System

Abstract

An object of a system according to an embodiment is to analyze data on the Internet and generate post contents suitable for a target user group.SOLUTION: A system includes a data collection unit, an analysis unit, a user layer identification unit, a post generation unit, and a configuration unit. The data collection unit collects data on the Internet. The analysis unit analyzes the data collected by the data collection unit. The user layer identification unit identifies the target user layer based on the data analyzed by the analysis unit. The post generation unit generates a post content suitable for the target user layer identified by the user layer identification unit. The composing unit composes the post content generated by the post generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of making it difficult to effectively utilize the vast amount of data on the Internet to generate posting content appropriate for target users.

[0005] The system according to the embodiment aims to analyze data on the Internet and generate posting content suitable for a target user group. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a user demographic identification unit, a post generation unit, and a configuration unit. The data collection unit collects data on the Internet. The analysis unit analyzes the data collected by the data collection unit. The user demographic identification unit identifies a target user demographic based on the data analyzed by the analysis unit. The post generation unit generates post content suitable for the target user demographic identified by the user demographic identification unit. The configuration unit configures the post content generated by the post generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze data on the Internet and generate posting content suitable for a target user group. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The automatic generation tool according to an embodiment of the present invention is a tool that automatically generates text to be posted to press releases, official social media accounts, etc. This tool considers the content of posts based on a huge amount of data and generates and organizes content that will appeal appropriately to the target user demographic. This allows the automatic generation tool to efficiently generate post content suited to the target user demographic, significantly improving the efficiency and effectiveness of a company's public relations activities.

[0029] The automatic generation tool according to the embodiment includes a data collection unit, an analysis unit, a user demographic identification unit, a post generation unit, and a configuration unit. The data collection unit collects data from the Internet, such as social media posts, news articles, and blog entries. The data collection unit can also collect data on past successful press releases and social media posts and analyze their commonalities and characteristics. The analysis unit analyzes the data collected by the data collection unit, such as using techniques like text mining, sentiment analysis, and topic modeling. The analysis unit can also calculate sentiment scores for the collected data and prioritize analysis of data with high positive scores. The user demographic identification unit identifies a target user demographic based on the data analyzed by the analysis unit. For example, the analysis unit can analyze a user demographic with a specific age group, gender, or interests, and generate content appropriate for that user demographic. The user demographic identification unit can also analyze users' purchasing histories and behavioral patterns to achieve more accurate targeting. The post generation unit generates post content appropriate for the target user demographic identified by the user demographic identification unit. For example, the automatic generation tool according to the embodiment generates a press release regarding a new product announcement or a social media post containing campaign information. The post generation unit can also use an emotion estimation function to select an expression that evokes the most positive emotions in users. The composition unit composes the content of the post generated by the post generation unit. For example, in the case of a press release, the composition includes elements such as a headline, a lead sentence, a main text, and a quote. In the case of a social media post, the composition unit can also use the emotion estimation function to select a composition that most emotionally resonates with users. This allows the automatic generation tool according to the embodiment to efficiently generate and compose content suitable for a target user demographic. For example, the automatic generation tool can significantly improve the efficiency and effectiveness of a company's public relations activities.

[0030] The data collection unit can reflect the latest information, including news or trend information. For example, the data collection unit allows the generation AI to collect the latest trend information from news sites and social media in real time and reflect it in the analysis. For example, the latest news articles and trending hashtags are included in the data. The data collection unit also integrates real-time news feeds into the generation AI to build a system that always reflects the latest information. For example, it uses a news API to obtain the latest information. The data collection unit also allows the generation AI to regularly update trend information and reflect it in the analysis. For example, it collects the latest trend data every day and uses it in the analysis. This allows the generation AI to reflect the latest information and always generate fresh post content.

[0031] The data collection unit can also incorporate data from different industries or fields, promoting crossover innovation. For example, the data collection unit allows the generation AI to collect data from different industries and incorporate it into analysis. For example, data from the technology and entertainment fields can be integrated and analyzed. The data collection unit also builds a system that crosses data from different fields to gain new insights. For example, data from the medical and fashion fields can be combined. The data collection unit also allows the generation AI to analyze data from different industries to find similarities and differences. For example, data from the finance and education industries can be compared and analyzed. This allows new insights to be gained by incorporating data from different industries and fields.

[0032] The data collection unit can analyze multimodal information, including the user's voice or image data. In the data collection unit, for example, the generation AI collects the user's voice data and performs voice analysis. For example, it analyzes the content and tone of the user's speech to understand their emotions and intentions. In addition, the data collection unit collects image data and performs image analysis. For example, it analyzes photos and illustrations posted by the user to extract visual information. In addition, the data collection unit integrates multimodal information, and the generation AI simultaneously analyzes the voice data and image data. For example, it analyzes the user's facial expressions and voice tone to infer their emotions. This makes it possible to obtain more multifaceted insights by analyzing multimodal information, including voice and image data.

[0033] The data collection unit can also incorporate data from different regions or cultural spheres, allowing for analysis from a global perspective. In the data collection unit, for example, the generation AI collects data from different regions and analyzes it from a global perspective. For example, it integrates and analyzes data from Asia, Europe, and America. The data collection unit also collects data from different cultural spheres and performs analysis that takes cultural differences into account. For example, it compares and analyzes social media post data from Japan and America. In addition, in the data collection unit, the generation AI collects data in multiple languages, allowing for analysis from a global perspective. For example, it analyzes data in English, French, Chinese, etc. This makes it possible to incorporate data from different regions and cultural spheres and perform analysis from a global perspective.

