System

Abstract

An object of a system according to an embodiment is to save time and effort for a user to manually select a hashtag and to automatically post the hashtag at an optimum timing.SOLUTION: A system includes a hashtag proposal part, a popularity degree display part, a trend monitoring part, and a schedule management part. The hashtag proposing section proposes a hashtag based on the post content of the user. The popularity display section displays the popularity and relevance of the proposed hashtag. The trend monitoring unit monitors the latest trend and proposes a hashtag based on the trend. A schedule management part manages the schedule of contribution and automatically contributes at optimum timing.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring users to manually select appropriate hashtags and manage the timing of posts, which is cumbersome.

[0005] The system according to the embodiment aims to save users the trouble of manually selecting hashtags and to automatically post at the optimal timing. [Means for solving the problem]

[0006] The system according to the embodiment includes a hashtag suggestion unit, a popularity display unit, a trend monitoring unit, and a schedule management unit. The hashtag suggestion unit suggests hashtags based on the content posted by users. The popularity display unit displays the popularity and relevance of the suggested hashtags. The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. The schedule management unit manages the posting schedule and automatically posts at the optimal timing. [Effects of the Invention]

[0007] The system according to the embodiment eliminates the need for users to manually select hashtags, and allows users to automatically post at the optimal timing. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A hashtag generation tool according to an embodiment of the present invention is a system that automatically suggests appropriate hashtags based on the content of a user's post, thereby increasing the number of impressions. As a result, the hashtag generation tool can suggest optimal hashtags for a user's post, thereby increasing the number of impressions.

[0029] A hashtag generation tool according to an embodiment includes a hashtag suggestion unit, a popularity display unit, a trend monitoring unit, and a schedule management unit. The hashtag suggestion unit suggests hashtags based on the content of a user's posts. For example, the generation AI analyzes text and images posted by a user and suggests hashtags most relevant to the content. For example, the generation AI suggests hashtags such as "#cake #sweets #homemadecake" for a post such as "I made a delicious cake." The popularity display unit displays the popularity and relevance of the suggested hashtags. For example, the generation AI displays that the hashtag "#cake" is very popular and that "#homemadecake" is highly relevant in a specific community. The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. For example, the generation AI analyzes trend data on social media to identify the currently most popular hashtags. If a specific event or topic is rapidly gaining attention, the generation AI suggests related hashtags to the user. The schedule management unit manages posting schedules and automatically posts at the optimal time. For example, the generation AI analyzes the activity data of a user's followers, identifies the most effective posting times, schedules posts to coincide with the time periods when the user's followers are most active, and automatically posts. This allows the hashtag generation tool according to the embodiment to suggest optimal hashtags based on the content of the user's posts, thereby increasing the number of impressions. For example, when a user posts "I made a delicious cake," the generation AI suggests hashtags such as "#cake #sweets #homemadecake" and further indicates that "#cake" is very popular. It also suggests that "#sweetsfestival" is a current trend, and automatically posts at the optimal time, thereby gaining more impressions.

[0030] The hashtag suggestion unit can analyze a user's past posting history and suggest customized hashtags based on their individual posting style or theme. For example, the generation AI analyzes a user's past posting history to identify posting trends and themes. For example, if a user frequently posts about travel, the generation AI will prioritize suggesting travel-related hashtags. The generation AI also analyzes the content of the user's past posts and extracts specific keywords and phrases. For example, if a user posts "I made a delicious cake," the generation AI will suggest hashtags such as "#cake #sweets #homemadecake." The generation AI also learns the user's posting style and suggests hashtags that match their individual style. For example, if a user posts humorously, the generation AI will suggest hashtags related to humor. This allows for more personalized hashtag suggestions based on the user's past posting history.

[0031] The hashtag suggestion unit can analyze the content of images or videos included in the post and suggest hashtags based on visual elements. For example, the hashtag suggestion unit uses a generative AI to analyze the images included in the post and suggest hashtags based on the content of the image. For example, if a cake is in the image, hashtags such as "#cake #sweets #dessert" are suggested. In addition, video analysis technology is used to analyze the content of videos included in the post and suggest related hashtags. For example, for a cooking video, hashtags such as "#cooking #recipe #cooking" are suggested. In addition, image recognition technology is used to identify visual elements included in the post and suggest hashtags based on them. For example, for a landscape photo, hashtags such as "#scenery #nature #travel" are suggested. This allows more appropriate hashtags to be suggested based on the visual elements of the post content.

[0032] The hashtag suggestion unit can collect data from other social media platforms and suggest hashtags that are effective across multiple platforms. For example, the generation AI collects data from other social media platforms (e.g., TikTok, Facebook) and suggests hashtags that are effective across platforms. For example, hashtags that are popular on TikTok are suggested for Instagram posts. The unit also analyzes trending data from other social media platforms to suggest common hashtags. For example, hashtags that are trending on Facebook are suggested for Twitter posts. The unit also analyzes the effectiveness of hashtags across platforms and suggests the most effective hashtags. For example, hashtags that have high engagement across multiple platforms are suggested. This allows the unit to suggest a wider range of hashtags by utilizing data from other social media platforms.

[0033] The hashtag suggestion unit can analyze the interests of followers and suggest hashtags that will resonate most with them. For example, the generation AI in the hashtag suggestion unit analyzes the interests of a user's followers and suggests hashtags that will resonate most with them. For example, if a follower is interested in travel, hashtags such as "#travel #adventure #tourism" are suggested. The unit also analyzes the follower's past response data to identify hashtags with the highest engagement. For example, hashtags that followers have given the most "likes" are suggested. The unit also analyzes the follower's profile information to suggest hashtags based on their interests. For example, if a follower is interested in music, hashtags such as "#music #live #band" are suggested. This allows the unit to suggest more effective hashtags based on the follower's interests.

[0034] The popularity display unit can analyze the popularity of hashtags by time of day and display the most effective usage times. For example, the generation AI in the popularity display unit analyzes the popularity of hashtags by time of day and displays the most effective usage times. For example, it displays that the hashtag "#breakfast" is most effective in the morning. It also analyzes the popularity of hashtags by time of day and suggests the optimal posting times to users. For example, it displays that the hashtag "#nightview" is most effective in the evening. It also suggests the optimal posting schedule to users based on hashtag popularity data by time of day. For example, it displays that the hashtag "#lunch" is most effective in the daytime. In this way, by displaying the effective usage times of hashtags, the number of impressions can be increased.

