system

Abstract

The system according to the embodiment aims to automate effective content creation and posting and increase the number of subscribers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a posting unit, and a promotion unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates content based on the analysis results obtained by the analysis unit. The posting unit posts the content generated by the generation unit. The promotion unit advertises and sells based on the number of subscribers increased by the posting unit.

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 not done enough to automate effective content creation and posting and increase subscriber numbers, and there is room for improvement.

[0005] The system according to the embodiment aims to automate effective content creation and posting and increase the number of subscribers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, a posting unit, and a promotion unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The generation unit generates content based on the analysis results obtained by the analysis unit. The posting unit posts the content generated by the generation unit. The promotion unit advertises and sells based on the number of registered users increased by the posting unit. [Effects of the Invention]

[0007] The system according to the embodiment automates effective content creation and posting, and can increase the number of subscribers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention automatically generates content and posts likely to create buzz, thereby increasing the number of account subscribers. In this system, a generation AI analyzes past posting data and trend information, generates content likely to create buzz, and automatically posts the content, thereby increasing the number of account subscribers. After a certain number of subscribers have been reached, the account is used for advertising and sales. For example, the generation AI analyzes past posting data and trend information to identify content likely to create buzz. Next, the generation AI generates images, videos, catchphrases, etc. that match the trends based on the analysis results. The generated content is automatically posted at specific times to maximize reach. After a certain number of account subscribers have been reached, the account is used to promote specific products or services. This allows the system to automatically generate and post content likely to create buzz, increase the number of account subscribers, and utilize the increased number of subscribers for effective advertising and sales. This allows the system to automatically generate and post content likely to create buzz, increase the number of account subscribers, and utilize the increased number of subscribers for effective advertising and sales.

[0029] A content generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a posting unit, and a promotion unit. The collection unit collects data. The data includes, but is not limited to, the number of likes, shares, comments, the time of posting, and user attribute information. For example, the collection unit obtains the number of likes and shares from a social media platform. The collection unit can also collect user attribute information. For example, the collection unit collects information such as the user's age, gender, region, and interests. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit identifies characteristics of content that is likely to create a buzz based on the collected data. The analysis unit can also analyze trending keywords, visual elements, emotional elements, and the like. The generation unit generates content based on the analysis results obtained by the analysis unit. The generated content includes, for example, blog posts, videos, and images, but is not limited to, for example. For example, the generation unit uses a generation AI to generate images, videos, and catchphrases that match trends. The generation unit can also generate original content by referring to past success stories. The posting unit posts the content generated by the generation unit. Examples of posting include, but are not limited to, posting to social media sites and blogs. For example, the posting unit maximizes reach by posting at specific times of the day. The posting unit can also optimize the frequency and timing of posting. The promotion unit performs promotion and sales based on the number of subscribers increased by the posting unit. Examples of promotion and sales include, but are not limited to, online advertising, email marketing, and direct sales. For example, the promotion unit can utilize the increased number of subscribers to promote specific products or services. The promotion unit can also perform targeted advertising to reach specific user demographics. As a result, the content generation system according to the embodiment can automatically generate and post content that is likely to create a buzz, increase the number of account subscribers, and utilize the increased number of subscribers to effectively promote and sell products.

[0030] The collection unit can collect user attribute information, such as the number of likes, shares, and comments, the time of posting, and the likes and shares from a social media platform. For example, the collection unit can acquire data from the social media platform using an API. The collection unit can also collect user attribute information. For example, the collection unit can collect information such as the user's age, gender, region, and interests. For example, the collection unit can acquire attribute information from a survey or user profile. The collection unit can also collect the time of posting. For example, the collection unit can analyze the timestamp of the post to identify the time of posting. This allows the collection unit to collect a variety of data, enabling more accurate analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from the social media platform into a generation AI and have the generation AI collect the data.

[0031] The analysis unit can analyze the collected data and identify characteristics of content that is likely to go viral. For example, the analysis unit can identify characteristics of content that is likely to go viral based on the collected data. For example, the analysis unit can analyze the data using statistical analysis to identify patterns of content that is likely to go viral. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit can input the collected data into a machine learning model to extract characteristics of content that is likely to go viral. The analysis unit can also analyze trending keywords, visual elements, emotional elements, etc. For example, the analysis unit can extract trending keywords using natural language processing technology. The analysis unit can also analyze visual elements using image analysis technology. By identifying characteristics of content that is likely to go viral, the generated content can be more likely to go viral. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify characteristics of content that is likely to go viral.

[0032] The generation unit can generate images, videos, and catchphrases that match trends. The generation unit generates images, videos, and catchphrases that match trends using, for example, a generation AI. For example, the generation unit inputs trend keywords into the generation AI to generate images that match trends. The generation unit can also input trend information into the generation AI to generate videos that match trends. The generation unit can also input past success stories into the generation AI to generate unique catchphrases. For example, the generation unit inputs a prompt to the generation AI such as, "Please generate a catchphrase that matches this trend," and generates a catchphrase. This allows the generation unit to generate content that matches trends, thereby reaching more users. Some or all of the above-described processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input trend information into the generation AI and cause the generation AI to generate images and videos that match trends.

[0033] The posting unit can maximize reach by posting at specific time periods. The posting unit maximizes reach, for example, by posting at specific time periods. For example, the posting unit posts at time periods when users are most active. The posting unit can also post at peak times for the target market. For example, the posting unit posts at the optimal posting time periods identified by the analysis unit. Furthermore, the posting unit can optimize the frequency and timing of posting. For example, the posting unit analyzes how many times a week to post and the optimal posting time periods, and adjusts the frequency and timing of posting. This allows the posting unit to reach more users by posting at the optimal time periods. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the optimal posting time periods identified by the analysis unit into the generation AI and cause the generation AI to optimize the timing of posting.

[0034] The promotion department can promote specific products and services by utilizing the increased number of subscribers. For example, the promotion department can promote specific products and services by utilizing the increased number of subscribers. For example, the promotion department can promote specific products through online advertising. The promotion department can also promote specific services through email marketing. Furthermore, the promotion department can sell specific products and services through direct sales. For example, the promotion department can promote specific products and services by targeting the increased number of subscribers. This allows the promotion department to utilize the increased number of subscribers for effective promotion. Some or all of the above-mentioned processing in the promotion department may be performed using AI, for example, or may be performed without using AI. For example, the promotion department can input the increased number of subscribers into the generation AI and have the generation AI optimize the promotion.

[0035] The advertising unit can perform targeted advertising to reach a specific user demographic. For example, the advertising unit performs targeted advertising to reach a specific user demographic. For example, the advertising unit performs targeted advertising based on user behavior data. The advertising unit can also perform targeted advertising based on demographic data. For example, the advertising unit performs targeted advertising based on data such as the user's age, gender, region, and interests. This allows targeted advertising to effectively reach a specific user demographic. Some or all of the above-described processing in the advertising unit may be performed using, or without, AI. For example, the advertising unit can input user behavior data into a generation AI and have the generation AI optimize the targeted advertising.

[0036] The generation unit can generate unique content while referring to past success stories. The generation unit generates unique content while referring to past success stories, for example. For example, the generation unit adjusts the generation algorithm by referring to content that has received high ratings in the past. The generation unit can also avoid content that has received low ratings in the past. Furthermore, the generation unit can propose a new content generation method based on past success stories. For example, the generation unit generates unique content by referring to the results of past campaigns and user feedback. In this way, the quality of the generated content is improved by referring to past success stories. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input past success stories into the generation AI and cause the generation AI to generate unique content.