[0034] The user demographic identification unit analyzes a user's purchasing history or behavioral patterns to enable more accurate targeting. The user demographic identification unit, for example, analyzes a user's purchasing history to identify a user demographic that is interested in a specific product or service as a target. For example, targeting is performed based on the category of products purchased in the past. The user demographic identification unit also analyzes a user's behavioral patterns to identify a user demographic that exhibits specific behavior as a target. For example, users who frequently visit a website are targeted. The user demographic identification unit also integrates the purchasing history and behavioral patterns to build a system that enables more accurate targeting. For example, users who exhibit specific behavior after purchasing a specific product are targeted. In this way, more accurate targeting is possible by analyzing a user's purchasing history and behavioral patterns.

[0035] The user demographic identification unit can analyze the follower demographic of an influencer on social media and identify an influential user demographic. The user demographic identification unit, for example, analyzes the follower demographic of an influencer on social media and identifies an influential user demographic. For example, it analyzes the interests of the followers of a specific influencer. The user demographic identification unit also analyzes demographic data of the influencer's follower demographic and identifies a target user demographic. For example, it performs targeting based on data such as age, gender, and region. The user demographic identification unit also analyzes the behavioral patterns of the influencer's follower demographic and identifies an influential user demographic. For example, it analyzes reactions and engagement to specific posts. In this way, it is possible to identify an influential user demographic by analyzing the follower demographic of an influencer.

[0036] The user demographic identification unit can integrate and analyze data from different platforms. For example, the user demographic identification unit integrates data collected from different platforms to identify a target user demographic. For example, data from social media, blogs, and forums is analyzed as a single data set. The user demographic identification unit also analyzes data from each platform to build a system that identifies a common target user demographic. For example, it analyzes common user interests across multiple platforms. The user demographic identification unit also collects data from different platforms in real time to dynamically identify a target user demographic. For example, it simultaneously analyzes data from social media and blogs to perform targeting. This enables more comprehensive targeting by integrating and analyzing data from different platforms.

[0037] The user demographic identification unit can analyze the user's lifestyle or values ​​to perform more personalized targeting. The user demographic identification unit, for example, analyzes the user's lifestyle data to identify a user demographic with a specific lifestyle as a target. For example, outdoor enthusiasts or health-conscious users are targeted. The user demographic identification unit also analyzes the user's value data to identify a user demographic with specific values ​​as a target. For example, users who are interested in environmental protection are targeted. The user demographic identification unit also integrates lifestyle and values ​​to build a system for more personalized targeting. For example, users with specific lifestyles and values ​​are targeted. This makes it possible to perform more personalized targeting by analyzing the user's lifestyle and values.

[0038] The post generation unit can learn from success stories and incorporate their patterns. For example, the post generation unit's generation AI learns from past success stories and incorporates those patterns to generate post content. For example, it analyzes the characteristics of posts that have received high engagement in the past and uses similar patterns. The post generation unit also builds a database of success stories, and the generation AI references that database to generate post content. For example, it uses templates of successful press releases and social media posts. The post generation unit also builds a system in which the generation AI learns from past success stories and generates new post content based on those patterns. For example, it incorporates keywords and phrases from success stories. In this way, by learning from past success stories and incorporating their patterns, it is possible to increase the effectiveness of post content.

[0039] The post generation unit can generate multilingual posts that support different languages ​​or cultures. In the post generation unit, for example, a generation AI generates post content that supports different languages. For example, multilingual posts such as English, Japanese, and French are generated. In addition, in order to generate post content that supports different cultures, the generation AI takes cultural backgrounds and customs into consideration. For example, expressions and tones appropriate for a particular culture are used. In addition, in order to generate multilingual post content, the post generation unit integrates an automatic translation function. For example, the post content is automatically translated into multiple languages ​​and expressions appropriate for each language are used. This makes it possible to generate multilingual posts that support different languages ​​and cultures, thereby catering to a global user base.

[0040] The post generation unit can analyze a user's voice input and convert it from voice to text. In the post generation unit, for example, a generation AI analyzes a user's voice input and converts it from voice to text. For example, what the user says is automatically converted into text to generate the post content. The post generation unit also uses voice recognition technology to build a system that converts a user's voice input into text in real time. For example, what the user says is instantly converted into text. The post generation unit also takes into account the tone and emotion of the voice when the generation AI analyzes the voice input and converts it from voice to text. For example, it generates text that reflects the user's emotions. This makes it possible to generate posts using voice input by analyzing a user's voice input and converting it from voice to text.

[0041] The post generation unit can generate posts that include visual content. In the post generation unit, for example, a generation AI generates post content that includes visual content. For example, the post generation unit automatically generates SNS posts that include images and videos. In addition, in order to generate visual content, the generation AI integrates image generation technology and video editing technology. For example, the image and video are generated based on user instructions. In addition, the post generation unit builds a system that evaluates the quality of the visual content when the generation AI generates post content that includes visual content. For example, the system evaluates the image resolution and video frame rate. This enables posts that include visual content to be generated, making them visually appealing.

[0042] The composition unit can create multimedia compositions that combine different media formats. For example, the composition unit allows the generation AI to compose post content that combines different media formats. For example, it generates press releases and social media posts that combine text, images, and videos. In addition, the composition unit allows the generation AI to consider the characteristics of each media format in order to create multimedia composition. For example, it links text headlines and image captions. In addition, the composition unit builds a system that evaluates the quality of each media format when the generation AI composes post content that combines different media formats. For example, it evaluates image resolution and video frame rate. This enables visually appealing posts to be created by creating multimedia compositions that combine different media formats.