[0035] The popularity display unit can display the relevance of hashtags individually for specific follower groups of a user. For example, the popularity display unit displays hashtag relevance customized by the generation AI for a specific follower group of a user. For example, it prioritizes displaying hashtags that followers have given many likes. It also analyzes the interests and concerns of the follower group and displays hashtag relevance based on that. For example, if followers are interested in music, it displays hashtags such as "#music #live #band." It also displays highly relevant hashtags for a specific follower group based on past response data. For example, it prioritizes displaying hashtags that followers have given many comments to. This makes it possible to improve engagement by displaying hashtag relevance customized for a specific follower group.

[0036] The popularity display unit can analyze hashtag popularity by region and display the most suitable hashtags for each region. For example, the popularity display unit uses a generation AI to analyze hashtag popularity by region and display the most suitable hashtags for each region. For example, hashtags that are popular in Japan are suggested to Japanese users. Trend data for each region is also analyzed to display the most effective hashtags. For example, hashtags that are popular in the United States are suggested to American users. The most suitable hashtags for users are also suggested based on hashtag popularity data for each region. For example, hashtags that are popular in Europe are suggested to European users. This makes it possible to analyze hashtag popularity by region and display the most suitable hashtags for each region.

[0037] The popularity display unit can analyze the popularity of hashtags related to specific events or campaigns, and display the optimal hashtags for each event. For example, the popularity display unit uses a generation AI to analyze the popularity of hashtags related to specific events or campaigns, and display the optimal hashtags. For example, it suggests hashtags related to music festivals. It also analyzes trend data for events and campaigns and displays the most effective hashtags. For example, it suggests hashtags related to sporting events. It also suggests the optimal hashtags to the user based on hashtag popularity data for specific events or campaigns. For example, it suggests hashtags related to film festivals. This makes it possible to analyze the popularity of hashtags related to specific events or campaigns, and display the optimal hashtags.

[0038] The trend monitoring unit can monitor trends specific to a particular industry or theme and suggest hashtags that are optimal for that field. For example, the generation AI monitors trends specific to a particular industry (e.g., fashion, technology) and suggests hashtags that are optimal for that field. For example, hashtags such as "#latestfashion #trends #style" are suggested based on trends in the fashion industry. Trends specific to a particular theme (e.g., environmental protection, health) are also monitored and hashtags that are optimal for that theme are suggested. For example, hashtags such as "#eco #sustainability #environmentalprotection" are suggested based on trends related to environmental protection. Trend data for each industry or theme is also analyzed to suggest the most effective hashtags. For example, hashtags such as "#AI #IoT #technology" are suggested based on trends in the technology industry. This allows the generation AI to monitor trends specific to a particular industry or theme and suggest hashtags that are optimal for that field.

[0039] The trend monitoring unit can analyze the source of a trend and evaluate the reliability or influence of the trend before suggesting it. For example, the generation AI in the trend monitoring unit analyzes the source of a trend and evaluates its reliability and influence. For example, trends posted by reliable news sites or influential influencers are preferentially suggested. The trend monitoring unit also analyzes the source of a trend and identifies trends with high influence. For example, trends posted by influencers with a large number of followers are suggested. Furthermore, to evaluate the reliability of a trend, data from multiple sources is analyzed and matching trends are suggested. For example, trends reported on multiple news sites are preferentially suggested. This makes it possible to analyze the source of a trend and evaluate its reliability and influence before suggesting it.

[0040] The trend monitoring unit can monitor trends in different languages ​​or cultural spheres and suggest hashtags from a global perspective. In the trend monitoring unit, for example, the generation AI monitors trends in different languages ​​or cultural spheres and suggests hashtags from a global perspective. For example, it analyzes trends in multiple languages, such as English, French, and Chinese. It also analyzes trend data from different cultural spheres and suggests the most effective hashtags. For example, it suggests hashtags based on trends by region, such as Asia, Europe, and America. It also analyzes trends from a global perspective and suggests hashtags that are commonly used in different languages ​​and cultural spheres. For example, it suggests hashtags related to international events or topics. This makes it possible to monitor trends in different languages ​​and cultural spheres and suggest hashtags from a global perspective.

[0041] The trend monitoring unit monitors trends related to specific seasons or events and can suggest the most suitable hashtags for each season. For example, the generation AI monitors trends related to specific seasons (e.g., spring, summer, fall, winter) and suggests the most suitable hashtags for each season. For example, in spring, it suggests hashtags such as "#cherryblossoms #springarrival #flowerviewing." It also monitors trends related to seasonal events (e.g., Christmas, Halloween) and suggests the most effective hashtags. For example, it suggests hashtags such as "#christmas #santaclaus #presents" for Christmas. It also analyzes trend data for each season and event and suggests the most suitable hashtags for users. For example, it suggests hashtags such as "#beach #summervacation #ocean" in summer. This allows it to monitor trends related to specific seasons and events and suggest the most suitable hashtags for each season.

[0042] The schedule management unit can analyze a user's past posting data and individually suggest the most effective posting schedule. For example, the schedule management unit uses a generation AI to analyze a user's past posting data and customize and suggest the most effective posting schedule. For example, it identifies the time of day when past posts received the most engagement. It also analyzes the user's posting history and suggests the optimal posting time. For example, it schedules posts to match the time of day when followers are most active. It also suggests the optimal posting schedule for the user based on past posting data. For example, if posts receive a lot of engagement on weekends, it schedules posts for the weekend. This allows it to analyze a user's past posting data and customize and suggest the most effective posting schedule.

[0043] The schedule management unit can analyze follower activity patterns and schedule posts according to the time periods when followers are most active. For example, the schedule management unit uses a generation AI to analyze follower activity patterns and schedule posts according to the time periods when followers are most active. For example, it identifies the time periods when followers leave the most likes. It also analyzes follower activity data and suggests the optimal posting time. For example, it schedules posts for the time periods when followers leave the most comments. It also suggests the optimal posting schedule to users based on follower activity patterns. For example, it automatically posts according to the time periods when followers are most active. This makes it possible to schedule posts for the most effective time periods based on follower activity patterns.

