system

Abstract

The system according to the embodiment aims to provide effective ideas for posting content that will create buzz on social media. [Solution] A system according to an embodiment includes a data collection unit, an analysis unit, and a generation unit. The data collection unit collects trend information or past buzz posts on social media. The analysis unit analyzes the data collected by the data collection unit to identify trends or patterns. The generation unit generates ideas for social media posts for users based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to find effective ideas for posts that would create buzz on social media.

[0005] The system according to the embodiment aims to provide effective ideas for posting content that will create buzz on social media. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a generation unit. The data collection unit collects trend information or past buzz posts on social media. The analysis unit analyzes the data collected by the data collection unit and identifies trends or patterns. The generation unit generates ideas for social media posts for users based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide effective ideas for posting content that will create buzz on social media. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An SNS post idea generation system according to an embodiment of the present invention provides users who use SNS as a source of income with ideas for posts that will create buzz. In this system, an AI first collects trend information and past buzz posts and stores them in a database. This database includes trend information and the content of buzz posts on SNS. A generation AI then analyzes the accumulated data and provides users with ideas for SNS posts. Based on the collected data, the generation AI generates post ideas that are likely to create buzz for users. Users then use the ideas provided by the generation AI to determine what content to post on SNS. For example, users can obtain ideas for interesting topics and questions, quizzes and challenge posts, inspiring short stories, trend-appropriate posts, user interactions, compelling quotes, and influencer collaborations. This service allows users who use SNS as a source of income to efficiently obtain ideas for posts that will create buzz and potentially increase their income. Furthermore, the ideas provided by the generation AI can diversify the content of users' posts and increase their presence on SNS. This allows the SNS post idea generation system to efficiently create posts that are likely to create buzz for users.

[0029] The SNS post idea generation system according to the embodiment includes a data collection unit, an analysis unit, and a generation unit. The data collection unit collects trend information or past buzz posts on SNS. The data collection unit can collect, for example, specific keywords, hashtags, trending topics, etc. The data collection unit can also collect posts that have received a large number of likes, shares, and comments on SNS. For example, the data collection unit collects posts containing specific keywords and stores them in a database as trend information. The data collection unit can also collect posts containing specific hashtags and store them in a database as buzz posts. The analysis unit analyzes the data collected by the data collection unit to identify trends or patterns. For example, the analysis unit can analyze the collected data using data mining technology to identify trends. The analysis unit can also identify data patterns using statistical analysis. For example, the analysis unit can analyze the frequency of appearance of specific keywords or hashtags based on the collected data to identify trends. The analysis unit can also analyze the data using a machine learning algorithm to identify buzz post patterns. The generation unit generates ideas for posting to social media for the user based on the analysis results obtained by the analysis unit. The generation unit can generate posting ideas using, for example, a template-based generation method. The generation unit can also generate posting ideas using automatic generation using AI. For example, the generation unit generates posting ideas that are likely to go viral based on the analysis results. The generation unit also has an interface for providing the generated posting ideas to the user. For example, the generation unit provides the posting ideas to the user through a web application or a mobile application. This allows the SNS posting idea generation system according to the embodiment to efficiently create posts that are likely to go viral.

[0030] The data collection unit can collect trend information for specific time periods on social media and analyze trends in buzz posts for each time period. For example, the data collection unit can collect trend information for the morning commute and analyze trends in business-related buzz posts. For example, the data collection unit can collect posts containing business-related keywords and hashtags during the morning commute and analyze trends in buzz posts. The data collection unit can also collect trend information for the lunch break and analyze trends in buzz posts related to lunch and restaurants. For example, the data collection unit can collect posts containing lunch- and restaurant-related keywords and hashtags during the lunch break and analyze trends in buzz posts. The data collection unit can also collect trend information for evening relaxation and analyze trends in entertainment- and hobby-related buzz posts. For example, the data collection unit can collect posts containing entertainment- and hobby-related keywords and hashtags during evening relaxation and analyze trends in buzz posts. By collecting trend information for specific time periods and analyzing trends in buzz posts for each time period, effective posting can be performed according to the time period. Some or all of the above-described processing by the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input trend information for a specific time period into the generation AI and have the generation AI analyze the trends in buzz posts for each time period.

[0031] The data collection unit can collect trend information in a specific region or cultural sphere and analyze the trends in buzz posts for each region. For example, the data collection unit can collect trend information for Japan and analyze the trends in buzz posts for each region. For example, the data collection unit can collect posts containing keywords and hashtags in a specific region in Japan and analyze the trends in buzz posts. The data collection unit can also collect trend information for the United States and analyze the trends in buzz posts for each region. For example, the data collection unit can collect posts containing keywords and hashtags in a specific region in the United States and analyze the trends in buzz posts. The data collection unit can also collect trend information for European countries and analyze the trends in buzz posts for each region. For example, the data collection unit can collect posts containing keywords and hashtags in a specific region in European countries and analyze the trends in buzz posts. By collecting trend information for a specific region or cultural sphere and analyzing the trends in buzz posts for each region, effective posting can be performed according to the region. Some or all of the above-described processing by the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input trend information from a specific region or cultural area into the generation AI and have the generation AI analyze the trends in buzz posts by region.