[0043] The composition unit can analyze a user's browsing history and behavioral patterns to propose the optimal composition. The composition unit, for example, analyzes a user's browsing history and builds a system that proposes the optimal composition of post content. For example, it proposes a composition based on data on press releases and SNS posts viewed in the past. The composition unit also analyzes a user's behavioral patterns and proposes the optimal composition of post content. For example, it proposes a composition that is likely to be viewed during a specific time period. The composition unit also integrates the browsing history and behavioral patterns to build a system that proposes the optimal composition. For example, it proposes a composition that is likely to be viewed on a specific device. In this way, it is possible to propose the optimal composition by analyzing a user's browsing history and behavioral patterns.

[0044] The composition unit can create compositions optimized for different platforms. For example, the generation AI in the composition unit creates post content optimized for different platforms. For example, it generates a short catchy slogan for social media and a detailed description for blogs. The composition unit also considers the characteristics of each platform and builds a system that proposes the optimal composition. For example, social media makes extensive use of images and videos, while blogs are composed primarily of text. The composition unit also takes into account the algorithms of each platform when the generation AI creates post content optimized for different platforms. For example, it adjusts the composition based on the engagement algorithm of the social media platform. This makes it possible to create posts that are suitable for each platform by creating compositions optimized for different platforms.

[0045] The composition unit can adjust the composition in real time based on user feedback. For example, the composition unit builds a system in which a generation AI collects user feedback in real time and adjusts the composition of the post content. For example, it changes the composition based on user comments and reactions. The composition unit also dynamically adjusts the composition of the post content based on user feedback data. For example, it prioritizes the use of components that receive a lot of positive feedback. The composition unit also uses a generation AI to analyze user feedback in real time and continuously optimize the composition of the post content. For example, it corrects parts that receive a lot of negative feedback. This allows for more effective posts by adjusting the composition in real time based on user feedback.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can also incorporate data from different industries or fields, promoting crossover innovation. For example, the generative AI collects data from different industries and incorporates it into analysis. For example, data from the technology and entertainment fields can be integrated and analyzed. The data collection unit can also build a system that crosses data from different fields to gain new insights. For example, data from the medical and fashion fields can be combined. Furthermore, the data collection unit allows the generative AI to analyze data from different industries to find similarities and differences. For example, data from the finance and education industries can be compared and analyzed. This allows new insights to be gained by incorporating data from different industries and fields.

[0048] The data collection unit can also incorporate data from different regions or cultural spheres to perform analysis from a global perspective. For example, the generation AI collects data from different regions and analyzes it from a global perspective. For example, data from Asia, Europe, and America can be integrated and analyzed. The data collection unit also collects data from different cultural spheres and performs analysis that takes cultural differences into account. For example, it can compare and analyze social media post data from Japan and the United States. Furthermore, in order to perform analysis from a global perspective, the data collection unit allows the generation AI to collect data in multiple languages. For example, it can analyze data in English, French, Chinese, etc. This makes it possible to incorporate data from different regions and cultural spheres and perform analysis from a global perspective.

[0049] The user demographic identification unit can analyze a user's purchasing history or behavioral patterns to perform more accurate targeting. For example, it can analyze a user's purchasing history to identify a user demographic that is interested in a specific product or service as a target. For example, it can perform targeting based on the category of products purchased in the past. The user demographic identification unit can also analyze a user's behavioral patterns to identify a user demographic that exhibits specific behavior as a target. For example, it can target users who frequently visit a website. Furthermore, the user demographic identification unit can integrate the purchasing history and behavioral patterns to build a system that performs more accurate targeting. For example, it can target users who exhibit specific behavior after purchasing a specific product. This makes it possible to perform more accurate targeting by analyzing a user's purchasing history and behavioral patterns.

[0050] The user demographic identification unit can analyze the follower demographic of an influencer on social media to identify an influential user demographic. For example, the user demographic identification unit can analyze the follower demographic of an influencer on social media to identify an influential user demographic. For example, the user demographic identification unit can analyze the interests of the followers of a specific influencer. The user demographic identification unit can also analyze demographic data of the influencer's follower demographic to identify a target user demographic. For example, the user demographic identification unit can perform targeting based on data such as age, gender, and region. The user demographic identification unit can also analyze the behavioral patterns of the influencer's follower demographic to identify an influential user demographic. For example, the user demographic identification unit can analyze reactions and engagement to specific posts. In this way, by analyzing the follower demographic of an influencer, it is possible to identify an influential user demographic.

[0051] The post generation unit can generate posts that include visual content. For example, the generation AI generates post content that includes visual content. For example, it automatically generates social media posts that include images and videos. In addition, the post generation unit integrates image generation technology and video editing technology to generate visual content. For example, it generates images and videos based on user instructions. Furthermore, the post generation unit builds a system that evaluates the quality of the visual content when the generation AI generates post content that includes visual content. For example, it evaluates the image resolution and video frame rate. This enables posts that include visual content to be generated and are visually appealing.