[0037] The posting unit can optimize the frequency and timing of posts. The posting unit, for example, optimizes the frequency and timing of posts. For example, the posting unit analyzes how many times a week to post and the optimal posting time period, and adjusts the frequency and timing of posts. The posting unit can also post during times when users are most active. Furthermore, the posting unit can post during peak hours for the target market. For example, the posting unit posts during the optimal posting time period identified by the analysis unit. This allows the frequency and timing of posts to be optimized, thereby reaching more users. Some or all of the above-described processing in the posting unit may be performed using, or without, AI, for example. For example, the posting unit can input the optimal posting time period identified by the analysis unit into the generation AI, and cause the generation AI to optimize the timing of posts.

[0038] The collection unit can dynamically change the type of data to be collected based on the user's past behavioral history. For example, the collection unit dynamically changes the type of data to be collected based on the user's past behavioral history. For example, the collection unit prioritizes collecting data on posts that the user has received many likes in the past. The collection unit can also prioritize collecting data on posts that the user has received many shares in the past. Furthermore, the collection unit can prioritize collecting data on posts that the user has received many comments in the past. For example, the collection unit analyzes the user's past behavioral history and dynamically changes the type of data to be collected. This allows for more relevant data to be collected by dynamically changing the data collection based on the user's past behavioral history. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past behavioral history into a generation AI and cause the generation AI to change the type of data collection.

[0039] The collection unit can filter data based on the user's current interests and trends when collecting data. For example, the collection unit can filter data based on the user's current interests and trends when collecting data. For example, the collection unit can prioritize collecting data related to topics in which the user is currently interested. The collection unit can also prioritize collecting data of posts that include hashtags related to current trends. Furthermore, the collection unit can prioritize collecting post data of influencers followed by the user. For example, the collection unit filters data based on real-time search data and social media trend information. By filtering the data based on the user's current interests and trends, more relevant data can be collected. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's current interests and trend information into the generation AI and have the generation AI perform data filtering.

[0040] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Furthermore, if the user posts an image, the collection unit can prioritize collecting image data. For example, the collection unit analyzes the user's input method and selects the optimal collection means. This enables more efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method to a generation AI and cause the generation AI to select the optimal collection means.

[0041] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting trend data related to the area where the user is currently located. The collection unit can also prioritize collecting data related to places the user has visited in the past. Furthermore, the collection unit can prioritize collecting data related to places the user plans to visit in the future. For example, the collection unit analyzes the user's geographical location information and prioritizes collecting highly relevant data. This makes it possible to collect more relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority of data collection.

[0042] The collection unit may analyze the user's social media activities and collect related data when collecting data. For example, the collection unit may analyze the user's social media activities and collect related data when collecting data. For example, the collection unit may collect data related to locations where the user has checked in on social media. The collection unit may also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit may collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may analyze the user's social media activities and collect related data. This allows for the collection of more relevant data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media activity data into a generation AI and cause the generation AI to optimize data collection.

[0043] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit preferentially uses data collection methods that the user has previously rated highly. The collection unit can also avoid data collection methods that the user has previously rated poorly. Furthermore, the collection unit can suggest a new data collection method based on the user's past feedback. For example, the collection unit analyzes the user's past feedback and customizes the collection method. This enables more appropriate data collection by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit evaluates the importance of the data and adjusts the level of detail of the analysis. This enables more efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. For example, the analysis unit classifies the category of data and applies an appropriate analysis algorithm. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0046] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to analysis results that the user has previously given a high rating. The analysis unit can also avoid analysis results that the user has previously given a low rating. Furthermore, the analysis unit can propose a new analysis method based on the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit evaluates the time when the data was collected and determines the analysis priority. This enables more efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically change the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and adjusts the order of analysis. This enables more efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0049] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit uses a lot of technical terms for users with high levels of expertise. The analysis unit can also avoid technical terms for users with low levels of expertise. Furthermore, the analysis unit can use appropriate technical terms according to the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and adjust the use of technical terms in the analysis. By adjusting the technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms.

[0050] The generation unit can adjust the level of detail of the generation based on the importance of the trend when generating content. For example, the generation unit adjusts the level of detail of the generation based on the importance of the trend when generating content. For example, the generation unit generates detailed content for a trend with high importance. The generation unit can also generate concise content for a trend with low importance. Furthermore, the generation unit can generate content with an appropriate level of detail for a trend with medium importance. For example, the generation unit evaluates the importance of the trend and adjusts the level of detail of the generation. In this way, more appropriate content can be generated by adjusting the level of detail of the generation based on the importance of the trend. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the trend to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0051] The generation unit can apply different generation algorithms depending on the trend category when generating content. For example, the generation unit applies different generation algorithms depending on the trend category when generating content. For example, the generation unit applies a natural language generation algorithm to text content. The generation unit can also apply an image generation algorithm to image content. The generation unit can also apply a video generation algorithm to video content. For example, the generation unit classifies trend categories and applies an appropriate generation algorithm. This improves the quality of the generated content by applying an appropriate generation algorithm depending on the trend category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the trend category to the generation AI and cause the generation AI to apply the generation algorithm.

[0052] The generation unit can improve the accuracy of content generation by referring to the user's past success stories when generating content. For example, the generation unit can improve the accuracy of content generation by referring to the user's past success stories when generating content. For example, the generation unit can adjust the generation algorithm by referring to content that the user has previously rated highly. The generation unit can also avoid content that the user has previously rated poorly. Furthermore, the generation unit can propose a new content generation method based on the user's past success stories. For example, the generation unit can analyze the user's past success stories and improve the accuracy of generation. As a result, the quality of the generated content is improved by referring to the user's past success stories. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past success stories into the generation AI and cause the generation AI to improve the accuracy of generation.

[0053] The generation unit can determine the generation priority based on the time when a trend occurred when generating content. For example, the generation unit determines the generation priority based on the time when a trend occurred when generating content. For example, the generation unit prioritizes generating content based on the latest trend. The generation unit can also generate content based on the latest trend while referring to past trends. Furthermore, the generation unit can generate content based on trends that occurred during a specific period. For example, the generation unit evaluates the time when a trend occurred and determines the generation priority. In this way, more appropriate content can be generated by determining the generation priority based on the time when a trend occurred. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the time when a trend occurred into the generation AI and have the generation AI determine the generation priority.

[0054] The generation unit can adjust the order of generation based on trend relevance when generating content. The generation unit, for example, adjusts the order of generation based on trend relevance when generating content. For example, the generation unit prioritizes generating content based on highly relevant trends. The generation unit can also postpone generating content based on less relevant trends. Furthermore, the generation unit can dynamically change the order of generation based on trend relevance. For example, the generation unit evaluates trend relevance and adjusts the order of generation. In this way, more appropriate content can be generated by adjusting the order of generation based on trend relevance. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input trend relevance to the generation AI and cause the generation AI to adjust the order of generation.

[0055] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating content. For example, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise when generating content. For example, the generation unit uses a lot of technical terminology for users with high levels of expertise. The generation unit can also avoid technical terminology for users with low levels of expertise. Furthermore, the generation unit can use appropriate technical terminology according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise and adjusts the use of technical terminology in the generation. In this way, more appropriate content can be generated by adjusting the technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0056] The posting unit can adjust the level of detail of the post based on the importance of the content when posting. For example, the posting unit adjusts the level of detail of the post based on the importance of the content when posting. For example, the posting unit makes a detailed post for content with high importance. The posting unit can also make a concise post for content with low importance. Furthermore, the posting unit can make a post with an appropriate level of detail for content with medium importance. For example, the posting unit evaluates the importance of the content and adjusts the level of detail of the post. In this way, by adjusting the level of detail of the post based on the importance of the content, more appropriate posts can be made. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the importance of the content to a generation AI and cause the generation AI to adjust the level of detail of the post.