[0032] The data collection unit can collect trend information related to specific hashtags on social media and analyze trends in buzz posts for each hashtag. The data collection unit, for example, collects trend information related to #fitness and analyzes trends in fitness-related buzz posts. For example, the data collection unit collects posts containing keywords and hashtags related to #fitness and analyzes trends in buzz posts. The data collection unit can also collect trend information related to #food and analyze trends in food-related buzz posts. For example, the data collection unit collects posts containing keywords and hashtags related to #food and analyzes trends in buzz posts. The data collection unit can also collect trend information related to #travel and analyze trends in travel-related buzz posts. For example, the data collection unit collects posts containing keywords and hashtags related to #travel and analyzes trends in buzz posts. By collecting trend information related to specific hashtags and analyzing trends in buzz posts for each hashtag, effective posting according to the hashtag is possible. Some or all of the above-described processing by the data collection unit may be performed, for example, using AI or without AI. For example, the data collection unit can input trend information related to a specific hashtag into the generation AI and have the generation AI perform an analysis of buzz post trends for each hashtag.

[0033] The data collection unit can collect posts from specific influencers on social media and analyze trends in buzz posts for each influencer. For example, the data collection unit can collect posts from a specific fashion influencer and analyze trends in fashion-related buzz posts. For example, the data collection unit can collect posts containing keywords and hashtags of a specific fashion influencer and analyze trends in buzz posts. The data collection unit can also collect posts from a specific fitness influencer and analyze trends in fitness-related buzz posts. For example, the data collection unit can collect posts containing keywords and hashtags of a specific fitness influencer and analyze trends in buzz posts. The data collection unit can also collect posts from a specific food influencer and analyze trends in food-related buzz posts. For example, the data collection unit can collect posts containing keywords and hashtags of a specific food influencer and analyze trends in buzz posts. In this way, by collecting posts from a specific influencer and analyzing trends in buzz posts for each influencer, it is possible to create effective posts tailored to each influencer. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input posts from a specific influencer into the generation AI and cause the generation AI to analyze trends in buzz posts for each influencer.

[0034] The analysis unit can analyze the collected data along a time axis and identify changes in trends. The analysis unit can analyze data from the past year, for example, and identify changes in seasonal trends. For example, the analysis unit can analyze the frequency of appearance of seasonal keywords and hashtags based on the data from the past year and identify changes in trends. The analysis unit can also analyze data from the past month and identify changes in weekly trends. For example, the analysis unit can analyze the frequency of appearance of keywords and hashtags based on the data from the past month and identify changes in trends. The analysis unit can also analyze data from the past week and identify changes in daily trends. For example, the analysis unit can analyze the frequency of appearance of keywords and hashtags based on the data from the past week and identify changes in trends. In this way, changes in trends can be identified by analyzing the collected data along a time axis. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI analyze the changes in trends over time.

[0035] The analysis unit can analyze the collected data by category and identify patterns of buzz posts in each category. The analysis unit can, for example, analyze data in the fashion category and identify patterns of buzz posts. For example, the analysis unit can collect posts containing keywords and hashtags in the fashion category and identify patterns of buzz posts. The analysis unit can also analyze data in the fitness category and identify patterns of buzz posts. For example, the analysis unit can collect posts containing keywords and hashtags in the fitness category and identify patterns of buzz posts. The analysis unit can also analyze data in the food category and identify patterns of buzz posts. For example, the analysis unit can collect posts containing keywords and hashtags in the food category and identify patterns of buzz posts. In this way, by analyzing the collected data by category, patterns of buzz posts in each category can be identified. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to analyze patterns of buzz posts for each category.

[0036] The analysis unit analyzes the collected data for each SNS platform and can identify buzz posting patterns for each platform. The analysis unit, for example, analyzes data from X (formerly Twitter (registered trademark)) and can identify buzz posting patterns. For example, the analysis unit collects posts containing keywords and hashtags from X (formerly Twitter) and can identify buzz posting patterns. The analysis unit can also analyze data from Instagram (registered trademark) and can identify buzz posting patterns. For example, the analysis unit collects posts containing Instagram keywords and hashtags and can identify buzz posting patterns. The analysis unit can also analyze data from Facebook (registered trademark) and can identify buzz posting patterns. For example, the analysis unit collects posts containing Facebook keywords and hashtags and can identify buzz posting patterns. By analyzing the collected data for each SNS platform, it is possible to identify buzz posting patterns for each platform. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI analyze the patterns of buzz posts for each platform.

[0037] The analysis unit can analyze the collected data for each posting format (text, image, video, etc.) and identify buzz posting patterns for each format. The analysis unit, for example, analyzes text posting data and identifies buzz posting patterns. For example, the analysis unit collects posts containing text posting keywords and hashtags and identifies buzz posting patterns. The analysis unit can also analyze image posting data and identify buzz posting patterns. For example, the analysis unit collects image posting keywords and hashtags and identifies buzz posting patterns. The analysis unit can also analyze video posting data and identify buzz posting patterns. For example, the analysis unit collects video posting keywords and hashtags and identifies buzz posting patterns. In this way, by analyzing the collected data for each posting format, it is possible to identify buzz posting patterns for each format. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to analyze buzz posting patterns for each posting format.

[0038] The generation unit can generate ideas for posting during specific time periods based on the analysis results. The generation unit, for example, generates business-related ideas for posting during the morning commute. For example, the generation unit generates posting ideas including business-related keywords and hashtags for posting during the morning commute based on the analysis results. The generation unit can also generate lunch- and restaurant-related ideas for posting during lunch breaks. For example, the generation unit generates posting ideas including lunch- and restaurant-related keywords and hashtags for posting during lunch breaks based on the analysis results. The generation unit can also generate entertainment- and hobby-related ideas for posting during evening relaxation. For example, the generation unit generates posting ideas including entertainment- and hobby-related keywords and hashtags for posting during evening relaxation based on the analysis results. This enables effective posting according to the time period by generating ideas for posting during specific time periods based on the analysis results. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate ideas for posting during specific time periods.