[0052] The composition unit can create multimedia compositions that combine different media formats. For example, the generation AI can compose post content that combines different media formats. For example, it can generate press releases and social media posts that combine text, images, and videos. In addition, the composition unit allows the generation AI to consider the characteristics of each media format in order to create multimedia composition. For example, it can link text headlines and image captions. Furthermore, when the generation AI composes post content that combines different media formats, the composition unit builds a system that evaluates the quality of each media format. For example, it evaluates image resolution and video frame rate. This allows for visually appealing posts by creating multimedia compositions that combine different media formats.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data collection department collects data from the internet, such as social media posts, news articles, blog entries, etc. They can also collect data on past successful press releases and social media posts and analyze their commonalities and characteristics. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the data using techniques such as text mining, sentiment analysis, and topic modeling. It can also calculate sentiment scores for the collected data and prioritize analysis of data with high positive scores. Step 3: The user demographic identification unit identifies the target user demographic based on the data analyzed by the analysis unit. For example, it analyzes user demographics with specific age groups, genders, or interests, and generates content appropriate for those users. It can also analyze users' purchasing history and behavioral patterns to achieve more precise targeting. Step 4: The post generation unit generates post content appropriate for the target user group identified by the user group identification unit. For example, it generates a press release about a new product announcement or a social media post containing campaign information. It can also use the emotion estimation function to select expressions that evoke the most positive emotions in users. Step 5: The composition unit composes the content of the post generated by the post generation unit. For example, in the case of a press release, it composes it with elements such as a headline, lead sentence, body text, and quotes. In the case of a social media post, it composes it with a short catchy copy and hashtags. It can also use an emotion estimation function to select the composition that most resonates with the user emotionally.

[0055] (Example 2) The automatic generation tool according to an embodiment of the present invention is a tool that automatically generates text to be posted to press releases, official social media accounts, etc. This tool considers the content of posts based on a huge amount of data and generates and organizes content that will appeal appropriately to the target user demographic. This allows the automatic generation tool to efficiently generate post content suited to the target user demographic, significantly improving the efficiency and effectiveness of a company's public relations activities.

[0056] The automatic generation tool according to the embodiment includes a data collection unit, an analysis unit, a user demographic identification unit, a post generation unit, and a configuration unit. The data collection unit collects data from the Internet, such as social media posts, news articles, and blog entries. The data collection unit can also collect data on past successful press releases and social media posts and analyze their commonalities and characteristics. The analysis unit analyzes the data collected by the data collection unit, such as using techniques like text mining, sentiment analysis, and topic modeling. The analysis unit can also calculate sentiment scores for the collected data and prioritize analysis of data with high positive scores. The user demographic identification unit identifies a target user demographic based on the data analyzed by the analysis unit. For example, the analysis unit can analyze a user demographic with a specific age group, gender, or interests, and generate content appropriate for that user demographic. The user demographic identification unit can also analyze users' purchasing histories and behavioral patterns to achieve more accurate targeting. The post generation unit generates post content appropriate for the target user demographic identified by the user demographic identification unit. For example, the automatic generation tool according to the embodiment generates a press release regarding a new product announcement or a social media post containing campaign information. The post generation unit can also use an emotion estimation function to select an expression that evokes the most positive emotions in users. The composition unit composes the content of the post generated by the post generation unit. For example, in the case of a press release, the composition includes elements such as a headline, a lead sentence, a main text, and a quote. In the case of a social media post, the composition unit can also use the emotion estimation function to select a composition that most emotionally resonates with users. This allows the automatic generation tool according to the embodiment to efficiently generate and compose content suitable for a target user demographic. For example, the automatic generation tool can significantly improve the efficiency and effectiveness of a company's public relations activities.

[0057] The data collection unit uses the emotion estimation function to analyze users' emotional reactions and prioritizes the use of data that indicates positive reactions. For example, the data collection unit performs emotion analysis on data collected by the generation AI and prioritizes the use of data that indicates positive emotional reactions. For example, it analyzes user comments and reviews and extracts data with many positive ratings. The data collection unit also uses the emotion estimation function to calculate an emotion score for the collected data and prioritizes the analysis of data with a high positive score. For example, it filters data based on the emotion score of SNS posts. The data collection unit also uses the emotion estimation function to exclude data that indicates negative reactions from the data collected by the generation AI and analyzes only positive data. For example, it excludes negative comments and reviews. This prioritizes the use of data that indicates positive reactions, making it possible to generate post content that is more likely to attract users' attention.

[0058] The data collection unit can reflect the latest information, including news or trend information. For example, the data collection unit allows the generation AI to collect the latest trend information from news sites and social media in real time and reflect it in the analysis. For example, the latest news articles and trending hashtags are included in the data. The data collection unit also integrates real-time news feeds into the generation AI to build a system that always reflects the latest information. For example, it uses a news API to obtain the latest information. The data collection unit also allows the generation AI to regularly update trend information and reflect it in the analysis. For example, it collects the latest trend data every day and uses it in the analysis. This allows the generation AI to reflect the latest information and always generate fresh post content.

[0059] The data collection unit can also incorporate data from different industries or fields, promoting crossover innovation. For example, the data collection unit allows the generation AI to collect data from different industries and incorporate it into analysis. For example, data from the technology and entertainment fields can be integrated and analyzed. The data collection unit also builds a system that crosses data from different fields to gain new insights. For example, data from the medical and fashion fields can be combined. The data collection unit also allows the generation AI to analyze data from different industries to find similarities and differences. For example, data from the finance and education industries can be compared and analyzed. This allows new insights to be gained by incorporating data from different industries and fields.

[0060] The data collection unit can analyze multimodal information, including the user's voice or image data. In the data collection unit, for example, the generation AI collects the user's voice data and performs voice analysis. For example, it analyzes the content and tone of the user's speech to understand their emotions and intentions. In addition, the data collection unit collects image data and performs image analysis. For example, it analyzes photos and illustrations posted by the user to extract visual information. In addition, the data collection unit integrates multimodal information, and the generation AI simultaneously analyzes the voice data and image data. For example, it analyzes the user's facial expressions and voice tone to infer their emotions. This makes it possible to obtain more multifaceted insights by analyzing multimodal information, including voice and image data.