[0044] The schedule management unit can integrate posting schedules for different social media platforms and suggest the optimal posting timing across multiple platforms. For example, the schedule management unit uses a generative AI to integrate posting schedules for different social media platforms and suggest the optimal posting timing across platforms. For example, it adjusts posting times for Instagram and Twitter. It also analyzes activity data from different platforms and suggests the optimal posting timing. For example, it integrates posting times for Facebook and LinkedIn. It also optimizes cross-platform posting schedules and suggests the optimal posting timing to users. For example, it posts on multiple platforms simultaneously. This allows it to integrate posting schedules for different social media platforms and suggest the optimal posting timing across platforms.

[0045] The schedule management unit proposes a posting schedule tailored to a specific event or campaign, allowing posts to be made at the optimal timing for each event. For example, the schedule management unit uses a generation AI to propose a posting schedule tailored to a specific event or campaign, and posts at the optimal timing. For example, posts may be scheduled to coincide with a music festival. Trend data for events and campaigns may also be analyzed to propose the most effective posting timing. For example, posts may be scheduled to coincide with a sporting event. Posting schedules for specific events and campaigns may also be optimized, and the optimal posting timing may be suggested to users. For example, posts may be scheduled to coincide with a film festival. This allows a posting schedule tailored to a specific event or campaign to be proposed, allowing posts to be made at the optimal timing.

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

[0047] The hashtag suggestion unit suggests hashtags based on the content of a user's posts. For example, the generation AI analyzes the text and images posted by the user and suggests hashtags that are most relevant to that content. For example, the generation AI might suggest hashtags such as "#cake #sweets #homemadecake" for a post such as "I made a delicious cake." The popularity display unit displays the popularity and relevance of the suggested hashtags. For example, the generation AI might indicate that the hashtag "#cake" is very popular and that "#homemadecake" is highly relevant in a particular community. The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. For example, the generation AI might analyze trend data on social media to identify the currently most popular hashtags. If a specific event or topic is rapidly gaining attention, the generation AI might suggest related hashtags to the user. The schedule management unit manages posting schedules and automatically posts at the optimal times. For example, the generation AI might analyze the activity data of a user's followers to identify the most effective posting times, schedule posts to coincide with the time periods when the user's followers are most active, and then automatically post. As a result, the hashtag generation tool according to the embodiment can suggest optimal hashtags based on the content of a user's post, thereby increasing the number of impressions. For example, when a user posts "I made a delicious cake," the generation AI suggests hashtags such as "#cake #sweets #homemadecake" and further indicates that "#cake" is very popular. It also suggests that "#sweetsfestival" is a current trend, and automatically posts at the optimal time, thereby gaining more impressions.

[0048] The hashtag suggestion unit can analyze a user's past posting history and suggest customized hashtags based on their individual posting style or theme. For example, the generation AI can analyze a user's past posting history to identify posting trends and themes. For example, if a user frequently posts about travel, travel-related hashtags can be prioritized. The generation AI can also analyze the content of a user's past posts and extract specific keywords and phrases. For example, if a user posts "I made a delicious cake," the generation AI can suggest hashtags such as "#cake #sweets #homemadecake." The generation AI can also learn the user's posting style and suggest hashtags that match their individual style. For example, if a user posts humorously, it can suggest hashtags related to humor. This allows for more personalized hashtag suggestions based on the user's past posting history.

[0049] The hashtag suggestion unit can analyze the content of images or videos included in the post and suggest hashtags based on visual elements. For example, the generative AI analyzes the images included in the post and suggests hashtags based on the content of the image. For example, if the image shows a cake, hashtags such as "#cake #sweets #dessert" are suggested. Video analysis technology is also used to analyze the content of the video included in the post and suggest related hashtags. For example, for a cooking video, hashtags such as "#cooking #recipe #cooking" are suggested. Image recognition technology is also used to identify visual elements included in the post and suggest hashtags based on them. For example, for a landscape photo, hashtags such as "#scenery #nature #travel" are suggested. This allows more appropriate hashtags to be suggested based on the visual elements of the post.

[0050] The hashtag suggestion unit can collect data from other social media platforms to suggest hashtags that are effective across multiple platforms. For example, the generative AI collects data from other social media platforms (e.g., TikTok, Facebook) to suggest cross-platform hashtags. For example, hashtags that are popular on TikTok may be suggested for Instagram posts. It may also analyze trending data from other social media platforms to suggest common hashtags. For example, hashtags that are trending on Facebook may be suggested for Twitter posts. It may also analyze the effectiveness of hashtags across platforms to suggest the most effective hashtags. For example, it may suggest hashtags that have high engagement across multiple platforms. This allows it to suggest a wider range of hashtags by leveraging data from other social media platforms.

[0051] The hashtag suggestion unit can analyze followers' interests and suggest hashtags that will resonate most with them. For example, the generation AI analyzes the interests and concerns of a user's followers and suggests hashtags that will resonate most with them. For example, if a follower is interested in travel, hashtags such as "#travel #adventure #tourism" are suggested. The system also analyzes followers' past response data to identify hashtags with the highest engagement. For example, hashtags that followers have given the most "likes" are suggested. The system also analyzes followers' profile information to suggest hashtags based on their interests and concerns. For example, if a follower is interested in music, hashtags such as "#music #live #band" are suggested. This allows the system to suggest more effective hashtags based on followers' interests and concerns.

[0052] The popularity display unit can analyze the popularity of hashtags by time of day and display the most effective times to use them. For example, the generation AI can analyze the popularity of hashtags by time of day and display the most effective times to use them. For example, it can display that the hashtag "#breakfast" is most effective in the morning. It can also analyze the popularity of hashtags by time of day and suggest the optimal posting times to users. For example, it can display that the hashtag "#nightview" is most effective in the evening. It can also suggest the optimal posting schedule to users based on hashtag popularity data by time of day. For example, it can display that the hashtag "#lunch" is most effective in the daytime. In this way, by displaying the effective times to use hashtags, the number of impressions can be increased.