[0039] The generation unit can generate posting ideas for a specific region or cultural area based on the analysis results. The generation unit generates posting ideas for Japan, for example. For example, the generation unit generates posting ideas including keywords and hashtags for Japan based on the analysis results. The generation unit can also generate posting ideas for the United States. For example, the generation unit generates posting ideas including keywords and hashtags for the United States based on the analysis results. The generation unit can also generate posting ideas for European countries. For example, the generation unit generates posting ideas including keywords and hashtags for European countries based on the analysis results. This enables effective posting tailored to the region or culture by generating posting ideas for a specific region or cultural area based on the analysis results. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results to a generation AI and cause the generation AI to generate posting ideas for a specific region or cultural area.

[0040] The generation unit can generate posting ideas including specific hashtags based on the analysis results. The generation unit generates posting ideas including, for example, #fitness. For example, the generation unit generates posting ideas including keywords and hashtags including #fitness based on the analysis results. The generation unit can also generate posting ideas including #food. For example, the generation unit generates posting ideas including keywords and hashtags including #food based on the analysis results. The generation unit can also generate posting ideas including #travel. For example, the generation unit generates posting ideas including keywords and hashtags including #travel based on the analysis results. In this way, by generating posting ideas including specific hashtags based on the analysis results, effective posts according to the hashtags can be made. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results to a generation AI and cause the generation AI to generate posting ideas including specific hashtags.

[0041] The generation unit can generate post ideas proposing collaboration with specific influencers based on the analysis results. The generation unit generates, for example, post ideas proposing collaboration with fashion influencers. For example, the generation unit generates post ideas including keywords and hashtags proposing collaboration with fashion influencers based on the analysis results. The generation unit can also generate post ideas proposing collaboration with fitness influencers. For example, the generation unit generates post ideas including keywords and hashtags proposing collaboration with fitness influencers based on the analysis results. The generation unit can also generate post ideas proposing collaboration with food influencers. For example, the generation unit generates post ideas including keywords and hashtags proposing collaboration with food influencers based on the analysis results. This enables effective collaboration with influencers by generating post ideas proposing collaboration with specific influencers based on the analysis results. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate post ideas proposing collaboration with specific influencers.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The SNS posting idea generation system may further include a history analysis unit that analyzes a user's past posting history and identifies the user's posting style and preferences. The history analysis unit, for example, analyzes the content and frequency of the user's past posts and the characteristics of posts that received good responses, to identify the user's posting style and preferences. This allows the generation unit to generate more personalized posting ideas based on the user's past posting style and preferences. For example, the system may identify patterns in posts that have received many responses from the user in the past and provide new posting ideas based on those patterns. Furthermore, if a user is interested in a particular theme or topic, the system may preferentially generate posting ideas related to that theme or topic. This allows users to obtain posting ideas that match their own style and preferences, further enhancing their presence on the SNS.

[0044] The data collection unit can acquire the user's real-time location information and select trend information to collect based on the location information. For example, if the user is at a specific event venue, trend information related to that event can be collected preferentially. Also, if the user is traveling, trend information related to the area where the user is traveling can be collected. This makes it possible to provide appropriate trend information according to the user's current situation. Furthermore, based on the location information, past buzz posts related to places the user has visited can be collected and provided to the user. This allows the user to get posting ideas that suit their current situation and improve the content of their posts on SNS.

[0045] The data collection unit can collect trend information for specific communities or groups on SNS and analyze the trends in buzz posts for each community. For example, it can collect trend information for communities with specific hobbies or interests and analyze the trends in buzz posts within those communities. It can also collect trend information for groups related to specific occupations or industries and analyze the trends in buzz posts within those groups. This allows for effective posting tailored to each community by collecting trend information for specific communities or groups and analyzing the trends in buzz posts for each community. Some or all of the above-mentioned processing by the data collection unit can be performed, for example, using AI, or without AI.

[0046] The analysis unit can analyze the collected data along a time axis and identify changes in trends. For example, it can analyze data from the past year and identify changes in seasonal trends. It can also analyze data from the past month and identify changes in weekly trends. It can also analyze data from the past week and identify changes in daily trends. In this way, by analyzing the collected data along a time axis, it is possible to identify changes in trends. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The analysis unit can analyze the collected data by category and identify patterns of buzz posts in each category. For example, it can analyze data in the fashion category and identify patterns of buzz posts. It can also analyze data in the fitness category and identify patterns of buzz posts. It can also analyze data in the food category and identify patterns of buzz posts. In this way, by analyzing the collected data by category, it is possible to identify patterns of buzz posts in each category. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0048] The generation unit can generate ideas for posting during specific time periods based on the analysis results. For example, it can generate business-related ideas for posting during the morning commute. It can also generate lunch- and restaurant-related ideas for posting during lunch breaks. It can also generate entertainment- and hobby-related ideas for posting during evening relaxation. In this way, by generating ideas for posting during specific time periods based on the analysis results, it is possible to post effectively according to the time period. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The data collection unit collects trending information or past buzz posts on social media. For example, posts are collected based on specific keywords, hashtags, trending topics, number of likes, shares, and comments, and are stored in a database as trending information or buzz posts. Step 2: The analysis unit analyzes the data collected by the data collection unit and identifies trends or patterns. For example, using data mining techniques, statistical analysis, and machine learning algorithms, the analysis unit analyzes the frequency of occurrence of specific keywords and hashtags and identifies patterns of trending and buzz posts. Step 3: The generation unit generates ideas for social media posts for users based on the analysis results obtained by the analysis unit. For example, using a template-based generation method or automatic generation using AI, the unit generates ideas for posts that are likely to create buzz for users and provides them via a web application or mobile application.