[0061] The data collection unit can also incorporate data from different regions or cultural spheres, allowing for analysis from a global perspective. In the data collection unit, for example, the generation AI collects data from different regions and analyzes it from a global perspective. For example, it integrates and analyzes data from Asia, Europe, and America. The data collection unit also collects data from different cultural spheres and performs analysis that takes cultural differences into account. For example, it compares and analyzes social media post data from Japan and America. In addition, in the data collection unit, the generation AI collects data in multiple languages, allowing for analysis from a global perspective. For example, it analyzes data in English, French, Chinese, etc. This makes it possible to incorporate data from different regions and cultural spheres and perform analysis from a global perspective.

[0062] The data collection unit can use the emotion estimation function to monitor users' emotional reactions to collected data in real time, thereby improving the quality of the data. The data collection unit, for example, uses the emotion estimation function to build a system that monitors users' emotional reactions to collected data in real time. For example, it displays emotion scores of user comments and reviews in real time. The data collection unit also improves the quality of the data based on the users' emotional reaction data. For example, it prioritizes analysis of data with a high number of positive emotional reactions. The data collection unit also uses the emotion estimation function to update the emotion scores of the collected data in real time, thereby continuously improving the quality of the data. For example, it excludes data with a high number of negative emotional reactions. In this way, the quality of the data can be improved by monitoring users' emotional reactions in real time.

[0063] The user demographic identification unit can use the emotion estimation function to narrow down the target demographic based on the user's emotional response. For example, the user demographic identification unit uses the emotion estimation function to analyze the user's emotional response and identify a user demographic that shows positive emotions as a target. For example, it targets users with high emotional scores for joy and excitement. The user demographic identification unit also builds a system that narrows down the target user demographic based on the user's emotional response data. For example, it identifies age groups and genders that show a high number of positive emotional responses. The user demographic identification unit also uses the emotion estimation function to analyze the user's emotional response in real time and dynamically narrow down the target user demographic. For example, it identifies a user demographic whose emotional response changes during a campaign period. This enables more effective targeting by narrowing down the target demographic based on the user's emotional response.

[0064] The user demographic identification unit analyzes a user's purchasing history or behavioral patterns to enable more accurate targeting. The user demographic identification unit, for example, analyzes a user's purchasing history to identify a user demographic that is interested in a specific product or service as a target. For example, targeting is performed based on the category of products purchased in the past. The user demographic identification unit also analyzes a user's behavioral patterns to identify a user demographic that exhibits specific behavior as a target. For example, users who frequently visit a website are targeted. The user demographic identification unit also integrates the purchasing history and behavioral patterns to build a system that enables more accurate targeting. For example, users who exhibit specific behavior after purchasing a specific product are targeted. In this way, more accurate targeting is possible by analyzing a user's purchasing history and behavioral patterns.

[0065] The user demographic identification unit can analyze the follower demographic of an influencer on social media and identify an influential user demographic. The user demographic identification unit, for example, analyzes the follower demographic of an influencer on social media and identifies an influential user demographic. For example, it analyzes the interests of the followers of a specific influencer. The user demographic identification unit also analyzes demographic data of the influencer's follower demographic and identifies a target user demographic. For example, it performs targeting based on data such as age, gender, and region. The user demographic identification unit also analyzes the behavioral patterns of the influencer's follower demographic and identifies an influential user demographic. For example, it analyzes reactions and engagement to specific posts. In this way, it is possible to identify an influential user demographic by analyzing the follower demographic of an influencer.

[0066] The user demographic identification unit can integrate and analyze data from different platforms. For example, the user demographic identification unit integrates data collected from different platforms to identify a target user demographic. For example, data from social media, blogs, and forums is analyzed as a single data set. The user demographic identification unit also analyzes data from each platform to build a system that identifies a common target user demographic. For example, it analyzes common user interests across multiple platforms. The user demographic identification unit also collects data from different platforms in real time to dynamically identify a target user demographic. For example, it simultaneously analyzes data from social media and blogs to perform targeting. This enables more comprehensive targeting by integrating and analyzing data from different platforms.

[0067] The user demographic identification unit can analyze the user's lifestyle or values ​​to perform more personalized targeting. The user demographic identification unit, for example, analyzes the user's lifestyle data to identify a user demographic with a specific lifestyle as a target. For example, outdoor enthusiasts or health-conscious users are targeted. The user demographic identification unit also analyzes the user's value data to identify a user demographic with specific values ​​as a target. For example, users who are interested in environmental protection are targeted. The user demographic identification unit also integrates lifestyle and values ​​to build a system for more personalized targeting. For example, users with specific lifestyles and values ​​are targeted. This makes it possible to perform more personalized targeting by analyzing the user's lifestyle and values.

[0068] The user demographic identification unit can use the emotion estimation function to identify the emotional needs of the target user demographic and perform targeting according to those needs. The user demographic identification unit, for example, uses the emotion estimation function to identify the emotional needs of the target user demographic. For example, it analyzes the emotion scores of users' comments and reviews to understand their emotional needs. The user demographic identification unit also builds a system that performs targeting according to their emotional needs based on users' emotional response data. For example, it targets a user demographic that shows positive emotions. The user demographic identification unit also uses the emotion estimation function to analyze the emotional needs of the target user demographic in real time and perform dynamic targeting. For example, it identifies a user demographic whose emotional response changes during a campaign period. This allows for more effective targeting by identifying the emotional needs of the target user demographic.