[0057] The posting unit can apply different posting algorithms depending on the content category when posting. For example, the posting unit applies different posting algorithms depending on the content category when posting. For example, the posting unit applies a text posting algorithm to text content. The posting unit can also apply an image posting algorithm to image content. The posting unit can also apply a video posting algorithm to video content. For example, the posting unit classifies the content category and applies an appropriate posting algorithm. This improves the quality of posts by applying an appropriate posting algorithm depending on the content category. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the content category to a generation AI and cause the generation AI to apply a posting algorithm.

[0058] The posting unit can improve the accuracy of posts by referring to the user's past posting results when posting. For example, the posting unit improves the accuracy of posts by referring to the user's past posting results when posting. For example, the posting unit adjusts the posting algorithm by referring to posts that the user has previously received high ratings. The posting unit can also avoid posts that the user has previously received low ratings. Furthermore, the posting unit can suggest new posting methods based on the user's past posting results. For example, the posting unit analyzes the user's past posting results and improves the accuracy of posts. In this way, the quality of posts is improved by referring to the user's past posting results. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the user's past posting results into the generation AI and cause the generation AI to improve the accuracy of posts.

[0059] The posting unit can determine the priority of posts based on the time of content creation at the time of posting. For example, the posting unit determines the priority of posts based on the time of content creation at the time of posting. For example, the posting unit prioritizes posting the latest content. The posting unit can also post the latest content while referring to past content. Furthermore, the posting unit can prioritize posting content created within a specific period. For example, the posting unit evaluates the time of content creation and determines the priority of posts. This enables more appropriate posts to be made by determining the priority of posts based on the time of content creation. Some or all of the above-described processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the time of content creation to a generation AI and cause the generation AI to determine the priority of posts.

[0060] The posting unit can adjust the order of posts based on the relevance of the content when posting. For example, the posting unit adjusts the order of posts based on the relevance of the content when posting. For example, the posting unit prioritizes posting of highly relevant content. The posting unit can also postpone posting of less relevant content. Furthermore, the posting unit can dynamically change the order of posts based on the relevance of the content. For example, the posting unit evaluates the relevance of the content and adjusts the order of posts. In this way, more appropriate posts can be made by adjusting the order of posts based on the relevance of the content. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the relevance of the content to a generation AI and cause the generation AI to adjust the order of posts.

[0061] The posting unit can adjust the use of technical terminology in a post according to the user's level of expertise when posting. For example, the posting unit can adjust the use of technical terminology in a post according to the user's level of expertise when posting. For example, the posting unit can use a lot of technical terminology for users with high levels of expertise. The posting unit can also avoid technical terminology for users with low levels of expertise. Furthermore, the posting unit can use appropriate technical terminology according to the user's level of expertise. For example, the posting unit can evaluate the user's level of expertise and adjust the use of technical terminology in a post. This allows for more appropriate posts to be made by adjusting the technical terminology in a post according to the user's level of expertise. Some or all of the above-described processing in the posting unit may be performed using AI or without AI. For example, the posting unit can input the user's level of expertise to a generation AI and cause the generation AI to use technical terminology.

[0062] The advertising unit can adjust the level of detail of the advertising based on the importance of the product when advertising. For example, the advertising unit adjusts the level of detail of the advertising based on the importance of the product when advertising. For example, the advertising unit provides detailed advertising for products with high importance. The advertising unit can also provide brief advertising for products with low importance. Furthermore, the advertising unit can provide advertising with an appropriate level of detail for products with medium importance. For example, the advertising unit evaluates the importance of the product and adjusts the level of detail of the advertising. In this way, more effective advertising can be achieved by adjusting the level of detail of the advertising based on the importance of the product. Some or all of the above-described processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the importance of the product to the generation AI and have the generation AI adjust the level of detail of the advertising.

[0063] The advertising unit can apply different advertising algorithms depending on the product category during advertising. For example, the advertising unit applies different advertising algorithms depending on the product category during advertising. For example, the advertising unit applies a text generation algorithm to text advertisements. The advertising unit can also apply an image generation algorithm to image advertisements. The advertising unit can also apply a video generation algorithm to video advertisements. For example, the advertising unit classifies product categories and applies an appropriate advertising algorithm. This improves the quality of advertising by applying an appropriate advertising algorithm depending on the product category. Some or all of the above-mentioned processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the product category into a generation AI and have the generation AI apply an advertising algorithm.

[0064] The advertising unit can improve the accuracy of advertising by referring to the user's past advertising results when advertising. For example, the advertising unit can improve the accuracy of advertising by referring to the user's past advertising results when advertising. For example, the advertising unit can adjust the advertising algorithm by referring to advertisements that the user has previously rated highly. The advertising unit can also avoid advertisements that the user has previously rated poorly. Furthermore, the advertising unit can suggest new advertising methods based on the user's past advertising results. For example, the advertising unit can analyze the user's past advertising results and improve the accuracy of advertising. As a result, the quality of advertising is improved by referring to the user's past advertising results. Some or all of the above-mentioned processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the user's past advertising results into the generation AI and cause the generation AI to improve the accuracy of advertising.

[0065] The advertising department can determine the priority of advertising based on the time of product creation during advertising. For example, the advertising department determines the priority of advertising based on the time of product creation during advertising. For example, the advertising department prioritizes advertising the latest products. The advertising department can also promote the latest products while referring to past products. Furthermore, the advertising department can prioritize advertising products that occurred during a specific period. For example, the advertising department evaluates the time of product creation and determines the priority of advertising. In this way, more effective advertising can be achieved by determining the priority of advertising based on the time of product creation. Some or all of the above-mentioned processing in the advertising department may be performed using AI, or may be performed without using AI. For example, the advertising department can input the time of product creation into the generation AI and have the generation AI determine the priority of advertising.

[0066] The advertising unit can adjust the order of advertising based on the relevance of the products when advertising. For example, the advertising unit adjusts the order of advertising based on the relevance of the products when advertising. For example, the advertising unit prioritizes advertising highly relevant products. The advertising unit can also postpone advertising less relevant products. Furthermore, the advertising unit can dynamically change the order of advertising based on the relevance of the products. For example, the advertising unit evaluates the relevance of the products and adjusts the order of advertising. In this way, more effective advertising can be achieved by adjusting the order of advertising based on the relevance of the products. Some or all of the above-mentioned processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the relevance of the products into the generation AI and have the generation AI adjust the order of advertising.

[0067] The advertising unit can adjust the use of technical terms in advertising according to the user's level of expertise during advertising. For example, the advertising unit can adjust the use of technical terms in advertising according to the user's level of expertise during advertising. For example, the advertising unit can use a lot of technical terms for users with high levels of expertise. The advertising unit can also avoid technical terms for users with low levels of expertise. Furthermore, the advertising unit can use appropriate technical terms according to the user's level of expertise. For example, the advertising unit can evaluate the user's level of expertise and adjust the use of technical terms in advertising. This allows for more effective advertising by adjusting the technical terms in advertising according to the user's level of expertise. Some or all of the above-described processing in the advertising unit can be performed using AI or without AI. For example, the advertising unit can input the user's level of expertise into a generation AI and have the generation AI execute the use of technical terms.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The collection unit can also prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, trend data related to the area where the user is currently located can be collected preferentially. Data related to places the user has visited in the past can also be collected preferentially. Furthermore, data related to places the user plans to visit in the future can also be collected preferentially. In this way, more relevant data can be collected by taking into account the user's geographical location information.

[0070] The posting unit can also improve the accuracy of posts by referring to the user's past posting results. For example, it can adjust the posting algorithm by referring to posts that the user has previously received high ratings. It can also avoid posts that the user has previously received low ratings. Furthermore, it can suggest new posting methods based on the user's past posting results. In this way, the quality of posts can be improved by referring to the user's past posting results.

[0071] When collecting data, the collection unit can also analyze the user's social media activities and collect related data. For example, data related to the places where the user checked in on social media can be collected. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect more relevant data by analyzing the user's social media activities.