[0051] (Example 2) An SNS post idea generation system according to an embodiment of the present invention provides users who use SNS as a source of income with ideas for posts that will create buzz. In this system, an AI first collects trend information and past buzz posts and stores them in a database. This database includes trend information and the content of buzz posts on SNS. A generation AI then analyzes the accumulated data and provides users with ideas for SNS posts. Based on the collected data, the generation AI generates post ideas that are likely to create buzz for users. Users then use the ideas provided by the generation AI to determine what content to post on SNS. For example, users can obtain ideas for interesting topics and questions, quizzes and challenge posts, inspiring short stories, trend-appropriate posts, user interactions, compelling quotes, and influencer collaborations. This service allows users who use SNS as a source of income to efficiently obtain ideas for posts that will create buzz and potentially increase their income. Furthermore, the ideas provided by the generation AI can diversify the content of users' posts and increase their presence on SNS. This allows the SNS post idea generation system to efficiently create posts that are likely to create buzz for users.

[0052] The SNS post idea generation system according to the embodiment includes a data collection unit, an analysis unit, and a generation unit. The data collection unit collects trend information or past buzz posts on SNS. The data collection unit can collect, for example, specific keywords, hashtags, trending topics, etc. The data collection unit can also collect posts that have received a large number of likes, shares, and comments on SNS. For example, the data collection unit collects posts containing specific keywords and stores them in a database as trend information. The data collection unit can also collect posts containing specific hashtags and store them in a database as buzz posts. The analysis unit analyzes the data collected by the data collection unit to identify trends or patterns. For example, the analysis unit can analyze the collected data using data mining technology to identify trends. The analysis unit can also identify data patterns using statistical analysis. For example, the analysis unit can analyze the frequency of appearance of specific keywords or hashtags based on the collected data to identify trends. The analysis unit can also analyze the data using a machine learning algorithm to identify buzz post patterns. The generation unit generates ideas for posting to social media for the user based on the analysis results obtained by the analysis unit. The generation unit can generate posting ideas using, for example, a template-based generation method. The generation unit can also generate posting ideas using automatic generation using AI. For example, the generation unit generates posting ideas that are likely to go viral based on the analysis results. The generation unit also has an interface for providing the generated posting ideas to the user. For example, the generation unit provides the posting ideas to the user through a web application or a mobile application. This allows the SNS posting idea generation system according to the embodiment to efficiently create posts that are likely to go viral.

[0053] The data collection unit can estimate the user's emotions and determine the priority of trend information to be collected based on the estimated user emotions. For example, if the user is excited, the data collection unit prioritizes collecting entertainment- and sports-related trend information. For example, if the data collection unit analyzes the user's emotions and determines that the user is excited, it prioritizes collecting posts containing entertainment- and sports-related keywords and hashtags. Furthermore, if the user is relaxed, the data collection unit can also prioritize collecting trend information related to lifestyle and health. For example, if the data collection unit analyzes the user's emotions and determines that the user is relaxed, it prioritizes collecting posts containing lifestyle- and health-related keywords and hashtags. Furthermore, if the user is stressed, the data collection unit can also prioritize collecting trend information related to healing and relaxation. For example, if the data collection unit analyzes the user's emotions and determines that the user is stressed, it prioritizes collecting posts containing healing and relaxation-related keywords and hashtags. This allows for more appropriate trend information to be collected by prioritizing trend information based on the user's emotions. Emotion estimation can be achieved, for example, using an emotion analysis algorithm or natural language processing technology. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0054] The data collection unit can collect trend information for specific time periods on social media and analyze trends in buzz posts for each time period. For example, the data collection unit can collect trend information for the morning commute and analyze trends in business-related buzz posts. For example, the data collection unit can collect posts containing business-related keywords and hashtags during the morning commute and analyze trends in buzz posts. The data collection unit can also collect trend information for the lunch break and analyze trends in buzz posts related to lunch and restaurants. For example, the data collection unit can collect posts containing lunch- and restaurant-related keywords and hashtags during the lunch break and analyze trends in buzz posts. The data collection unit can also collect trend information for evening relaxation and analyze trends in entertainment- and hobby-related buzz posts. For example, the data collection unit can collect posts containing entertainment- and hobby-related keywords and hashtags during evening relaxation and analyze trends in buzz posts. By collecting trend information for specific time periods and analyzing trends in buzz posts for each time period, effective posting can be performed according to the time period. Some or all of the above-described processing by the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input trend information for a specific time period into the generation AI and have the generation AI analyze the trends in buzz posts for each time period.

[0055] The data collection unit can collect trend information in a specific region or cultural sphere and analyze the trends in buzz posts for each region. For example, the data collection unit can collect trend information for Japan and analyze the trends in buzz posts for each region. For example, the data collection unit can collect posts containing keywords and hashtags in a specific region in Japan and analyze the trends in buzz posts. The data collection unit can also collect trend information for the United States and analyze the trends in buzz posts for each region. For example, the data collection unit can collect posts containing keywords and hashtags in a specific region in the United States and analyze the trends in buzz posts. The data collection unit can also collect trend information for European countries and analyze the trends in buzz posts for each region. For example, the data collection unit can collect posts containing keywords and hashtags in a specific region in European countries and analyze the trends in buzz posts. By collecting trend information for a specific region or cultural sphere and analyzing the trends in buzz posts for each region, effective posting can be performed according to the region. Some or all of the above-described processing by the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input trend information from a specific region or cultural area into the generation AI and have the generation AI analyze the trends in buzz posts by region.