[0053] The popularity display unit can display hashtag relevance individually for a user's specific follower group. For example, the generation AI displays hashtag relevance customized for a user's specific follower group. For example, hashtags that followers have given many likes are displayed preferentially. The unit also analyzes the interests of the follower group and displays hashtag relevance based on that. For example, if followers are interested in music, hashtags such as "#music #live #band" are displayed. The unit also displays highly relevant hashtags for a specific follower group based on past response data. For example, hashtags that followers have given many comments are displayed preferentially. This makes it possible to improve engagement by displaying hashtag relevance customized for a specific follower group.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The hashtag suggestion unit suggests hashtags based on the content of the user's post. For example, the generative AI analyzes the text and images posted by the user and suggests hashtags that are most relevant to that content. Specifically, for a post such as "I made a delicious cake," it would suggest hashtags such as "#cake #sweets #homemadecake." Step 2: The popularity indicator displays the popularity and relevance of the proposed hashtags. For example, it displays that the hashtag "#cake" is very popular, and that "#homemadecake" has high relevance in a specific community. Step 3: The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. For example, the generation AI analyzes trend data on social media to identify the currently most popular hashtags, and if a specific event or topic is rapidly gaining attention, it will suggest related hashtags to users. Step 4: The schedule management unit manages the posting schedule and automatically posts at the optimal time. For example, the generation AI analyzes the activity data of the user's followers, identifies the most effective posting times, schedules posts to coincide with the time periods when the user's followers are most active, and automatically posts.

[0056] (Example 2) A hashtag generation tool according to an embodiment of the present invention is a system that automatically suggests appropriate hashtags based on the content of a user's post, thereby increasing the number of impressions. As a result, the hashtag generation tool can suggest optimal hashtags for a user's post, thereby increasing the number of impressions.

[0057] A hashtag generation tool according to an embodiment includes a hashtag suggestion unit, a popularity display unit, a trend monitoring unit, and a schedule management unit. The hashtag suggestion unit suggests hashtags based on the content of a user's posts. For example, the generation AI analyzes text and images posted by a user and suggests hashtags most relevant to the content. For example, the generation AI suggests hashtags such as "#cake #sweets #homemadecake" for a post such as "I made a delicious cake." The popularity display unit displays the popularity and relevance of the suggested hashtags. For example, the generation AI displays that the hashtag "#cake" is very popular and that "#homemadecake" is highly relevant in a specific community. The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. For example, the generation AI analyzes trend data on social media to identify the currently most popular hashtags. If a specific event or topic is rapidly gaining attention, the generation AI suggests related hashtags to the user. The schedule management unit manages posting schedules and automatically posts at the optimal time. For example, the generation AI analyzes the activity data of a user's followers, identifies the most effective posting times, schedules posts to coincide with the time periods when the user's followers are most active, and automatically posts. This allows the hashtag generation tool according to the embodiment to suggest optimal hashtags based on the content of the user's posts, thereby increasing the number of impressions. For example, when a user posts "I made a delicious cake," the generation AI suggests hashtags such as "#cake #sweets #homemadecake" and further indicates that "#cake" is very popular. It also suggests that "#sweetsfestival" is a current trend, and automatically posts at the optimal time, thereby gaining more impressions.

[0058] The hashtag suggestion unit can analyze a user's past posting history and suggest customized hashtags based on their individual posting style or theme. For example, the generation AI analyzes a user's past posting history to identify posting trends and themes. For example, if a user frequently posts about travel, the generation AI will prioritize suggesting travel-related hashtags. The generation AI also analyzes the content of the user's past posts and extracts specific keywords and phrases. For example, if a user posts "I made a delicious cake," the generation AI will suggest hashtags such as "#cake #sweets #homemadecake." The generation AI also learns the user's posting style and suggests hashtags that match their individual style. For example, if a user posts humorously, the generation AI will suggest hashtags related to humor. This allows for more personalized hashtag suggestions based on the user's past posting history.

[0059] The hashtag suggestion unit can analyze the content of images or videos included in the post and suggest hashtags based on visual elements. For example, the hashtag suggestion unit uses a generative AI to analyze the images included in the post and suggest hashtags based on the content of the image. For example, if a cake is in the image, hashtags such as "#cake #sweets #dessert" are suggested. In addition, video analysis technology is used to analyze the content of videos included in the post and suggest related hashtags. For example, for a cooking video, hashtags such as "#cooking #recipe #cooking" are suggested. In addition, image recognition technology is used to identify visual elements included in the post and suggest hashtags based on them. For example, for a landscape photo, hashtags such as "#scenery #nature #travel" are suggested. This allows more appropriate hashtags to be suggested based on the visual elements of the post content.

[0060] The hashtag suggestion unit can use the emotion estimation function to analyze the emotion of the posted content and suggest hashtags that are appropriate for that emotion. For example, the hashtag suggestion unit uses the emotion estimation function to analyze the emotion of the posted content and suggest hashtags that match positive emotions. For example, hashtags such as "#happy #fun #delighted" are suggested for posts with a strong emotion of joy. The hashtag suggestion unit can also analyze the emotion of the posted content in real time and suggest hashtags based on that emotion. For example, hashtags such as "#moving #tears #heartwarming" are suggested for moving posts. The emotion estimation function can also be used to suggest appropriate hashtags for posts with negative emotions. For example, hashtags such as "#sad #brokenhearted #tears" are suggested for posts with a strong emotion of sadness. This makes it possible to suggest hashtags that are more likely to resonate with users based on the emotion of the posted content.

[0061] The hashtag suggestion unit can collect data from other social media platforms and suggest hashtags that are effective across multiple platforms. For example, the generation AI collects data from other social media platforms (e.g., TikTok, Facebook) and suggests hashtags that are effective across platforms. For example, hashtags that are popular on TikTok are suggested for Instagram posts. The unit also analyzes trending data from other social media platforms to suggest common hashtags. For example, hashtags that are trending on Facebook are suggested for Twitter posts. The unit also analyzes the effectiveness of hashtags across platforms and suggests the most effective hashtags. For example, hashtags that have high engagement across multiple platforms are suggested. This allows the unit to suggest a wider range of hashtags by utilizing data from other social media platforms.