[0069] The post generation unit can use the emotion estimation function to select an expression that evokes the most positive emotion in the user. For example, the post generation unit uses the emotion estimation function to select an expression that evokes the most positive emotion in the user. For example, expressions with high emotion scores for joy and excitement are preferentially used. The post generation unit also builds a system that selects expressions that elicit positive emotions based on the user's emotional response data. For example, it analyzes past post data and extracts expressions that have received many positive responses. The post generation unit also uses the emotion estimation function to monitor the user's emotional response to the generated post content in real time and dynamically selects expressions that elicit positive emotions. For example, it adjusts the expression based on the user's real-time feedback. In this way, the effectiveness of the post content can be maximized by selecting an expression that evokes the most positive emotion in the user.

[0070] The post generation unit can learn from success stories and incorporate their patterns. For example, the post generation unit's generation AI learns from past success stories and incorporates those patterns to generate post content. For example, it analyzes the characteristics of posts that have received high engagement in the past and uses similar patterns. The post generation unit also builds a database of success stories, and the generation AI references that database to generate post content. For example, it uses templates of successful press releases and social media posts. The post generation unit also builds a system in which the generation AI learns from past success stories and generates new post content based on those patterns. For example, it incorporates keywords and phrases from success stories. In this way, by learning from past success stories and incorporating their patterns, it is possible to increase the effectiveness of post content.

[0071] The post generation unit can generate multilingual posts that support different languages ​​or cultures. In the post generation unit, for example, a generation AI generates post content that supports different languages. For example, multilingual posts such as English, Japanese, and French are generated. In addition, in order to generate post content that supports different cultures, the generation AI takes cultural backgrounds and customs into consideration. For example, expressions and tones appropriate for a particular culture are used. In addition, in order to generate multilingual post content, the post generation unit integrates an automatic translation function. For example, the post content is automatically translated into multiple languages ​​and expressions appropriate for each language are used. This makes it possible to generate multilingual posts that support different languages ​​and cultures, thereby catering to a global user base.

[0072] The post generation unit can analyze a user's voice input and convert it from voice to text. In the post generation unit, for example, a generation AI analyzes a user's voice input and converts it from voice to text. For example, what the user says is automatically converted into text to generate the post content. The post generation unit also uses voice recognition technology to build a system that converts a user's voice input into text in real time. For example, what the user says is instantly converted into text. The post generation unit also takes into account the tone and emotion of the voice when the generation AI analyzes the voice input and converts it from voice to text. For example, it generates text that reflects the user's emotions. This makes it possible to generate posts using voice input by analyzing a user's voice input and converting it from voice to text.

[0073] The post generation unit can generate posts that include visual content. In the post generation unit, for example, a generation AI generates post content that includes visual content. For example, the post generation unit automatically generates SNS posts that include images and videos. In addition, in order to generate visual content, the generation AI integrates image generation technology and video editing technology. For example, the image and video are generated based on user instructions. In addition, the post generation unit builds a system that evaluates the quality of the visual content when the generation AI generates post content that includes visual content. For example, the system evaluates the image resolution and video frame rate. This enables posts that include visual content to be generated, making them visually appealing.

[0074] The post generation unit can use the emotion estimation function to monitor users' emotional reactions to the generated post content in real time and optimize the post content. The post generation unit, for example, uses the emotion estimation function to build a system that monitors users' emotional reactions to the generated post content in real time. For example, it displays emotion scores of users' comments and reactions in real time. The post generation unit also optimizes the generated post content based on the user's emotional reaction data. For example, it prioritizes the use of expressions and tones that evoke a high number of positive emotional reactions. The post generation unit also uses the emotion estimation function to update the emotion scores of the generated post content in real time and continuously optimize the post content. For example, it corrects parts that evoke a high number of negative emotional reactions. In this way, by monitoring users' emotional reactions to the generated post content in real time and optimizing the post content, more effective posts are possible.

[0075] The composition unit can use the emotion estimation function to select a composition that the user will most emotionally empathize with. For example, the composition unit uses the emotion estimation function to select a composition that the user will most emotionally empathize with. For example, it prioritizes the use of headlines and lead sentences with high emotion scores. The composition unit also builds a system that selects a composition that is likely to be emotionally empathized with based on user emotional response data. For example, it extracts components that have a high number of positive emotional responses. The composition unit also uses the emotion estimation function to monitor users' emotional responses to the composition of the generated post content in real time, and dynamically selects a composition that is likely to be emotionally empathized with. For example, it adjusts the composition based on real-time user feedback. In this way, the effectiveness of the post content can be maximized by selecting a composition that the user will most emotionally empathize with.

[0076] The composition unit can create multimedia compositions that combine different media formats. For example, the composition unit allows the generation AI to compose post content that combines different media formats. For example, it generates press releases and social media posts that combine text, images, and videos. In addition, the composition unit allows the generation AI to consider the characteristics of each media format in order to create multimedia composition. For example, it links text headlines and image captions. In addition, the composition unit builds a system that evaluates the quality of each media format when the generation AI composes post content that combines different media formats. For example, it evaluates image resolution and video frame rate. This enables visually appealing posts to be created by creating multimedia compositions that combine different media formats.