[0056] The data collection unit can estimate the user's emotions and select the type of buzz post to collect based on the estimated user's emotions. For example, if the user is excited, the data collection unit prioritizes collecting entertainment- and sports-related buzz posts. For example, if the data collection unit analyzes the user's emotions and determines that the user is excited, it prioritizes collecting posts including entertainment- and sports-related keywords and hashtags. Furthermore, if the user is relaxed, the data collection unit can also prioritize collecting buzz posts related to lifestyle and health. For example, if the data collection unit analyzes the user's emotions and determines that the user is relaxed, it prioritizes collecting posts including lifestyle- and health-related keywords and hashtags. Furthermore, if the user is stressed, the data collection unit can also prioritize collecting buzz posts related to healing and relaxation. For example, if the data collection unit analyzes the user's emotions and determines that the user is stressed, it prioritizes collecting posts including healing and relaxation-related keywords and hashtags. This allows for more appropriate buzz post collection by selecting the type of buzz post based on the user's emotions. Emotion estimation is realized, for example, using an emotion analysis algorithm or natural language processing technology. Some or all of the above-mentioned processing in the data collection unit may be performed, for example, using AI, or may be performed without using AI. For example, the data collection unit may input user emotion data into the generation AI and have the generation AI perform emotion estimation.

[0057] The data collection unit can collect trend information related to specific hashtags on social media and analyze trends in buzz posts for each hashtag. The data collection unit, for example, collects trend information related to #fitness and analyzes trends in fitness-related buzz posts. For example, the data collection unit collects posts containing keywords and hashtags related to #fitness and analyzes trends in buzz posts. The data collection unit can also collect trend information related to #food and analyze trends in food-related buzz posts. For example, the data collection unit collects posts containing keywords and hashtags related to #food and analyzes trends in buzz posts. The data collection unit can also collect trend information related to #travel and analyze trends in travel-related buzz posts. For example, the data collection unit collects posts containing keywords and hashtags related to #travel and analyzes trends in buzz posts. By collecting trend information related to specific hashtags and analyzing trends in buzz posts for each hashtag, effective posting according to the hashtag is possible. Some or all of the above-described processing by the data collection unit may be performed, for example, using AI or without AI. For example, the data collection unit can input trend information related to a specific hashtag into the generation AI and have the generation AI perform an analysis of buzz post trends for each hashtag.

[0058] The data collection unit can collect posts from specific influencers on social media and analyze trends in buzz posts for each influencer. For example, the data collection unit can collect posts from a specific fashion influencer and analyze trends in fashion-related buzz posts. For example, the data collection unit can collect posts containing keywords and hashtags of a specific fashion influencer and analyze trends in buzz posts. The data collection unit can also collect posts from a specific fitness influencer and analyze trends in fitness-related buzz posts. For example, the data collection unit can collect posts containing keywords and hashtags of a specific fitness influencer and analyze trends in buzz posts. The data collection unit can also collect posts from a specific food influencer and analyze trends in food-related buzz posts. For example, the data collection unit can collect posts containing keywords and hashtags of a specific food influencer and analyze trends in buzz posts. In this way, by collecting posts from a specific influencer and analyzing trends in buzz posts for each influencer, it is possible to create effective posts tailored to each influencer. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input posts from a specific influencer into the generation AI and cause the generation AI to analyze trends in buzz posts for each influencer.

[0059] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. For example, if the analysis unit analyzes the user's emotions and determines that the user is nervous, it provides a display method using a simple, highly visible layout and color scheme. Furthermore, if the user is relaxed, the analysis unit can also provide a display method including detailed information. For example, if the analysis unit analyzes the user's emotions and determines that the user is relaxed, it provides a display method using a layout and color scheme including detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, if the analysis unit analyzes the user's emotions and determines that the user is in a hurry, it provides a display method using a layout and color scheme that focuses on the main points. This allows the display method of the analysis results to be adjusted based on the user's emotions, resulting in a display that is easy for the user to view. The emotion estimation is achieved using, for example, an emotion analysis algorithm or natural language processing technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI perform emotion estimation.

[0060] The analysis unit can analyze the collected data along a time axis and identify changes in trends. The analysis unit can analyze data from the past year, for example, and identify changes in seasonal trends. For example, the analysis unit can analyze the frequency of appearance of seasonal keywords and hashtags based on the data from the past year and identify changes in trends. The analysis unit can also analyze data from the past month and identify changes in weekly trends. For example, the analysis unit can analyze the frequency of appearance of keywords and hashtags based on the data from the past month and identify changes in trends. The analysis unit can also analyze data from the past week and identify changes in daily trends. For example, the analysis unit can analyze the frequency of appearance of keywords and hashtags based on the data from the past week and identify changes in trends. In this way, changes in trends can be identified by analyzing the collected data along a time axis. Some or all of the above-mentioned processing by the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input collected data into the generation AI and have the generation AI analyze the changes in trends over time.