[0062] The hashtag suggestion unit can analyze the interests of followers and suggest hashtags that will resonate most with them. For example, the generation AI in the hashtag suggestion unit analyzes the interests of a user's followers and suggests hashtags that will resonate most with them. For example, if a follower is interested in travel, hashtags such as "#travel #adventure #tourism" are suggested. The unit also analyzes the follower's past response data to identify hashtags with the highest engagement. For example, hashtags that followers have given the most "likes" are suggested. The unit also analyzes the follower's profile information to suggest hashtags based on their interests. For example, if a follower is interested in music, hashtags such as "#music #live #band" are suggested. This allows the unit to suggest more effective hashtags based on the follower's interests.

[0063] The hashtag suggestion unit can use the emotion estimation function to analyze the emotions of a user when creating a post in real time and suggest hashtags that elicit positive emotions. For example, the hashtag suggestion unit uses the emotion estimation function to analyze the emotions of a user when creating a post in real time and suggest hashtags that elicit positive emotions. For example, if the user is feeling happy, hashtags such as "#happy #fun #delighted" are suggested. The hashtag suggestion unit also analyzes the user's emotions when creating a post and suggests hashtags that reinforce positive emotions. For example, if the user is moved, hashtags such as "#moving #heartwarming #tears" are suggested. The hashtag suggestion unit also uses the emotion estimation function to suggest hashtags that will change the user's emotions to positive ones. For example, if the user is feeling a little down, hashtags such as "#energetic #positive #smile" are suggested. This makes it possible to suggest hashtags that elicit positive emotions based on the user's emotions.

[0064] The popularity display unit can analyze the popularity of hashtags by time of day and display the most effective usage times. For example, the generation AI in the popularity display unit analyzes the popularity of hashtags by time of day and displays the most effective usage times. For example, it displays that the hashtag "#breakfast" is most effective in the morning. It also analyzes the popularity of hashtags by time of day and suggests the optimal posting times to users. For example, it displays that the hashtag "#nightview" is most effective in the evening. It also suggests the optimal posting schedule to users based on hashtag popularity data by time of day. For example, it displays that the hashtag "#lunch" is most effective in the daytime. In this way, by displaying the effective usage times of hashtags, the number of impressions can be increased.

[0065] The popularity display unit can display the relevance of hashtags individually for specific follower groups of a user. For example, the popularity display unit displays hashtag relevance customized by the generation AI for a specific follower group of a user. For example, it prioritizes displaying hashtags that followers have given many likes. It also analyzes the interests and concerns of the follower group and displays hashtag relevance based on that. For example, if followers are interested in music, it displays hashtags such as "#music #live #band." It also displays highly relevant hashtags for a specific follower group based on past response data. For example, it prioritizes displaying hashtags that followers have given many comments to. This makes it possible to improve engagement by displaying hashtag relevance customized for a specific follower group.

[0066] The popularity display unit can use the emotion estimation function to analyze users' emotional reactions to the popularity and relevance of hashtags and display hashtags that are likely to resonate emotionally. The popularity display unit can, for example, use the emotion estimation function to analyze users' emotional reactions to the popularity and relevance of hashtags and display hashtags that are likely to resonate emotionally. For example, hashtags with a high number of positive emotional reactions are preferentially displayed. Furthermore, hashtags that are likely to resonate emotionally are identified and displayed based on the user's emotional reaction data. For example, hashtags such as "#moving #tears #heartwarming" are displayed for a moving post. Furthermore, the emotion estimation function can be used to display the popularity and relevance of hashtags based on the user's emotions. For example, hashtags such as "#happy #fun #delighted" are displayed for a post with a strong emotion of joy. In this way, hashtags that are likely to resonate emotionally can be displayed based on the user's emotional reaction.

[0067] The popularity display unit can analyze hashtag popularity by region and display the most suitable hashtags for each region. For example, the popularity display unit uses a generation AI to analyze hashtag popularity by region and display the most suitable hashtags for each region. For example, hashtags that are popular in Japan are suggested to Japanese users. Trend data for each region is also analyzed to display the most effective hashtags. For example, hashtags that are popular in the United States are suggested to American users. The most suitable hashtags for users are also suggested based on hashtag popularity data for each region. For example, hashtags that are popular in Europe are suggested to European users. This makes it possible to analyze hashtag popularity by region and display the most suitable hashtags for each region.

[0068] The popularity display unit can analyze the popularity of hashtags related to specific events or campaigns, and display the optimal hashtags for each event. For example, the popularity display unit uses a generation AI to analyze the popularity of hashtags related to specific events or campaigns, and display the optimal hashtags. For example, it suggests hashtags related to music festivals. It also analyzes trend data for events and campaigns and displays the most effective hashtags. For example, it suggests hashtags related to sporting events. It also suggests the optimal hashtags to the user based on hashtag popularity data for specific events or campaigns. For example, it suggests hashtags related to film festivals. This makes it possible to analyze the popularity of hashtags related to specific events or campaigns, and display the optimal hashtags.

[0069] The popularity display unit can use the emotion estimation function to identify hashtags that evoke the most positive emotions in users and display their popularity and relevance. For example, the popularity display unit can use the emotion estimation function to identify hashtags that evoke the most positive emotions in users and display their popularity and relevance. For example, hashtags that evoke a strong emotion of joy are preferentially displayed. Furthermore, hashtags that evoke positive emotions are identified and displayed based on the user's emotional response data. For example, hashtags such as "#moving #tears #heartwarming" are displayed for a moving post. Furthermore, the emotion estimation function can be used to display the popularity and relevance of hashtags based on the user's emotions. For example, hashtags such as "#happy #fun #delighted" are displayed for a post that evokes a strong emotion of joy. In this way, the hashtags that evoke the most positive emotions in users can be identified and their popularity and relevance can be displayed.