[0077] The composition unit can analyze a user's browsing history and behavioral patterns to propose the optimal composition. The composition unit, for example, analyzes a user's browsing history and builds a system that proposes the optimal composition of post content. For example, it proposes a composition based on data on press releases and SNS posts viewed in the past. The composition unit also analyzes a user's behavioral patterns and proposes the optimal composition of post content. For example, it proposes a composition that is likely to be viewed during a specific time period. The composition unit also integrates the browsing history and behavioral patterns to build a system that proposes the optimal composition. For example, it proposes a composition that is likely to be viewed on a specific device. In this way, it is possible to propose the optimal composition by analyzing a user's browsing history and behavioral patterns.

[0078] The composition unit can create compositions optimized for different platforms. For example, the generation AI in the composition unit creates post content optimized for different platforms. For example, it generates a short catchy slogan for social media and a detailed description for blogs. The composition unit also considers the characteristics of each platform and builds a system that proposes the optimal composition. For example, social media makes extensive use of images and videos, while blogs are composed primarily of text. The composition unit also takes into account the algorithms of each platform when the generation AI creates post content optimized for different platforms. For example, it adjusts the composition based on the engagement algorithm of the social media platform. This makes it possible to create posts that are suitable for each platform by creating compositions optimized for different platforms.

[0079] The composition unit can adjust the composition in real time based on user feedback. For example, the composition unit builds a system in which a generation AI collects user feedback in real time and adjusts the composition of the post content. For example, it changes the composition based on user comments and reactions. The composition unit also dynamically adjusts the composition of the post content based on user feedback data. For example, it prioritizes the use of components that receive a lot of positive feedback. The composition unit also uses a generation AI to analyze user feedback in real time and continuously optimize the composition of the post content. For example, it corrects parts that receive a lot of negative feedback. This allows for more effective posts by adjusting the composition in real time based on user feedback.

[0080] The composition unit can use the emotion estimation function to monitor users' emotional reactions to the composed post content and optimize the composition. The composition unit, for example, uses the emotion estimation function to build a system that monitors users' emotional reactions to the composed post content. For example, it displays emotion scores of users' comments and reactions in real time. The composition unit also optimizes the composition of the post content based on the user's emotional reaction data. For example, it prioritizes the use of components with a high number of positive emotional reactions. The composition unit also uses the emotion estimation function to update the emotion score of the composed post content in real time and continuously optimize the composition. For example, it modifies parts with a high number of negative emotional reactions. In this way, by monitoring users' emotional reactions to the composed post content and optimizing the composition, more effective posts are possible.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The data collection unit can collect user voice data and perform voice analysis. For example, it can analyze the content and tone of what the user says to understand their emotions and intentions. The data collection unit can also enable the generation AI to collect image data and perform image analysis. For example, it can analyze photos and illustrations posted by users to extract visual information. Furthermore, the data collection unit can integrate multimodal information, allowing the generation AI to simultaneously analyze voice data and image data. For example, it can analyze the user's facial expressions and voice tone to infer emotions. This allows for more multifaceted insights to be obtained by analyzing multimodal information including voice and image data.

[0083] The data collection unit can also incorporate data from different industries or fields, promoting crossover innovation. For example, the generative AI collects data from different industries and incorporates it into analysis. For example, data from the technology and entertainment fields can be integrated and analyzed. The data collection unit can also build a system that crosses data from different fields to gain new insights. For example, data from the medical and fashion fields can be combined. Furthermore, the data collection unit allows the generative AI to analyze data from different industries to find similarities and differences. For example, data from the finance and education industries can be compared and analyzed. This allows new insights to be gained by incorporating data from different industries and fields.

[0084] The data collection unit can also incorporate data from different regions or cultural spheres to perform analysis from a global perspective. For example, the generation AI collects data from different regions and analyzes it from a global perspective. For example, data from Asia, Europe, and America can be integrated and analyzed. The data collection unit also collects data from different cultural spheres and performs analysis that takes cultural differences into account. For example, it can compare and analyze social media post data from Japan and the United States. Furthermore, in order to perform analysis from a global perspective, the data collection unit allows the generation AI to collect data in multiple languages. For example, it can analyze data in English, French, Chinese, etc. This makes it possible to incorporate data from different regions and cultural spheres and perform analysis from a global perspective.

[0085] The user demographic identification unit can use the emotion estimation function to narrow down the target demographic based on the user's emotional response. For example, the emotion estimation function is used to analyze the user's emotional response and identify a user demographic that shows positive emotions as a target. For example, users with high emotional scores for joy and excitement are targeted. The user demographic identification unit also builds a system that narrows down the target user demographic based on the user's emotional response data. For example, it identifies age groups and genders that show a high number of positive emotional responses. Furthermore, the user demographic identification unit uses the emotion estimation function to analyze the user's emotional response in real time and dynamically narrow down the target user demographic. For example, it identifies a user demographic whose emotional response changes during a campaign period. This enables more effective targeting by narrowing down the target demographic based on the user's emotional response.

[0086] The user demographic identification unit can analyze a user's purchasing history or behavioral patterns to perform more accurate targeting. For example, it can analyze a user's purchasing history to identify a user demographic that is interested in a specific product or service as a target. For example, it can perform targeting based on the category of products purchased in the past. The user demographic identification unit can also analyze a user's behavioral patterns to identify a user demographic that exhibits specific behavior as a target. For example, it can target users who frequently visit a website. Furthermore, the user demographic identification unit can integrate the purchasing history and behavioral patterns to build a system that performs more accurate targeting. For example, it can target users who exhibit specific behavior after purchasing a specific product. This makes it possible to perform more accurate targeting by analyzing a user's purchasing history and behavioral patterns.