[0061] The analysis unit can analyze the collected data by category and identify patterns of buzz posts in each category. The analysis unit can, for example, analyze data in the fashion category and identify patterns of buzz posts. For example, the analysis unit can collect posts containing keywords and hashtags in the fashion category and identify patterns of buzz posts. The analysis unit can also analyze data in the fitness category and identify patterns of buzz posts. For example, the analysis unit can collect posts containing keywords and hashtags in the fitness category and identify patterns of buzz posts. The analysis unit can also analyze data in the food category and identify patterns of buzz posts. For example, the analysis unit can collect posts containing keywords and hashtags in the food category and identify patterns of buzz posts. In this way, by analyzing the collected data by category, patterns of buzz posts in each category can be identified. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to analyze patterns of buzz posts for each category.

[0062] The analysis unit can estimate the user's emotions and adjust the importance of analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit prioritizes analysis results related to entertainment and sports. For example, if the analysis unit analyzes the user's emotions and determines that the user is excited, it prioritizes analysis results including keywords and hashtags related to entertainment and sports. Furthermore, if the user is relaxed, the analysis unit can also prioritize analysis results related to lifestyle and health. For example, if the analysis unit analyzes the user's emotions and determines that the user is relaxed, it prioritizes analysis results including keywords and hashtags related to lifestyle and health. Furthermore, if the user is stressed, the analysis unit can also prioritize analysis results related to healing and relaxation. For example, if the analysis unit analyzes the user's emotions and determines that the user is stressed, it prioritizes analysis results including keywords and hashtags related to healing and relaxation. In this way, by adjusting the importance of analysis results based on the user's emotions, information important to the user can be displayed preferentially. Emotion estimation is achieved, for example, using an emotion analysis algorithm or natural language processing technology. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0063] The analysis unit can analyze the collected data for each SNS platform and identify buzz posting patterns for each platform. The analysis unit, for example, analyzes data from X (formerly Twitter) and identifies buzz posting patterns. For example, the analysis unit collects posts containing keywords and hashtags for X (formerly Twitter) and identifies buzz posting patterns. The analysis unit can also analyze data from Instagram and identify buzz posting patterns. For example, the analysis unit collects posts containing Instagram keywords and hashtags and identifies buzz posting patterns. The analysis unit can also analyze data from Facebook and identify buzz posting patterns. For example, the analysis unit collects posts containing Facebook keywords and hashtags and identifies buzz posting patterns. In this way, by analyzing the collected data for each SNS platform, buzz posting patterns for each platform can be identified. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to analyze buzz posting patterns for each platform.

[0064] The analysis unit can analyze the collected data for each posting format (text, image, video, etc.) and identify buzz posting patterns for each format. The analysis unit, for example, analyzes text posting data and identifies buzz posting patterns. For example, the analysis unit collects posts containing text posting keywords and hashtags and identifies buzz posting patterns. The analysis unit can also analyze image posting data and identify buzz posting patterns. For example, the analysis unit collects image posting keywords and hashtags and identifies buzz posting patterns. The analysis unit can also analyze video posting data and identify buzz posting patterns. For example, the analysis unit collects video posting keywords and hashtags and identifies buzz posting patterns. In this way, by analyzing the collected data for each posting format, it is possible to identify buzz posting patterns for each format. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and cause the generation AI to analyze buzz posting patterns for each posting format.

[0065] The generation unit can estimate the user's emotions and adjust the expression method of the generated idea to post based on the estimated user's emotions. For example, if the user is relaxed, the generation unit generates an idea to post that is expressed in a relaxed tone. For example, if the generation unit analyzes the user's emotions and determines that the user is relaxed, it generates an idea to post that is expressed in a relaxed tone. Furthermore, if the user is excited, the generation unit can generate an idea to post that is expressed in an energetic tone. For example, if the generation unit analyzes the user's emotions and determines that the user is excited, it generates an idea to post that is expressed in an energetic tone. Furthermore, if the user is feeling stressed, the generation unit can generate an idea to post that has a theme of healing or relaxation. For example, if the generation unit analyzes the user's emotions and determines that the user is feeling stressed, it generates an idea to post that has a theme of healing or relaxation. In this way, by adjusting the expression method of the idea to post based on the user's emotions, it is possible to provide an idea to post that is expressed in an appropriate way for the user. Emotion estimation is achieved using, for example, an emotion analysis algorithm or natural language processing technology. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data to the generation AI and have the generation AI estimate the emotion.

[0066] The generation unit can generate ideas for posting during specific time periods based on the analysis results. The generation unit, for example, generates business-related ideas for posting during the morning commute. For example, the generation unit generates posting ideas including business-related keywords and hashtags for posting during the morning commute based on the analysis results. The generation unit can also generate lunch- and restaurant-related ideas for posting during lunch breaks. For example, the generation unit generates posting ideas including lunch- and restaurant-related keywords and hashtags for posting during lunch breaks based on the analysis results. The generation unit can also generate entertainment- and hobby-related ideas for posting during evening relaxation. For example, the generation unit generates posting ideas including entertainment- and hobby-related keywords and hashtags for posting during evening relaxation based on the analysis results. This enables effective posting according to the time period by generating ideas for posting during specific time periods based on the analysis results. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate ideas for posting during specific time periods.

[0067] The generation unit can generate posting ideas for a specific region or cultural area based on the analysis results. The generation unit generates posting ideas for Japan, for example. For example, the generation unit generates posting ideas including keywords and hashtags for Japan based on the analysis results. The generation unit can also generate posting ideas for the United States. For example, the generation unit generates posting ideas including keywords and hashtags for the United States based on the analysis results. The generation unit can also generate posting ideas for European countries. For example, the generation unit generates posting ideas including keywords and hashtags for European countries based on the analysis results. This enables effective posting tailored to the region or culture by generating posting ideas for a specific region or cultural area based on the analysis results. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results to a generation AI and cause the generation AI to generate posting ideas for a specific region or cultural area.