[0070] The trend monitoring unit can monitor trends specific to a particular industry or theme and suggest hashtags that are optimal for that field. For example, the generation AI monitors trends specific to a particular industry (e.g., fashion, technology) and suggests hashtags that are optimal for that field. For example, hashtags such as "#latestfashion #trends #style" are suggested based on trends in the fashion industry. Trends specific to a particular theme (e.g., environmental protection, health) are also monitored and hashtags that are optimal for that theme are suggested. For example, hashtags such as "#eco #sustainability #environmentalprotection" are suggested based on trends related to environmental protection. Trend data for each industry or theme is also analyzed to suggest the most effective hashtags. For example, hashtags such as "#AI #IoT #technology" are suggested based on trends in the technology industry. This allows the generation AI to monitor trends specific to a particular industry or theme and suggest hashtags that are optimal for that field.

[0071] The trend monitoring unit can analyze the source of a trend and evaluate the reliability or influence of the trend before suggesting it. For example, the generation AI in the trend monitoring unit analyzes the source of a trend and evaluates its reliability and influence. For example, trends posted by reliable news sites or influential influencers are preferentially suggested. The trend monitoring unit also analyzes the source of a trend and identifies trends with high influence. For example, trends posted by influencers with a large number of followers are suggested. Furthermore, to evaluate the reliability of a trend, data from multiple sources is analyzed and matching trends are suggested. For example, trends reported on multiple news sites are preferentially suggested. This makes it possible to analyze the source of a trend and evaluate its reliability and influence before suggesting it.

[0072] The trend monitoring unit can use the emotion estimation function to analyze a user's emotional response to a trend and suggest emotionally positive trends. The trend monitoring unit, for example, uses the emotion estimation function to analyze a user's emotional response to a trend and suggest emotionally positive trends. For example, trends that evoke strong emotions of joy or excitement are preferentially suggested. Furthermore, based on the user's emotional response data, trends that elicit positive emotions are identified and suggested. For example, hashtags such as "#moving #tears #heartwarming" are suggested for an emotional trend. Furthermore, the emotion estimation function is used to suggest trends based on the user's emotions. For example, hashtags such as "#happy #fun #delighted" are suggested for a trend that evokes strong emotions of joy. In this way, the user's emotional response to a trend can be analyzed and emotionally positive trends can be suggested.

[0073] The trend monitoring unit can monitor trends in different languages ​​or cultural spheres and suggest hashtags from a global perspective. In the trend monitoring unit, for example, the generation AI monitors trends in different languages ​​or cultural spheres and suggests hashtags from a global perspective. For example, it analyzes trends in multiple languages, such as English, French, and Chinese. It also analyzes trend data from different cultural spheres and suggests the most effective hashtags. For example, it suggests hashtags based on trends by region, such as Asia, Europe, and America. It also analyzes trends from a global perspective and suggests hashtags that are commonly used in different languages ​​and cultural spheres. For example, it suggests hashtags related to international events or topics. This makes it possible to monitor trends in different languages ​​and cultural spheres and suggest hashtags from a global perspective.

[0074] The trend monitoring unit monitors trends related to specific seasons or events and can suggest the most suitable hashtags for each season. For example, the generation AI monitors trends related to specific seasons (e.g., spring, summer, fall, winter) and suggests the most suitable hashtags for each season. For example, in spring, it suggests hashtags such as "#cherryblossoms #springarrival #flowerviewing." It also monitors trends related to seasonal events (e.g., Christmas, Halloween) and suggests the most effective hashtags. For example, it suggests hashtags such as "#christmas #santaclaus #presents" for Christmas. It also analyzes trend data for each season and event and suggests the most suitable hashtags for users. For example, it suggests hashtags such as "#beach #summervacation #ocean" in summer. This allows it to monitor trends related to specific seasons and events and suggest the most suitable hashtags for each season.

[0075] The trend monitoring unit can use the emotion estimation function to identify trends that users are most interested in and suggest hashtags based on those trends. For example, the trend monitoring unit uses the emotion estimation function to identify trends that users are most interested in and suggest hashtags based on those trends. For example, hashtags such as "#exciting #thrilling #adventure" are suggested for trends that users are excited about. Also, based on the user's emotional response data, interesting trends are identified and suggested. For example, hashtags such as "#moving #tears #heartwarming" are suggested for moving trends. Also, the emotion estimation function is used to suggest trends based on the user's emotions. For example, hashtags such as "#happy #fun #delighted" are suggested for trends that have a strong emotion of joy. In this way, it is possible to identify trends that users are most interested in and suggest hashtags based on those trends.

[0076] The schedule management unit can analyze a user's past posting data and individually suggest the most effective posting schedule. For example, the schedule management unit uses a generation AI to analyze a user's past posting data and customize and suggest the most effective posting schedule. For example, it identifies the time of day when past posts received the most engagement. It also analyzes the user's posting history and suggests the optimal posting time. For example, it schedules posts to match the time of day when followers are most active. It also suggests the optimal posting schedule for the user based on past posting data. For example, if posts receive a lot of engagement on weekends, it schedules posts for the weekend. This allows it to analyze a user's past posting data and customize and suggest the most effective posting schedule.

[0077] The schedule management unit can analyze follower activity patterns and schedule posts according to the time periods when followers are most active. For example, the schedule management unit uses a generation AI to analyze follower activity patterns and schedule posts according to the time periods when followers are most active. For example, it identifies the time periods when followers leave the most likes. It also analyzes follower activity data and suggests the optimal posting time. For example, it schedules posts for the time periods when followers leave the most comments. It also suggests the optimal posting schedule to users based on follower activity patterns. For example, it automatically posts according to the time periods when followers are most active. This makes it possible to schedule posts for the most effective time periods based on follower activity patterns.

[0078] The schedule management unit can use the emotion estimation function to suggest the optimal posting timing based on the user's emotional state. The schedule management unit, for example, uses the emotion estimation function to suggest the optimal posting timing based on the user's emotional state. For example, posts are scheduled for times when the user is feeling positive. The schedule management unit also analyzes the user's emotional state in real time to suggest the optimal posting timing. For example, posts are scheduled for times when the user is feeling emotional. The emotion estimation function also suggests a posting schedule based on the user's emotional state. For example, posts are automatically made during times when the user is feeling happy. This makes it possible to suggest the optimal posting timing based on the user's emotional state.