[0072] During analysis, the analysis unit can also determine the priority of analysis based on the time when the data was collected. For example, the most recent data can be analyzed first. The most recent data can also be analyzed while referring to past data. Furthermore, data collected during a specific period can be analyzed first. This allows for more efficient analysis by determining the priority of analysis based on the time when the data was collected.

[0073] When generating content, the generation unit can also determine generation priorities based on the time when a trend occurred. For example, content can be generated with priority based on the latest trend. Content can also be generated based on the latest trend while referring to past trends. Furthermore, content can also be generated based on trends that occurred during a specific period. In this way, by determining generation priorities based on the time when a trend occurred, more appropriate content can be generated.

[0074] The advertising department can apply different advertising algorithms depending on the product category during advertising. For example, a text generation algorithm can be applied to a text advertisement. An image generation algorithm can be applied to an image advertisement. Furthermore, a video generation algorithm can be applied to a video advertisement. In this way, the quality of advertising can be improved by applying an appropriate advertising algorithm depending on the product category.

[0075] The processing flow of the first embodiment will be briefly explained below.

[0076] Step 1: The collection unit collects data. The data includes, for example, the number of likes, shares, comments, the time of posting, and user attribute information. The collection unit obtains the number of likes and shares from the SNS platform, and also collects attribute information such as the user's age, gender, region, and interests. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using statistical analysis and machine learning algorithms to identify characteristics of content that are likely to go viral, trending keywords, visual elements, emotional elements, etc. Step 3: The generation unit generates content based on the analysis results obtained by the analysis unit. The generated content includes blog articles, videos, images, etc., and uses generative AI to generate images, videos, and catchphrases that match trends. It is also possible to generate original content by referring to past success stories. Step 4: The publishing department posts the content generated by the generation department. Posting includes posting to social media and blogs, and posts at specific times to maximize reach and optimize posting frequency and timing. Step 5: The Promotion Department promotes and sells based on the number of subscribers increased by the Posting Department. Promotion and sales can include online advertising, email marketing, and direct sales, and can use the increased number of subscribers to promote specific products or services and reach specific user demographics through targeted advertising.

[0077] (Example 2) A system according to an embodiment of the present invention automatically generates content and posts likely to create buzz, thereby increasing the number of account subscribers. In this system, a generation AI analyzes past posting data and trend information, generates content likely to create buzz, and automatically posts the content, thereby increasing the number of account subscribers. After a certain number of subscribers have been reached, the account is used for advertising and sales. For example, the generation AI analyzes past posting data and trend information to identify content likely to create buzz. Next, the generation AI generates images, videos, catchphrases, etc. that match the trends based on the analysis results. The generated content is automatically posted at specific times to maximize reach. After a certain number of account subscribers have been reached, the account is used to promote specific products or services. This allows the system to automatically generate and post content likely to create buzz, increase the number of account subscribers, and utilize the increased number of subscribers for effective advertising and sales. This allows the system to automatically generate and post content likely to create buzz, increase the number of account subscribers, and utilize the increased number of subscribers for effective advertising and sales.

[0078] A content generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, a posting unit, and a promotion unit. The collection unit collects data. The data includes, but is not limited to, the number of likes, shares, comments, the time of posting, and user attribute information. For example, the collection unit obtains the number of likes and shares from a social media platform. The collection unit can also collect user attribute information. For example, the collection unit collects information such as the user's age, gender, region, and interests. The analysis unit analyzes the data collected by the collection unit. The analysis can be performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, for example. For example, the analysis unit identifies characteristics of content that is likely to create a buzz based on the collected data. The analysis unit can also analyze trending keywords, visual elements, emotional elements, and the like. The generation unit generates content based on the analysis results obtained by the analysis unit. The generated content includes, for example, blog posts, videos, and images, but is not limited to, for example. For example, the generation unit uses a generation AI to generate images, videos, and catchphrases that match trends. The generation unit can also generate original content by referring to past success stories. The posting unit posts the content generated by the generation unit. Examples of posting include, but are not limited to, posting to social media sites and blogs. For example, the posting unit maximizes reach by posting at specific times of the day. The posting unit can also optimize the frequency and timing of posting. The promotion unit performs promotion and sales based on the number of subscribers increased by the posting unit. Examples of promotion and sales include, but are not limited to, online advertising, email marketing, and direct sales. For example, the promotion unit can utilize the increased number of subscribers to promote specific products or services. The promotion unit can also perform targeted advertising to reach specific user demographics. As a result, the content generation system according to the embodiment can automatically generate and post content that is likely to create a buzz, increase the number of account subscribers, and utilize the increased number of subscribers to effectively promote and sell products.

[0079] The collection unit can collect user attribute information, such as the number of likes, shares, and comments, the time of posting, and the likes and shares from a social media platform. For example, the collection unit can acquire data from the social media platform using an API. The collection unit can also collect user attribute information. For example, the collection unit can collect information such as the user's age, gender, region, and interests. For example, the collection unit can acquire attribute information from a survey or user profile. The collection unit can also collect the time of posting. For example, the collection unit can analyze the timestamp of the post to identify the time of posting. This allows the collection unit to collect a variety of data, enabling more accurate analysis. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input data acquired from the social media platform into a generation AI and have the generation AI collect the data.

[0080] The analysis unit can analyze the collected data and identify characteristics of content that is likely to go viral. For example, the analysis unit can identify characteristics of content that is likely to go viral based on the collected data. For example, the analysis unit can analyze the data using statistical analysis to identify patterns of content that is likely to go viral. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit can input the collected data into a machine learning model to extract characteristics of content that is likely to go viral. The analysis unit can also analyze trending keywords, visual elements, emotional elements, etc. For example, the analysis unit can extract trending keywords using natural language processing technology. The analysis unit can also analyze visual elements using image analysis technology. By identifying characteristics of content that is likely to go viral, the generated content can be more likely to go viral. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI identify characteristics of content that is likely to go viral.

[0081] The generation unit can generate images, videos, and catchphrases that match trends. The generation unit generates images, videos, and catchphrases that match trends using, for example, a generation AI. For example, the generation unit inputs trend keywords into the generation AI to generate images that match trends. The generation unit can also input trend information into the generation AI to generate videos that match trends. The generation unit can also input past success stories into the generation AI to generate unique catchphrases. For example, the generation unit inputs a prompt to the generation AI such as, "Please generate a catchphrase that matches this trend," and generates a catchphrase. This allows the generation unit to generate content that matches trends, thereby reaching more users. Some or all of the above-described processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input trend information into the generation AI and cause the generation AI to generate images and videos that match trends.

[0082] The posting unit can maximize reach by posting at specific time periods. The posting unit maximizes reach, for example, by posting at specific time periods. For example, the posting unit posts at time periods when users are most active. The posting unit can also post at peak times for the target market. For example, the posting unit posts at the optimal posting time periods identified by the analysis unit. Furthermore, the posting unit can optimize the frequency and timing of posting. For example, the posting unit analyzes how many times a week to post and the optimal posting time periods, and adjusts the frequency and timing of posting. This allows the posting unit to reach more users by posting at the optimal time periods. Some or all of the above-described processing in the posting unit may be performed using, for example, AI, or may be performed without using AI. For example, the posting unit can input the optimal posting time periods identified by the analysis unit into the generation AI and cause the generation AI to optimize the timing of posting.

[0083] The promotion department can promote specific products and services by utilizing the increased number of subscribers. For example, the promotion department can promote specific products and services by utilizing the increased number of subscribers. For example, the promotion department can promote specific products through online advertising. The promotion department can also promote specific services through email marketing. Furthermore, the promotion department can sell specific products and services through direct sales. For example, the promotion department can promote specific products and services by targeting the increased number of subscribers. This allows the promotion department to utilize the increased number of subscribers for effective promotion. Some or all of the above-mentioned processing in the promotion department may be performed using AI, for example, or may be performed without using AI. For example, the promotion department can input the increased number of subscribers into the generation AI and have the generation AI optimize the promotion.

