can improve content based on the engagement data using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0160] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0161] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, distribution unit, monitoring unit, and improvement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit can collect the latest information from the internet using the crawler or API tools of the headset terminal 314. The analysis unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 using natural language processing technology. The generation unit can create blog posts or social media posts using the generation AI of the headset terminal 314. The distribution unit can automatically publish and distribute content using the API integration of the headset terminal 314. The monitoring unit can monitor the engagement data of the headset terminal 314 in real time. The improvement unit can improve content based on the engagement data using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0176] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0177] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0178] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0179] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0180] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0181] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0182] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0183] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0184] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0185] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0186] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0187] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0188] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0190] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0192] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, distribution unit, monitoring unit, and improvement unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect the latest information from the internet using the robot 414's crawler or API tools. The analysis unit can analyze the information collected by the specific processing unit 290 of the data processing unit 12 using natural language processing technology. The generation unit can create blog posts or social media posts using the robot 414's generation AI. The distribution unit can automatically publish and distribute content using the robot 414's API integration. The monitoring unit can monitor the robot 414's engagement data in real time. The improvement unit can improve content based on engagement data using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0193] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0194] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0195] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0196] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0197] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0198] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0199] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0200] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0201] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0202] 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.

[0203] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0204] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0205] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0206] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0207] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0208] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0209] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0210] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0211] (Note 1) The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates content based on the analysis results obtained by the analysis unit, A distribution unit that distributes the content generated by the generation unit, A monitoring unit monitors the engagement of content distributed by the aforementioned distribution unit, The system includes an improvement unit that improves content based on engagement data obtained by the monitoring unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects the latest information from the internet in conjunction with crawlers and API tools. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed using natural language processing technology to suggest content topics that are optimal for a specific period and target audience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate blog posts and social media posts using AI based on specified topics. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Create templates according to specific formats and brand guidelines. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned distribution unit, It integrates with platforms such as WordPress, HubSpot, and Hootsuite via APIs to automatically publish and distribute content. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, Monitor engagement data such as clicks, shares, comments, and likes in real time. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned improvement unit is, Using past content performance data, the machine learning algorithm continuously learns. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Evaluate the reliability of the information to be collected and prioritize the collection of highly reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Dynamically change the categories of information collected to gather information that aligns with marketing trends. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Considering the geographical distribution of the information to be collected, prioritize the collection of information relevant to specific regions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze social media trends in real time and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, adjust the level of detail based on the importance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, different generation algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is During generation, the generation priority is determined based on the submission date of the content. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is During generation, the generation order is adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned distribution unit, It estimates the user's emotions and adjusts the delivery timing based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned distribution unit, When broadcasting, select the optimal broadcast time considering the target audience's active time. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned distribution unit, When distributing content, apply different distribution strategies depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned distribution unit, It estimates user sentiment and prioritizes the content delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned distribution unit, When distributing content, the distribution should take into account the geographical distribution of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned distribution unit, When broadcasting, adjust your broadcasting strategy to take social media trends into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned monitoring unit, It estimates user sentiment and adjusts how engagement data is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned monitoring unit, During monitoring, adjust the level of detail based on the importance of the engagement data. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the category of engagement data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned monitoring unit, It estimates user sentiment and prioritizes engagement data based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned monitoring unit, During monitoring, prioritize monitoring based on when engagement data is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, During monitoring, adjust the order of monitoring based on the relevance of engagement data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned improvement unit is, It estimates user sentiment and adjusts improvement methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned improvement unit is, When making improvements, we analyze past engagement data to select the most suitable improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned improvement unit is, When making improvements, different improvement algorithms are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned improvement unit is, We estimate user emotions and determine improvement priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned improvement unit is, When making improvements, take into account the geographical distribution of engagement data. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned improvement unit is, When making improvements, adjust your improvement strategy to take social media trends into consideration. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0212] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The information collection unit, An analysis unit analyzes the information collected by the aforementioned collection unit, A generation unit that generates content based on the analysis results obtained by the analysis unit, A distribution unit that distributes the content generated by the generation unit, A monitoring unit monitors the engagement of content distributed by the aforementioned distribution unit, The system includes an improvement unit that improves content based on engagement data obtained by the monitoring unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects the latest information from the internet in conjunction with crawlers and API tools. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed using natural language processing technology to suggest content topics that are optimal for a specific period and target audience. The system according to feature 1.

4. The generating unit is Generate blog posts and social media posts using AI based on specified topics. The system according to feature 1.

5. The generating unit is Create templates according to specific formats and brand guidelines. The system according to feature 1.

6. The aforementioned monitoring unit, Monitor engagement data such as clicks, shares, comments, and likes in real time. The system according to feature 1.

7. The aforementioned improvement unit is, Using past content performance data, the machine learning algorithm continuously learns. The system according to feature 1.

Images