[0079] The schedule management unit can integrate posting schedules for different social media platforms and suggest the optimal posting timing across multiple platforms. For example, the schedule management unit uses a generative AI to integrate posting schedules for different social media platforms and suggest the optimal posting timing across platforms. For example, it adjusts posting times for Instagram and Twitter. It also analyzes activity data from different platforms and suggests the optimal posting timing. For example, it integrates posting times for Facebook and LinkedIn. It also optimizes cross-platform posting schedules and suggests the optimal posting timing to users. For example, it posts on multiple platforms simultaneously. This allows it to integrate posting schedules for different social media platforms and suggest the optimal posting timing across platforms.

[0080] The schedule management unit proposes a posting schedule tailored to a specific event or campaign, allowing posts to be made at the optimal timing for each event. For example, the schedule management unit uses a generation AI to propose a posting schedule tailored to a specific event or campaign, and posts at the optimal timing. For example, posts may be scheduled to coincide with a music festival. Trend data for events and campaigns may also be analyzed to propose the most effective posting timing. For example, posts may be scheduled to coincide with a sporting event. Posting schedules for specific events and campaigns may also be optimized, and the optimal posting timing may be suggested to users. For example, posts may be scheduled to coincide with a film festival. This allows a posting schedule tailored to a specific event or campaign to be proposed, allowing posts to be made at the optimal timing.

[0081] The schedule management unit can use the emotion estimation function to schedule posts according to the time period when the user is feeling the most positive emotions and automatically post them. The schedule management unit, for example, uses the emotion estimation function to schedule posts according to the time period when the user is feeling the most positive emotions and automatically post them. For example, posts are made during a time period when the user is feeling happy. The schedule management unit also analyzes the user's emotional state in real time and schedules posts for time periods when positive emotions are strongest. For example, posts are made during a time period when the user is feeling moved. The schedule management unit also uses the emotion estimation function to propose a posting schedule based on the user's emotional state and automatically post them. For example, posts are made during a time period when the user is feeling positive emotions. In this way, posts can be scheduled according to the time period when the user is feeling the most positive emotions and automatically post them.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The hashtag suggestion unit suggests hashtags based on the content of a user's posts. For example, the generation AI analyzes the text and images posted by the user and suggests hashtags that are most relevant to that content. For example, the generation AI might suggest hashtags such as "#cake #sweets #homemadecake" for a post such as "I made a delicious cake." The popularity display unit displays the popularity and relevance of the suggested hashtags. For example, the generation AI might indicate that the hashtag "#cake" is very popular and that "#homemadecake" is highly relevant in a particular community. The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. For example, the generation AI might analyze trend data on social media to identify the currently most popular hashtags. If a specific event or topic is rapidly gaining attention, the generation AI might suggest related hashtags to the user. The schedule management unit manages posting schedules and automatically posts at the optimal times. For example, the generation AI might analyze the activity data of a user's followers to identify the most effective posting times, schedule posts to coincide with the time periods when the user's followers are most active, and then automatically post. As a result, the hashtag generation tool according to the embodiment can suggest optimal hashtags based on the content of a user's post, thereby increasing the number of impressions. For example, when a user posts "I made a delicious cake," the generation AI suggests hashtags such as "#cake #sweets #homemadecake" and further indicates that "#cake" is very popular. It also suggests that "#sweetsfestival" is a current trend, and automatically posts at the optimal time, thereby gaining more impressions.

[0084] The hashtag suggestion unit can analyze a user's past posting history and suggest customized hashtags based on their individual posting style or theme. For example, the generation AI can analyze a user's past posting history to identify posting trends and themes. For example, if a user frequently posts about travel, travel-related hashtags can be prioritized. The generation AI can also analyze the content of a user's past posts and extract specific keywords and phrases. For example, if a user posts "I made a delicious cake," the generation AI can suggest hashtags such as "#cake #sweets #homemadecake." The generation AI can also learn the user's posting style and suggest hashtags that match their individual style. For example, if a user posts humorously, it can suggest hashtags related to humor. This allows for more personalized hashtag suggestions based on the user's past posting history.

[0085] The hashtag suggestion unit can analyze the content of images or videos included in the post and suggest hashtags based on visual elements. For example, the generative AI analyzes the images included in the post and suggests hashtags based on the content of the image. For example, if the image shows a cake, hashtags such as "#cake #sweets #dessert" are suggested. Video analysis technology is also used to analyze the content of the video included in the post and suggest related hashtags. For example, for a cooking video, hashtags such as "#cooking #recipe #cooking" are suggested. Image recognition technology is also used to identify visual elements included in the post and suggest hashtags based on them. For example, for a landscape photo, hashtags such as "#scenery #nature #travel" are suggested. This allows more appropriate hashtags to be suggested based on the visual elements of the post.

[0086] The hashtag suggestion unit can use the emotion estimation function to analyze the emotion of the posted content and suggest hashtags that are appropriate for that emotion. For example, the emotion estimation function can be used to analyze the emotion of the posted content and suggest hashtags that match positive emotions. For example, hashtags such as "#happy #fun #delighted" can be suggested for posts with a strong emotion of joy. The emotion of the posted content can also be analyzed in real time and hashtags based on that emotion can be suggested. For example, hashtags such as "#moving #tears #heartwarming" can be suggested for moving posts. The emotion estimation function can also be used to suggest appropriate hashtags for posts with negative emotions. For example, hashtags such as "#sad #brokenhearted #tears" can be suggested for posts with a strong emotion of sadness. This makes it possible to suggest hashtags that are more likely to resonate with people based on the emotion of the posted content.

[0087] The hashtag suggestion unit can collect data from other social media platforms to suggest hashtags that are effective across multiple platforms. For example, the generative AI collects data from other social media platforms (e.g., TikTok, Facebook) to suggest cross-platform hashtags. For example, hashtags that are popular on TikTok may be suggested for Instagram posts. It may also analyze trending data from other social media platforms to suggest common hashtags. For example, hashtags that are trending on Facebook may be suggested for Twitter posts. It may also analyze the effectiveness of hashtags across platforms to suggest the most effective hashtags. For example, it may suggest hashtags that have high engagement across multiple platforms. This allows it to suggest a wider range of hashtags by leveraging data from other social media platforms.