[0068] The generation unit can estimate the user's emotions and determine the priority of the generated posting ideas based on the estimated user emotions. For example, if the user is excited, the generation unit prioritizes generating posting ideas related to entertainment and sports. For example, if the generation unit analyzes the user's emotions and determines that the user is excited, it prioritizes generating posting ideas including entertainment and sports-related keywords and hashtags. Furthermore, if the user is relaxed, the generation unit can also prioritize generating posting ideas related to lifestyle and health. For example, if the generation unit analyzes the user's emotions and determines that the user is relaxed, it prioritizes generating posting ideas including lifestyle and health-related keywords and hashtags. Furthermore, if the user is stressed, the generation unit can also prioritize generating posting ideas related to healing and relaxation. For example, if the generation unit analyzes the user's emotions and determines that the user is stressed, it prioritizes generating posting ideas including healing and relaxation-related keywords and hashtags. In this way, by determining the priority of posting ideas based on the user's emotions, it is possible to provide posting ideas appropriate for the user preferentially. Emotion estimation is realized using, for example, an emotion analysis algorithm or natural language processing technology. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input user emotion data into the generation AI and cause the generation AI to estimate the emotion.

[0069] The generation unit can generate posting ideas including specific hashtags based on the analysis results. The generation unit generates posting ideas including, for example, #fitness. For example, the generation unit generates posting ideas including keywords and hashtags including #fitness based on the analysis results. The generation unit can also generate posting ideas including #food. For example, the generation unit generates posting ideas including keywords and hashtags including #food based on the analysis results. The generation unit can also generate posting ideas including #travel. For example, the generation unit generates posting ideas including keywords and hashtags including #travel based on the analysis results. In this way, by generating posting ideas including specific hashtags based on the analysis results, effective posts according to the hashtags can be made. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results to a generation AI and cause the generation AI to generate posting ideas including specific hashtags.

[0070] The generation unit can generate post ideas proposing collaboration with specific influencers based on the analysis results. The generation unit generates, for example, post ideas proposing collaboration with fashion influencers. For example, the generation unit generates post ideas including keywords and hashtags proposing collaboration with fashion influencers based on the analysis results. The generation unit can also generate post ideas proposing collaboration with fitness influencers. For example, the generation unit generates post ideas including keywords and hashtags proposing collaboration with fitness influencers based on the analysis results. The generation unit can also generate post ideas proposing collaboration with food influencers. For example, the generation unit generates post ideas including keywords and hashtags proposing collaboration with food influencers based on the analysis results. This enables effective collaboration with influencers by generating post ideas proposing collaboration with specific influencers based on the analysis results. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the analysis results into a generation AI and cause the generation AI to generate post ideas proposing collaboration with specific influencers. === Hard Collateral 1-1 === Each of the multiple elements including the data collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is realized by the control unit 46A of the smart device 14 and collects trend information and past buzz posts on SNS. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify trends and patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates ideas for SNS posts for the user based on the analysis results. The generation unit is also realized, for example, by the control unit 46A of the smart device 14 and has an interface for providing the generated posting ideas to the user. === Hard Collateral 1-2 === Each of the multiple elements including the data collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is realized by the control unit 46A of the smart glasses 214 and collects trend information and past buzz posts on SNS. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify trends and patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates ideas for SNS posts for the user based on the analysis results. The generation unit is also realized, for example, by the control unit 46A of the smart glasses 214 and includes an interface for providing the generated posting ideas to the user. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the data collection unit is realized by the control unit 46A of the headset type terminal 314 and collects trend information and past buzz posts on SNS. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify trends and patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates ideas for SNS posts for the user based on the analysis results. The generation unit is also realized, for example, by the control unit 46A of the headset type terminal 314 and has an interface for providing the generated posting ideas to the user. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, and generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit is realized by the control unit 46A of the robot 414 and collects trend information and past buzz posts on SNS. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to identify trends and patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates ideas for SNS posts for the user based on the analysis results. The generation unit is also realized, for example, by the control unit 46A of the robot 414 and includes an interface for providing the generated posting ideas to the user.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The SNS posting idea generation system may further include a history analysis unit that analyzes a user's past posting history and identifies the user's posting style and preferences. The history analysis unit, for example, analyzes the content and frequency of the user's past posts and the characteristics of posts that received good responses, to identify the user's posting style and preferences. This allows the generation unit to generate more personalized posting ideas based on the user's past posting style and preferences. For example, the system may identify patterns in posts that have received many responses from the user in the past and provide new posting ideas based on those patterns. Furthermore, if a user is interested in a particular theme or topic, the system may preferentially generate posting ideas related to that theme or topic. This allows users to obtain posting ideas that match their own style and preferences, further enhancing their presence on the SNS.

[0073] The data collection unit can acquire the user's real-time location information and select trend information to collect based on the location information. For example, if the user is at a specific event venue, trend information related to that event can be collected preferentially. Also, if the user is traveling, trend information related to the area where the user is traveling can be collected. This makes it possible to provide appropriate trend information according to the user's current situation. Furthermore, based on the location information, past buzz posts related to places the user has visited can be collected and provided to the user. This allows the user to get posting ideas that suit their current situation and improve the content of their posts on SNS.