[0084] The advertising unit can perform targeted advertising to reach a specific user demographic. For example, the advertising unit performs targeted advertising to reach a specific user demographic. For example, the advertising unit performs targeted advertising based on user behavior data. The advertising unit can also perform targeted advertising based on demographic data. For example, the advertising unit performs targeted advertising based on data such as the user's age, gender, region, and interests. This allows targeted advertising to effectively reach a specific user demographic. Some or all of the above-described processing in the advertising unit may be performed using, or without, AI. For example, the advertising unit can input user behavior data into a generation AI and have the generation AI optimize the targeted advertising.

[0085] The generation unit can generate unique content while referring to past success stories. The generation unit generates unique content while referring to past success stories, for example. For example, the generation unit adjusts the generation algorithm by referring to content that has received high ratings in the past. The generation unit can also avoid content that has received low ratings in the past. Furthermore, the generation unit can propose a new content generation method based on past success stories. For example, the generation unit generates unique content by referring to the results of past campaigns and user feedback. In this way, the quality of the generated content is improved by referring to past success stories. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI (e.g., a text generation AI or a multimodal generation AI), or may be performed without using a generation AI. For example, the generation unit can input past success stories into the generation AI and cause the generation AI to generate unique content.

[0086] The posting unit can optimize the frequency and timing of posts. The posting unit, for example, optimizes the frequency and timing of posts. For example, the posting unit analyzes how many times a week to post and the optimal posting time period, and adjusts the frequency and timing of posts. The posting unit can also post during times when users are most active. Furthermore, the posting unit can post during peak hours for the target market. For example, the posting unit posts during the optimal posting time period identified by the analysis unit. This allows the frequency and timing of posts to be optimized, thereby reaching more users. Some or all of the above-described processing in the posting unit may be performed using, or without, AI, for example. For example, the posting unit can input the optimal posting time period identified by the analysis unit into the generation AI, and cause the generation AI to optimize the timing of posts.

[0087] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, when the user is excited, the collection unit collects data in real time and reflects it immediately. Furthermore, when the user is relaxed, the collection unit can periodically collect data and reflect it at a stable timing. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. For example, the collection unit estimates the user's emotions using an emotion analysis algorithm and adjusts the timing of data collection based on the estimated emotions. This enables more appropriate data collection by adjusting the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0088] The collection unit can dynamically change the type of data to be collected based on the user's past behavioral history. For example, the collection unit dynamically changes the type of data to be collected based on the user's past behavioral history. For example, the collection unit prioritizes collecting data on posts that the user has received many likes in the past. The collection unit can also prioritize collecting data on posts that the user has received many shares in the past. Furthermore, the collection unit can prioritize collecting data on posts that the user has received many comments in the past. For example, the collection unit analyzes the user's past behavioral history and dynamically changes the type of data to be collected. This allows for more relevant data to be collected by dynamically changing the data collection based on the user's past behavioral history. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's past behavioral history into a generation AI and cause the generation AI to change the type of data collection.

[0089] The collection unit can filter data based on the user's current interests and trends when collecting data. For example, the collection unit can filter data based on the user's current interests and trends when collecting data. For example, the collection unit can prioritize collecting data related to topics in which the user is currently interested. The collection unit can also prioritize collecting data of posts that include hashtags related to current trends. Furthermore, the collection unit can prioritize collecting post data of influencers followed by the user. For example, the collection unit filters data based on real-time search data and social media trend information. By filtering the data based on the user's current interests and trends, more relevant data can be collected. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input the user's current interests and trend information into the generation AI and have the generation AI perform data filtering.

[0090] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Furthermore, if the user posts an image, the collection unit can prioritize collecting image data. For example, the collection unit analyzes the user's input method and selects the optimal collection means. This enables more efficient data collection by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input method to a generation AI and cause the generation AI to select the optimal collection means.

[0091] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, when the user is excited, the collection unit collects data in real time and reflects the data immediately. Furthermore, when the user is relaxed, the collection unit can periodically collect data and reflect the data at a stable timing. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. For example, the collection unit estimates the user's emotions using an emotion analysis algorithm and determines the priority of data to be collected based on the estimated emotions. This enables more appropriate data collection by determining the priority of data according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input user emotion data into the generation AI and have the generation AI determine the priority of the data.

[0092] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting trend data related to the area where the user is currently located. The collection unit can also prioritize collecting data related to places the user has visited in the past. Furthermore, the collection unit can prioritize collecting data related to places the user plans to visit in the future. For example, the collection unit analyzes the user's geographical location information and prioritizes collecting highly relevant data. This makes it possible to collect more relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's geographical location information to a generation AI and cause the generation AI to determine the priority of data collection.

[0093] The collection unit may analyze the user's social media activities and collect related data when collecting data. For example, the collection unit may analyze the user's social media activities and collect related data when collecting data. For example, the collection unit may collect data related to locations where the user has checked in on social media. The collection unit may also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit may collect related data by referring to the activities of the user's friends on social media. For example, the collection unit may analyze the user's social media activities and collect related data. This allows for the collection of more relevant data by analyzing the user's social media activities. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's social media activity data into a generation AI and cause the generation AI to optimize data collection.

[0094] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit preferentially uses data collection methods that the user has previously rated highly. The collection unit can also avoid data collection methods that the user has previously rated poorly. Furthermore, the collection unit can suggest a new data collection method based on the user's past feedback. For example, the collection unit analyzes the user's past feedback and customizes the collection method. This enables more appropriate data collection by reflecting the user's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0095] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide analysis results using visually stimulating graphs or charts when the user is excited. For example, the analysis unit can estimate the user's emotions using an emotion analysis algorithm and adjust the presentation method of the analysis based on the estimated emotions. This allows for more appropriate analysis results to be provided by adjusting the presentation method of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0096] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit performs a detailed analysis on data of high importance. The analysis unit can also perform a brief analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit evaluates the importance of the data and adjusts the level of detail of the analysis. This enables more efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0097] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. For example, the analysis unit classifies the category of data and applies an appropriate analysis algorithm. This improves the accuracy of analysis by applying an appropriate analysis algorithm depending on the category of data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the category of data to the generation AI and cause the generation AI to apply the analysis algorithm.

[0098] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to analysis results that the user has previously given a high rating. The analysis unit can also avoid analysis results that the user has previously given a low rating. Furthermore, the analysis unit can propose a new analysis method based on the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0099] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide the analysis result using visually stimulating graphs or charts. For example, the analysis unit estimates the user's emotions using an emotion analysis algorithm and adjusts the length of the analysis based on the estimated emotions. This allows for more appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the analysis.

[0100] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit, for example, determines the analysis priority based on the time when the data was collected during analysis. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also analyze the most recent data while referring to past data. Furthermore, the analysis unit can prioritize the analysis of data collected during a specific period. For example, the analysis unit evaluates the time when the data was collected and determines the analysis priority. This enables more efficient analysis by determining the analysis priority based on the time when the data was collected. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the time when the data was collected into the generation AI and have the generation AI determine the analysis priority.

[0101] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically change the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and adjusts the order of analysis. This enables more efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance of the data to the generation AI and cause the generation AI to adjust the order of analysis.

[0102] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit uses a lot of technical terms for users with high levels of expertise. The analysis unit can also avoid technical terms for users with low levels of expertise. Furthermore, the analysis unit can use appropriate technical terms according to the user's level of expertise. For example, the analysis unit can evaluate the user's level of expertise and adjust the use of technical terms in the analysis. By adjusting the technical terms in the analysis according to the user's level of expertise, more appropriate analysis results can be provided. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terms.