[0088] The hashtag suggestion unit can analyze followers' interests and suggest hashtags that will resonate most with them. For example, the generation AI analyzes the interests and concerns of a user's followers and suggests hashtags that will resonate most with them. For example, if a follower is interested in travel, hashtags such as "#travel #adventure #tourism" are suggested. The system also analyzes followers' past response data to identify hashtags with the highest engagement. For example, hashtags that followers have given the most "likes" are suggested. The system also analyzes followers' profile information to suggest hashtags based on their interests and concerns. For example, if a follower is interested in music, hashtags such as "#music #live #band" are suggested. This allows the system to suggest more effective hashtags based on followers' interests and concerns.

[0089] The hashtag suggestion unit can use the emotion estimation function to analyze the emotions of a user when creating a post in real time and suggest hashtags that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions of a user when creating a post in real time and suggest hashtags that elicit positive emotions. For example, if the user is feeling happy, hashtags such as "#happy #fun #delighted" are suggested. The hashtag suggestion unit can also analyze the user's emotions when creating a post and suggest hashtags that reinforce positive emotions. For example, if the user is moved, hashtags such as "#moving #heartwarming #tears" are suggested. The emotion estimation function can also be used to suggest hashtags that will change the user's emotions to positive ones. For example, if the user is feeling a little down, hashtags such as "#energetic #positive #smile" are suggested. This makes it possible to suggest hashtags that elicit positive emotions based on the user's emotions.

[0090] The popularity display unit can analyze the popularity of hashtags by time of day and display the most effective times to use them. For example, the generation AI can analyze the popularity of hashtags by time of day and display the most effective times to use them. For example, it can display that the hashtag "#breakfast" is most effective in the morning. It can also analyze the popularity of hashtags by time of day and suggest the optimal posting times to users. For example, it can display that the hashtag "#nightview" is most effective in the evening. It can also suggest the optimal posting schedule to users based on hashtag popularity data by time of day. For example, it can display that the hashtag "#lunch" is most effective in the daytime. In this way, by displaying the effective times to use hashtags, the number of impressions can be increased.

[0091] The popularity display unit can display hashtag relevance individually for a user's specific follower group. For example, the generation AI displays hashtag relevance customized for a user's specific follower group. For example, hashtags that followers have given many likes are displayed preferentially. The unit also analyzes the interests of the follower group and displays hashtag relevance based on that. For example, if followers are interested in music, hashtags such as "#music #live #band" are displayed. The unit also displays highly relevant hashtags for a specific follower group based on past response data. For example, hashtags that followers have given many comments are displayed preferentially. This makes it possible to improve engagement by displaying hashtag relevance customized for a specific follower group.

[0092] The popularity display unit can use the emotion estimation function to analyze users' emotional reactions to the popularity and relevance of hashtags and display hashtags that are likely to resonate emotionally. For example, the emotion estimation function can be used to analyze users' emotional reactions to the popularity and relevance of hashtags and display hashtags that are likely to resonate emotionally. For example, hashtags with a high number of positive emotional reactions can be preferentially displayed. Furthermore, hashtags that are likely to resonate emotionally can be identified and displayed based on the user's emotional reaction data. For example, hashtags such as "#moving #tears #heartwarming" can be displayed for a moving post. Furthermore, the emotion estimation function can be used to display the popularity and relevance of hashtags based on the user's emotions. For example, hashtags such as "#happy #fun #delighted" can be displayed for a post with a strong emotion of joy. In this way, hashtags that are likely to resonate emotionally can be displayed based on the user's emotional reaction.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The hashtag suggestion unit suggests hashtags based on the content of the user's post. For example, the generative AI analyzes the text and images posted by the user and suggests hashtags that are most relevant to that content. Specifically, for a post such as "I made a delicious cake," it would suggest hashtags such as "#cake #sweets #homemadecake." Step 2: The popularity indicator displays the popularity and relevance of the proposed hashtags. For example, it displays that the hashtag "#cake" is very popular, and that "#homemadecake" has high relevance in a specific community. Step 3: The trend monitoring unit monitors the latest trends and suggests hashtags based on the trends. For example, the generation AI analyzes trend data on social media to identify the currently most popular hashtags, and if a specific event or topic is rapidly gaining attention, it will suggest related hashtags to users. Step 4: The schedule management unit manages the posting schedule and automatically posts at the optimal time. For example, the generation AI analyzes the activity data of the user's followers, identifies the most effective posting times, schedules posts to coincide with the time periods when the user's followers are most active, and automatically posts.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] 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 AI 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.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] 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 AI 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.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] 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 AI 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0162] 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. The system comprises a hashtag suggestion unit that suggests hashtags based on the content of user posts, a popularity display unit that displays the popularity and relevance of the suggested hashtags, a trend monitoring unit that monitors the latest trends and suggests hashtags based on the trends, and a schedule management unit that manages posting schedules and automatically posts at optimal times.

2. The system according to claim 1 , wherein the hashtag suggestion unit analyzes the content of an image or video included in the posted content and suggests hashtags based on visual elements.

3. The system of claim 1 , wherein the popularity display unit displays the relevance of the hashtag individually to a particular group of followers of a user.

4. The system of claim 1 , wherein the trend monitoring unit analyzes the source of the trend, evaluates the reliability or influence of the trend, and makes a proposal.

5. The system of claim 1 , wherein the schedule manager analyzes follower activity patterns and schedules posts to coincide with times when the follower is most active.

6. The system according to claim 1 , wherein the hashtag suggestion unit analyzes the sentiment of the posted content and suggests hashtags that are appropriate to the sentiment.

7. The system according to claim 1 , wherein the popularity display unit analyzes users' emotional reactions to the popularity and relevance of the hashtags and displays hashtags that are likely to evoke emotional empathy.

8. The system of claim 1 , wherein the trend monitoring unit analyzes a user's emotional response to the trend and suggests the trend that is emotionally positive.

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