[0087] The user demographic identification unit can analyze the follower demographic of an influencer on social media to identify an influential user demographic. For example, the user demographic identification unit can analyze the follower demographic of an influencer on social media to identify an influential user demographic. For example, the user demographic identification unit can analyze the interests of the followers of a specific influencer. The user demographic identification unit can also analyze demographic data of the influencer's follower demographic to identify a target user demographic. For example, the user demographic identification unit can perform targeting based on data such as age, gender, and region. The user demographic identification unit can also analyze the behavioral patterns of the influencer's follower demographic to identify an influential user demographic. For example, the user demographic identification unit can analyze reactions and engagement to specific posts. In this way, by analyzing the follower demographic of an influencer, it is possible to identify an influential user demographic.

[0088] The post generation unit can use the emotion estimation function to select expressions that evoke the most positive emotions in the user. For example, the emotion estimation function is used to select expressions that evoke the most positive emotions in the user. For example, expressions with high emotion scores for joy and excitement are preferentially used. The post generation unit also builds a system that selects expressions that elicit positive emotions based on the user's emotional response data. For example, it analyzes past post data and extracts expressions that have received many positive responses. Furthermore, the post generation unit uses the emotion estimation function to monitor the user's emotional response to the generated post content in real time and dynamically selects expressions that elicit positive emotions. For example, it adjusts the expressions based on the user's real-time feedback. In this way, the effectiveness of the post content can be maximized by selecting expressions that evoke the most positive emotions in the user.

[0089] The post generation unit can generate posts that include visual content. For example, the generation AI generates post content that includes visual content. For example, it automatically generates social media posts that include images and videos. In addition, the post generation unit integrates image generation technology and video editing technology to generate visual content. For example, it generates images and videos based on user instructions. Furthermore, the post generation unit builds a system that evaluates the quality of the visual content when the generation AI generates post content that includes visual content. For example, it evaluates the image resolution and video frame rate. This enables posts that include visual content to be generated and are visually appealing.

[0090] The composition unit can use the emotion estimation function to select a composition that the user will most emotionally empathize with. For example, the emotion estimation function is used to select a composition that the user will most emotionally empathize with. For example, headlines and lead sentences with high emotion scores are used preferentially. The composition unit also builds a system that selects a composition that is likely to be emotionally empathized with based on user emotional response data. For example, components that have a high number of positive emotional responses are extracted. Furthermore, the composition unit uses the emotion estimation function to monitor users' emotional responses to the composition of the generated post content in real time, and dynamically selects a composition that is likely to be emotionally empathized with. For example, the composition is adjusted based on real-time user feedback. In this way, the effectiveness of the post content can be maximized by selecting a composition that the user will most emotionally empathize with.

[0091] The composition unit can create multimedia compositions that combine different media formats. For example, the generation AI can compose post content that combines different media formats. For example, it can generate press releases and social media posts that combine text, images, and videos. In addition, the composition unit allows the generation AI to consider the characteristics of each media format in order to create multimedia composition. For example, it can link text headlines and image captions. Furthermore, when the generation AI composes post content that combines different media formats, the composition unit builds a system that evaluates the quality of each media format. For example, it evaluates image resolution and video frame rate. This allows for visually appealing posts by creating multimedia compositions that combine different media formats.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The data collection department collects data from the internet, such as social media posts, news articles, blog entries, etc. They can also collect data on past successful press releases and social media posts and analyze their commonalities and characteristics. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, it analyzes the data using techniques such as text mining, sentiment analysis, and topic modeling. It can also calculate sentiment scores for the collected data and prioritize analysis of data with high positive scores. Step 3: The user demographic identification unit identifies the target user demographic based on the data analyzed by the analysis unit. For example, it analyzes user demographics with specific age groups, genders, or interests, and generates content appropriate for those users. It can also analyze users' purchasing history and behavioral patterns to achieve more precise targeting. Step 4: The post generation unit generates post content appropriate for the target user group identified by the user group identification unit. For example, it generates a press release about a new product announcement or a social media post containing campaign information. It can also use the emotion estimation function to select expressions that evoke the most positive emotions in users. Step 5: The composition unit composes the content of the post generated by the post generation unit. For example, in the case of a press release, it composes it with elements such as a headline, lead sentence, body text, and quotes. In the case of a social media post, it composes it with a short catchy copy and hashtags. It can also use an emotion estimation function to select the composition that most resonates with the user emotionally.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

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

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 7, a 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data on the Internet; an analysis unit that analyzes the data collected by the data collection unit; a user demographic identification unit that identifies a target user demographic based on the data analyzed by the analysis unit; a post generation unit that generates post content suitable for the target user group identified by the user group identification unit; a configuration unit that configures the post content generated by the post generation unit. A system characterized by:

2. The data collection unit Analyze users' emotional reactions and prioritize the use of data that shows positive reactions.

2. The system of claim 1.

3. The data collection unit Incorporating data from different regions or cultural spheres to conduct analysis from a global perspective 2. The system of claim 1.

4. The user group identification unit Narrow your target audience based on users' emotional responses 2. The system of claim 1.

5. The post generation unit: Select the expression that gives the user the most positive feelings 2. The system of claim 1.

6. The component comprises: Select the structure that the user most emotionally resonates with.

2. The system of claim 1.

7. The post generation unit: Monitoring users' emotional reactions to the generated posts in real time and optimizing the posts.

2. The system of claim 1.

8. The component comprises: Monitoring users' emotional responses to the structured posts and optimizing the structure.

2. The system of claim 1.

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