[0103] The generation unit can estimate the user's emotions and adjust the presentation method of the generated content based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the presentation method of the generated content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate content that progresses at a leisurely pace. Furthermore, if the user is in a hurry, the generation unit can generate content that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate content that adds visually stimulating effects. For example, the generation unit can estimate the user's emotions using an emotion analysis algorithm and adjust the presentation method of the content based on the estimated emotions. This allows for the generation of more appropriate content by adjusting the presentation method of the content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the way the content is expressed.

[0104] The generation unit can adjust the level of detail of the generation based on the importance of the trend when generating content. For example, the generation unit adjusts the level of detail of the generation based on the importance of the trend when generating content. For example, the generation unit generates detailed content for a trend with high importance. The generation unit can also generate concise content for a trend with low importance. Furthermore, the generation unit can generate content with an appropriate level of detail for a trend with medium importance. For example, the generation unit evaluates the importance of the trend and adjusts the level of detail of the generation. In this way, more appropriate content can be generated by adjusting the level of detail of the generation based on the importance of the trend. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the importance of the trend to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0105] The generation unit can apply different generation algorithms depending on the trend category when generating content. For example, the generation unit applies different generation algorithms depending on the trend category when generating content. For example, the generation unit applies a natural language generation algorithm to text content. The generation unit can also apply an image generation algorithm to image content. The generation unit can also apply a video generation algorithm to video content. For example, the generation unit classifies trend categories and applies an appropriate generation algorithm. This improves the quality of the generated content by applying an appropriate generation algorithm depending on the trend category. Some or all of the above-mentioned processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the trend category to the generation AI and cause the generation AI to apply the generation algorithm.

[0106] The generation unit can improve the accuracy of content generation by referring to the user's past success stories when generating content. For example, the generation unit can improve the accuracy of content generation by referring to the user's past success stories when generating content. For example, the generation unit can adjust the generation algorithm by referring to content that the user has previously rated highly. The generation unit can also avoid content that the user has previously rated poorly. Furthermore, the generation unit can propose a new content generation method based on the user's past success stories. For example, the generation unit can analyze the user's past success stories and improve the accuracy of generation. As a result, the quality of the generated content is improved by referring to the user's past success stories. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's past success stories into the generation AI and cause the generation AI to improve the accuracy of generation.

[0107] The generation unit can estimate the user's emotion and adjust the length of the generated content based on the estimated user emotion. For example, the generation unit can estimate the user's emotion and adjust the length of the generated content based on the estimated user emotion. For example, if the user is in a hurry, the generation unit can generate short, to-the-point content. Furthermore, if the user is relaxed, the generation unit can generate longer content with detailed explanations. Furthermore, if the user is excited, the generation unit can generate content with visually stimulating effects. For example, the generation unit can estimate the user's emotion using an emotion analysis algorithm and adjust the length of the content based on the estimated emotion. This allows for more appropriate content to be generated by adjusting the length of the content according to the user's emotion. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI adjust the length of the content.

[0108] The generation unit can determine the generation priority based on the time when a trend occurred when generating content. For example, the generation unit determines the generation priority based on the time when a trend occurred when generating content. For example, the generation unit prioritizes generating content based on the latest trend. The generation unit can also generate content based on the latest trend while referring to past trends. Furthermore, the generation unit can generate content based on trends that occurred during a specific period. For example, the generation unit evaluates the time when a trend occurred and determines the generation priority. In this way, more appropriate content can be generated by determining the generation priority based on the time when a trend occurred. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the time when a trend occurred into the generation AI and have the generation AI determine the generation priority.

[0109] The generation unit can adjust the order of generation based on trend relevance when generating content. The generation unit, for example, adjusts the order of generation based on trend relevance when generating content. For example, the generation unit prioritizes generating content based on highly relevant trends. The generation unit can also postpone generating content based on less relevant trends. Furthermore, the generation unit can dynamically change the order of generation based on trend relevance. For example, the generation unit evaluates trend relevance and adjusts the order of generation. In this way, more appropriate content can be generated by adjusting the order of generation based on trend relevance. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input trend relevance to the generation AI and cause the generation AI to adjust the order of generation.

[0110] The generation unit can adjust the use of technical terminology in the generation according to the user's level of expertise when generating content. For example, the generation unit adjusts the use of technical terminology in the generation according to the user's level of expertise when generating content. For example, the generation unit uses a lot of technical terminology for users with high levels of expertise. The generation unit can also avoid technical terminology for users with low levels of expertise. Furthermore, the generation unit can use appropriate technical terminology according to the user's level of expertise. For example, the generation unit evaluates the user's level of expertise and adjusts the use of technical terminology in the generation. In this way, more appropriate content can be generated by adjusting the technical terminology in the generation according to the user's level of expertise. Some or all of the above-described processing in the generation unit may be performed using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the user's level of expertise into the generation AI and cause the generation AI to use technical terminology.

[0111] The posting unit can estimate the user's emotions and adjust the timing of posting based on the estimated user emotions. The posting unit, for example, estimates the user's emotions and adjusts the timing of posting based on the estimated user emotions. For example, if the user is excited, the posting unit posts in real time. Furthermore, if the user is relaxed, the posting unit can post periodically. Furthermore, if the user is feeling stressed, the posting unit can reduce the frequency of posting to reduce the burden on the user. For example, the posting unit can estimate the user's emotions using an emotion analysis algorithm and adjust the timing of posting based on the estimated emotions. In this way, by adjusting the timing of posting according to the user's emotions, posting can be performed at more appropriate times. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the posting unit may be performed using AI or without AI. For example, the posting unit can input the user's emotional data into the generation AI and have the generation AI adjust the timing of posting.

[0112] The posting unit can adjust the level of detail of the post based on the importance of the content when posting. For example, the posting unit adjusts the level of detail of the post based on the importance of the content when posting. For example, the posting unit makes a detailed post for content with high importance. The posting unit can also make a concise post for content with low importance. Furthermore, the posting unit can make a post with an appropriate level of detail for content with medium importance. For example, the posting unit evaluates the importance of the content and adjusts the level of detail of the post. In this way, by adjusting the level of detail of the post based on the importance of the content, more appropriate posts can be made. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the importance of the content to a generation AI and cause the generation AI to adjust the level of detail of the post.

[0113] The posting unit can apply different posting algorithms depending on the content category when posting. For example, the posting unit applies different posting algorithms depending on the content category when posting. For example, the posting unit applies a text posting algorithm to text content. The posting unit can also apply an image posting algorithm to image content. The posting unit can also apply a video posting algorithm to video content. For example, the posting unit classifies the content category and applies an appropriate posting algorithm. This improves the quality of posts by applying an appropriate posting algorithm depending on the content category. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the content category to a generation AI and cause the generation AI to apply a posting algorithm.

[0114] The posting unit can improve the accuracy of posts by referring to the user's past posting results when posting. For example, the posting unit improves the accuracy of posts by referring to the user's past posting results when posting. For example, the posting unit adjusts the posting algorithm by referring to posts that the user has previously received high ratings. The posting unit can also avoid posts that the user has previously received low ratings. Furthermore, the posting unit can suggest new posting methods based on the user's past posting results. For example, the posting unit analyzes the user's past posting results and improves the accuracy of posts. In this way, the quality of posts is improved by referring to the user's past posting results. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the user's past posting results into the generation AI and cause the generation AI to improve the accuracy of posts.

[0115] The posting unit can estimate the user's emotions and adjust the length of the post based on the estimated user emotions. For example, the posting unit estimates the user's emotions and adjusts the length of the post based on the estimated user emotions. For example, if the user is in a hurry, the posting unit can post a short, to-the-point message. Furthermore, if the user is relaxed, the posting unit can post a longer message with detailed explanations. Furthermore, if the user is excited, the posting unit can post a message with visually stimulating effects. For example, the posting unit can estimate the user's emotions using an emotion analysis algorithm and adjust the length of the post based on the estimated emotions. This allows for more appropriate posts by adjusting the length of the post according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the posting unit may be performed using AI or without AI. For example, the posting unit can input the user's emotional data into the generation AI and have the generation AI adjust the length of the post.