[0074] The data collection unit can estimate the user's emotions and select the type of trend information to collect based on the estimated user emotions. For example, if the user is excited, entertainment and sports-related trend information can be collected preferentially. If the user is relaxed, trend information related to lifestyle and health can be collected preferentially. Furthermore, if the user is stressed, trend information related to healing and relaxation can be collected preferentially. By selecting the type of trend information based on the user's emotions, more appropriate trend information can be collected. Emotion estimation is achieved using, for example, an emotion analysis algorithm or natural language processing technology. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or without AI.

[0075] The data collection unit can collect trend information for specific communities or groups on SNS and analyze the trends in buzz posts for each community. For example, it can collect trend information for communities with specific hobbies or interests and analyze the trends in buzz posts within those communities. It can also collect trend information for groups related to specific occupations or industries and analyze the trends in buzz posts within those groups. This allows for effective posting tailored to each community by collecting trend information for specific communities or groups and analyzing the trends in buzz posts for each community. Some or all of the above-mentioned processing by the data collection unit can be performed, for example, using AI, or without AI.

[0076] The data collection unit can estimate the user's emotions and select the type of buzz post to collect based on the estimated user emotions. For example, if the user is excited, entertainment- and sports-related buzz posts can be preferentially collected. Also, if the user is relaxed, lifestyle and health-related buzz posts can be preferentially collected. Furthermore, if the user is stressed, healing and relaxation-related buzz posts can be preferentially collected. By selecting the type of buzz post based on the user's emotions, more appropriate buzz posts can be collected. The emotion estimation can be achieved using, for example, a sentiment analysis algorithm or natural language processing technology. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI.

[0077] The analysis unit can analyze the collected data along a time axis and identify changes in trends. For example, it can analyze data from the past year and identify changes in seasonal trends. It can also analyze data from the past month and identify changes in weekly trends. It can also analyze data from the past week and identify changes in daily trends. In this way, by analyzing the collected data along a time axis, it is possible to identify changes in trends. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. By adjusting the display method of the analysis results based on the user's emotions, it is possible to provide a display that is easy for the user to view. Emotion estimation is achieved using, for example, an emotion analysis algorithm or natural language processing technology. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0079] The analysis unit can analyze the collected data by category and identify patterns of buzz posts in each category. For example, it can analyze data in the fashion category and identify patterns of buzz posts. It can also analyze data in the fitness category and identify patterns of buzz posts. It can also analyze data in the food category and identify patterns of buzz posts. In this way, by analyzing the collected data by category, it is possible to identify patterns of buzz posts in each category. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0080] The generation unit can generate ideas for posting during specific time periods based on the analysis results. For example, it can generate business-related ideas for posting during the morning commute. It can also generate lunch- and restaurant-related ideas for posting during lunch breaks. It can also generate entertainment- and hobby-related ideas for posting during evening relaxation. In this way, by generating ideas for posting during specific time periods based on the analysis results, it is possible to post effectively according to the time period. Some or all of the above-described processing by the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0081] The generation unit can estimate the user's emotions and adjust the expression method of the generated idea to post based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an idea to post that is expressed in a relaxed tone. If the user is excited, the generation unit can generate an idea to post that is expressed in an energetic tone. Furthermore, if the user is feeling stressed, the generation unit can generate an idea to post that has a healing or relaxation theme. In this way, by adjusting the expression method of the idea to post based on the user's emotions, it is possible to provide an idea to post that is expressed in an appropriate manner for the user. The estimation of emotions is realized using, for example, an emotion analysis algorithm or natural language processing technology. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The data collection unit collects trending information or past buzz posts on social media. For example, posts are collected based on specific keywords, hashtags, trending topics, number of likes, shares, and comments, and are stored in a database as trending information or buzz posts. Step 2: The analysis unit analyzes the data collected by the data collection unit and identifies trends or patterns. For example, using data mining techniques, statistical analysis, and machine learning algorithms, the analysis unit analyzes the frequency of occurrence of specific keywords and hashtags and identifies patterns of trending and buzz posts. Step 3: The generation unit generates ideas for social media posts for users based on the analysis results obtained by the analysis unit. For example, using a template-based generation method or automatic generation using AI, the unit generates ideas for posts that are likely to create buzz for users and provides them via a web application or mobile application.

[0084] 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0086] 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.

[0087] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0098] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0099] 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.

[0100] 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.

[0101] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0114] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0115] 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.

[0116] 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.

[0117] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

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

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

[0131] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0142] 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."

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects trending information and past buzz posts on social media; an analysis unit that analyzes the data collected by the data collection unit and identifies trends or patterns; a generation unit that generates ideas for posting on social media for users based on the analysis results obtained by the analysis unit. A system characterized by:

2. The data collection unit Estimate user sentiment and determine the priority of trend information to be collected based on the estimated user sentiment.

2. The system of claim 1.

3. The data collection unit Collect trend information on social media during specific time periods and analyze the trend of buzz posts during each time period.

2. The system of claim 1.

4. The data collection unit Collect trend information in specific regions or cultural areas and analyze buzz posting trends by region 2. The system of claim 1.

5. The data collection unit Estimate user sentiment and select the type of buzz posts to collect based on the estimated user sentiment.

2. The system of claim 1.

6. The data collection unit Collect trending information related to specific hashtags on social media and analyze the trend of buzz posts for each hashtag 2. The system of claim 1.

7. The data collection unit Collect posts from specific influencers on social media and analyze the trends of buzz posts by each influencer.

2. The system of claim 1.

8. The analysis unit Inferring user emotions and adjusting the display of analysis results based on the estimated user emotions 2. The system of claim 1.

Images