[0116] The posting unit can determine the priority of posts based on the time of content creation at the time of posting. For example, the posting unit determines the priority of posts based on the time of content creation at the time of posting. For example, the posting unit prioritizes posting the latest content. The posting unit can also post the latest content while referring to past content. Furthermore, the posting unit can prioritize posting content created within a specific period. For example, the posting unit evaluates the time of content creation and determines the priority of posts. This enables more appropriate posts to be made by determining the priority of posts based on the time of content creation. Some or all of the above-described processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the time of content creation to a generation AI and cause the generation AI to determine the priority of posts.

[0117] The posting unit can adjust the order of posts based on the relevance of the content when posting. For example, the posting unit adjusts the order of posts based on the relevance of the content when posting. For example, the posting unit prioritizes posting of highly relevant content. The posting unit can also postpone posting of less relevant content. Furthermore, the posting unit can dynamically change the order of posts based on the relevance of the content. For example, the posting unit evaluates the relevance of the content and adjusts the order of posts. In this way, more appropriate posts can be made by adjusting the order of posts based on the relevance of the content. Some or all of the above-mentioned processing in the posting unit may be performed using AI, or may be performed without using AI. For example, the posting unit can input the relevance of the content to a generation AI and cause the generation AI to adjust the order of posts.

[0118] The posting unit can adjust the use of technical terminology in a post according to the user's level of expertise when posting. For example, the posting unit can adjust the use of technical terminology in a post according to the user's level of expertise when posting. For example, the posting unit can use a lot of technical terminology for users with high levels of expertise. The posting unit can also avoid technical terminology for users with low levels of expertise. Furthermore, the posting unit can use appropriate technical terminology according to the user's level of expertise. For example, the posting unit can evaluate the user's level of expertise and adjust the use of technical terminology in a post. This allows for more appropriate posts to be made by adjusting the technical terminology in a post according to the user's level of expertise. Some or all of the above-described processing in the posting unit may be performed using AI or without AI. For example, the posting unit can input the user's level of expertise to a generation AI and cause the generation AI to use technical terminology.

[0119] The advertising unit can estimate a user's emotions and adjust the advertising presentation method based on the estimated user emotions. For example, the advertising unit estimates a user's emotions and adjusts the advertising presentation method based on the estimated user emotions. For example, if the user is relaxed, the advertising unit can present advertising at a leisurely pace. If the user is in a hurry, the advertising unit can present concise advertising that focuses on the main points. Furthermore, if the user is excited, the advertising unit can present advertising that adds visually stimulating effects. For example, the advertising unit can estimate a user's emotions using an emotion analysis algorithm and adjust the advertising presentation method based on the estimated emotions. This allows for more effective advertising by adjusting the advertising presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advertising unit can be performed using AI or without AI. For example, the advertising department can input user emotional data into the generation AI and have the generation AI adjust the way the advertisement is expressed.

[0120] The advertising unit can adjust the level of detail of the advertising based on the importance of the product when advertising. For example, the advertising unit adjusts the level of detail of the advertising based on the importance of the product when advertising. For example, the advertising unit provides detailed advertising for products with high importance. The advertising unit can also provide brief advertising for products with low importance. Furthermore, the advertising unit can provide advertising with an appropriate level of detail for products with medium importance. For example, the advertising unit evaluates the importance of the product and adjusts the level of detail of the advertising. In this way, more effective advertising can be achieved by adjusting the level of detail of the advertising based on the importance of the product. Some or all of the above-described processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the importance of the product to the generation AI and have the generation AI adjust the level of detail of the advertising.

[0121] The advertising unit can apply different advertising algorithms depending on the product category during advertising. For example, the advertising unit applies different advertising algorithms depending on the product category during advertising. For example, the advertising unit applies a text generation algorithm to text advertisements. The advertising unit can also apply an image generation algorithm to image advertisements. The advertising unit can also apply a video generation algorithm to video advertisements. For example, the advertising unit classifies product categories and applies an appropriate advertising algorithm. This improves the quality of advertising by applying an appropriate advertising algorithm depending on the product category. Some or all of the above-mentioned processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the product category into a generation AI and have the generation AI apply an advertising algorithm.

[0122] The advertising unit can improve the accuracy of advertising by referring to the user's past advertising results when advertising. For example, the advertising unit can improve the accuracy of advertising by referring to the user's past advertising results when advertising. For example, the advertising unit can adjust the advertising algorithm by referring to advertisements that the user has previously rated highly. The advertising unit can also avoid advertisements that the user has previously rated poorly. Furthermore, the advertising unit can suggest new advertising methods based on the user's past advertising results. For example, the advertising unit can analyze the user's past advertising results and improve the accuracy of advertising. As a result, the quality of advertising is improved by referring to the user's past advertising results. Some or all of the above-mentioned processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the user's past advertising results into the generation AI and cause the generation AI to improve the accuracy of advertising.

[0123] The advertising unit can estimate a user's emotions and adjust the length of advertisements based on the estimated user emotions. For example, the advertising unit estimates a user's emotions and adjusts the length of advertisements based on the estimated user emotions. For example, if the user is in a hurry, the advertising unit can provide short, to-the-point advertisements. Furthermore, if the user is relaxed, the advertising unit can provide longer advertisements with detailed explanations. Furthermore, if the user is excited, the advertising unit can provide advertisements with visually stimulating effects. For example, the advertising unit can estimate a user's emotions using an emotion analysis algorithm and adjust the length of advertisements based on the estimated emotions. This allows for more effective advertisements by adjusting the length of advertisements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the advertising unit can be performed using AI or without AI. For example, the advertising department can input user emotion data into the generation AI and have the generation AI adjust the length of the advertisement.

[0124] The advertising department can determine the priority of advertising based on the time of product creation during advertising. For example, the advertising department determines the priority of advertising based on the time of product creation during advertising. For example, the advertising department prioritizes advertising the latest products. The advertising department can also promote the latest products while referring to past products. Furthermore, the advertising department can prioritize advertising products that occurred during a specific period. For example, the advertising department evaluates the time of product creation and determines the priority of advertising. In this way, more effective advertising can be achieved by determining the priority of advertising based on the time of product creation. Some or all of the above-mentioned processing in the advertising department may be performed using AI, or may be performed without using AI. For example, the advertising department can input the time of product creation into the generation AI and have the generation AI determine the priority of advertising.

[0125] The advertising unit can adjust the order of advertising based on the relevance of the products when advertising. For example, the advertising unit adjusts the order of advertising based on the relevance of the products when advertising. For example, the advertising unit prioritizes advertising highly relevant products. The advertising unit can also postpone advertising less relevant products. Furthermore, the advertising unit can dynamically change the order of advertising based on the relevance of the products. For example, the advertising unit evaluates the relevance of the products and adjusts the order of advertising. In this way, more effective advertising can be achieved by adjusting the order of advertising based on the relevance of the products. Some or all of the above-mentioned processing in the advertising unit may be performed using AI, or may be performed without using AI. For example, the advertising unit can input the relevance of the products into the generation AI and have the generation AI adjust the order of advertising.

[0126] The advertising unit can adjust the use of technical terms in advertising according to the user's level of expertise during advertising. For example, the advertising unit can adjust the use of technical terms in advertising according to the user's level of expertise during advertising. For example, the advertising unit can use a lot of technical terms for users with high levels of expertise. The advertising unit can also avoid technical terms for users with low levels of expertise. Furthermore, the advertising unit can use appropriate technical terms according to the user's level of expertise. For example, the advertising unit can evaluate the user's level of expertise and adjust the use of technical terms in advertising. This allows for more effective advertising by adjusting the technical terms in advertising according to the user's level of expertise. Some or all of the above-described processing in the advertising unit can be performed using AI or without AI. For example, the advertising unit can input the user's level of expertise into a generation AI and have the generation AI execute the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, posting unit, and promotion unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 38B of the smart device 14, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies characteristics of content that is likely to become a buzz based on the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates content based on the analysis results. The posting unit is realized by the control unit 46A of the smart device 14 and posts the generated content during a specific time period. The promotion unit is realized by the specific processing unit 290 of the data processing device 12 and promotes specific products and services by utilizing the increased number of registered users. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, posting unit, and promotion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies characteristics of content that is likely to become buzzworthy based on the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates content based on the analysis results. The posting unit is realized by the control unit 46A of the smart glasses 214 and posts the generated content during a specific time period. The promotion unit is realized by the specific processing unit 290 of the data processing device 12 and promotes specific products and services by utilizing the increased number of registered users. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, generation unit, posting unit, and promotion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the headset-type terminal 314, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies characteristics of content that is likely to go viral based on the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates content based on the analysis results. The posting unit is realized by the control unit 46A of the headset-type terminal 314 and posts the generated content during a specific time period. The promotion unit is realized by the specific processing unit 290 of the data processing device 12 and promotes specific products and services by utilizing the increased number of registered users. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, generation unit, posting unit, and promotion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414, and the collected data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and identifies characteristics of content that is likely to go viral based on the collected data. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates content based on the analysis results. The posting unit is realized by the control unit 46A of the robot 414 and posts the generated content during a specific time period. The promotion unit is realized by the specific processing unit 290 of the data processing device 12 and promotes specific products and services by utilizing the increased number of registered users.

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

[0128] The analysis unit can also estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is excited, the analysis can be performed in real time to provide the results immediately. Alternatively, if the user is relaxed, the analysis can be performed periodically to provide the results at a stable timing. Furthermore, if the user is feeling stressed, the frequency of analysis can be reduced to reduce the burden on the user. In this way, by adjusting the analysis priority according to the user's emotions, more appropriate analysis results can be provided.

[0129] The collection unit can also prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, trend data related to the area where the user is currently located can be collected preferentially. Data related to places the user has visited in the past can also be collected preferentially. Furthermore, data related to places the user plans to visit in the future can also be collected preferentially. In this way, more relevant data can be collected by taking into account the user's geographical location information.

[0130] The generation unit can also estimate the user's emotions and adjust the tone of the generated content based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate content with a calm tone. If the user is excited, the generation unit can generate content with an energetic tone. Furthermore, if the user is sad, the generation unit can generate content with a comforting tone. In this way, by adjusting the tone of the content according to the user's emotions, more appropriate content can be provided.

[0131] The posting unit can also improve the accuracy of posts by referring to the user's past posting results. For example, it can adjust the posting algorithm by referring to posts that the user has previously received high ratings. It can also avoid posts that the user has previously received low ratings. Furthermore, it can suggest new posting methods based on the user's past posting results. In this way, the quality of posts can be improved by referring to the user's past posting results.

[0132] The advertising unit can also estimate the user's emotions and adjust the timing of advertising based on the estimated user emotions. For example, if the user is excited, advertising can be performed in real time. If the user is relaxed, advertising can be performed periodically. Furthermore, if the user is feeling stressed, advertising frequency can be reduced to reduce the burden on the user. In this way, more effective advertising can be performed by adjusting the timing of advertising according to the user's emotions.

[0133] When collecting data, the collection unit can also analyze the user's social media activities and collect related data. For example, data related to the places where the user checked in on social media can be collected. The collection unit can also analyze the content of the user's social media posts and collect related data. Furthermore, the collection unit can also collect related data by referring to the activities of the user's friends on social media. This makes it possible to collect more relevant data by analyzing the user's social media activities.

[0134] During analysis, the analysis unit can also determine the priority of analysis based on the time when the data was collected. For example, the most recent data can be analyzed first. The most recent data can also be analyzed while referring to past data. Furthermore, data collected during a specific period can be analyzed first. This allows for more efficient analysis by determining the priority of analysis based on the time when the data was collected.

[0135] When generating content, the generation unit can also determine generation priorities based on the time when a trend occurred. For example, content can be generated with priority based on the latest trend. Content can also be generated based on the latest trend while referring to past trends. Furthermore, content can also be generated based on trends that occurred during a specific period. In this way, by determining generation priorities based on the time when a trend occurred, more appropriate content can be generated.

[0136] The posting unit can also estimate the user's emotions and adjust the length of the post based on the estimated user's emotions. For example, if the user is in a hurry, the posting unit can make a short, to-the-point post. If the user is relaxed, the posting unit can make a longer post with detailed explanations. Furthermore, if the user is excited, the posting unit can make a post with visually stimulating effects. In this way, by adjusting the length of the post according to the user's emotions, more appropriate posts can be made.

[0137] The advertising department can apply different advertising algorithms depending on the product category during advertising. For example, a text generation algorithm can be applied to a text advertisement. An image generation algorithm can be applied to an image advertisement. Furthermore, a video generation algorithm can be applied to a video advertisement. In this way, the quality of advertising can be improved by applying an appropriate advertising algorithm depending on the product category.

[0138] The processing flow of the second embodiment will be briefly explained below.

[0139] Step 1: The collection unit collects data. The data includes, for example, the number of likes, shares, comments, the time of posting, and user attribute information. The collection unit obtains the number of likes and shares from the SNS platform, and also collects attribute information such as the user's age, gender, region, and interests. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is carried out using statistical analysis and machine learning algorithms to identify characteristics of content that are likely to go viral, trending keywords, visual elements, emotional elements, etc. Step 3: The generation unit generates content based on the analysis results obtained by the analysis unit. The generated content includes blog articles, videos, images, etc., and uses generative AI to generate images, videos, and catchphrases that match trends. It is also possible to generate original content by referring to past success stories. Step 4: The publishing department posts the content generated by the generation department. Posting includes posting to social media and blogs, and posts at specific times to maximize reach and optimize posting frequency and timing. Step 5: The Promotion Department promotes and sells based on the number of subscribers increased by the Posting Department. Promotion and sales can include online advertising, email marketing, and direct sales, and can use the increased number of subscribers to promote specific products or services and reach specific user demographics through targeted advertising.

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

[0141] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0142] 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.

[0143] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0146] The data processing device 12 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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.

[0151] 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.

[0152] 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.

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

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

[0155] 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.

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

[0157] 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.

[0158] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0162] The data processing device 12 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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.

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

[0170] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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).

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

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

[0187] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0188] 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.

[0189] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0190] 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.

[0191] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0192] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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."

[0199] 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.

[0200] 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.

[0201] 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.

[0202] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] [Explanation of symbols]

[0212] 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 collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a generation unit that generates content based on the analysis result obtained by the analysis unit; a posting unit that posts the content generated by the generation unit; an advertising department that advertises and sells based on the number of registered users increased by the posting department; Equipped with A system characterized by:

2. The collecting unit Collecting information such as the number of likes, shares, comments, time of posting, and user demographic information 2. The system of claim 1.

3. The analysis unit Analyze the collected data and identify the characteristics of content that is likely to go viral 2. The system of claim 1.

4. The generation unit Generate trending images, videos, and catchphrases 2. The system of claim 1.

5. The posting unit: Post at specific times to maximize reach 2. The system of claim 1.

6. The advertising department: Use the increased subscriber numbers to promote specific products or services 2. The system of claim 1.

7. The advertising department: Targeted advertising to reach specific demographics 2. The system of claim 1.

8. The generation unit Generate unique content while referencing past success stories 2. The system of claim 1.

Images