system

Abstract

An object of a system according to an embodiment is to optimize a posting timing and content of an SNS.SOLUTION: A system includes a data collection unit, an analysis unit, and a proposal unit. The data collection unit collects data related to an SNS account of a user. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes an optimal posting timing and content on the basis of a result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to optimize the timing and content of social media posts, and there is room for improvement in maximizing advertising effectiveness.

[0005] The system according to the embodiment aims to optimize the timing and content of posts on SNS. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data related to a user's SNS account. The analysis unit analyzes the data collected by the data collection unit. The suggestion unit suggests optimal posting timing and content based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can optimize the timing and content of posts on SNS. [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) The SNS manager system according to the embodiment of the present invention collects data related to users' SNS accounts, analyzes it using a generation AI, and suggests optimal posting timing and content. This allows the SNS manager system to streamline the management and operation of SNS and promote monetization.

[0029] An SNS manager system according to an embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data related to a user's SNS account. For example, the data collection unit uses an API to collect post content, follower count, and engagement data. The data collection unit can also collect data from the web using scraping technology. For example, the data collection unit uses the API of an SNS platform to obtain user post content and follower activity data. When using scraping technology, the data collection unit analyzes the HTML structure of a webpage and extracts necessary data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis to identify data trends. The analysis unit can also analyze the data and find patterns using a machine learning algorithm. For example, the analysis unit uses regression analysis to analyze the relationship between post timing and engagement. When using a machine learning algorithm, the analysis unit uses a clustering method to classify follower behavior patterns. The suggestion unit suggests optimal post timing and content based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests post timing based on followers' active time periods. The suggestion unit can also suggest post content that matches the interests of followers based on past engagement data. For example, the suggestion unit identifies the active time periods of followers and notifies the user to post during those times. Based on the past engagement data, the suggestion unit analyzes the post content that followers responded most to and suggests similar content. This allows the SNS manager system according to the embodiment to improve the efficiency of SNS management and operation and promote monetization. For example, the SNS manager system automatically optimizes the user's posting schedule and maximizes engagement. The user can post content that attracts the interest of followers based on the suggested post content. The SNS manager system supports user monetization and improves the effectiveness of SNS operation.

[0030] The data collection unit can collect real-time behavioral data of users. For example, the data collection unit collects location information from a user's smartphone in real time and suggests the optimal posting timing when the user is in a specific location. For example, if the user is at an event venue, the data collection unit notifies the user that posting on the spot would be effective. The data collection unit also analyzes the user's activity log and suggests the optimal posting timing based on specific behavioral patterns. For example, the data collection unit suggests that the user post a health-related post immediately after finishing a run. The data collection unit also works with the user's calendar app to suggest the optimal posting timing based on their schedule. For example, the data collection unit finds a time to attract followers' attention by posting before or after a meeting or event. This makes it possible to suggest the optimal posting timing based on the user's real-time behavioral data.

[0031] The analysis unit can analyze the behavioral patterns of a user's followers and suggest posting times that match the time periods when followers are most active. For example, the analysis unit's generation AI can analyze followers' login times and activity and suggest posting during the time periods when followers are most active. For example, it can recommend posting in the evening when many followers are logged in. The analysis unit can also analyze followers' past engagement data and suggest posting times that match the time periods when responses are highest. For example, it can identify the time periods when followers leave many likes and comments. The analysis unit can also enable the generation AI to take into account the region and time zone of followers and suggest posting during the time periods when followers in each region are most active. For example, it can create a posting schedule that matches followers in different time zones. This allows engagement to be maximized by posting during the time periods when followers are most active.

[0032] The suggestion unit can suggest the optimal posting timing and content for each different social media platform. For example, the generation AI in the suggestion unit analyzes the algorithms of each social media platform and suggests the optimal posting timing for each platform. For example, on Twitter, real-time is important, so posts are made during times when followers are most active. The suggestion unit also analyzes user engagement data for each platform and suggests the optimal posting content. For example, on Instagram, visual content is important, so high-quality images and videos are suggested. The generation AI in the suggestion unit also analyzes trends on each platform and suggests posting content that matches the trends. For example, on Facebook, it recommends posts related to currently popular news and events. This makes it possible to suggest the optimal posting timing and content for each different social media platform.

[0033] The suggestion unit can analyze a user's past advertising campaign data and suggest the most effective posting timing and content. For example, the generation AI analyzes the click rates and conversion rates of past advertising campaigns to suggest the most effective posting timing. For example, posting during a time period that recorded a high click rate in the past. The suggestion unit also analyzes the creative elements (images, text, videos) used in past advertising campaigns to suggest the most effective posting content. For example, reusing image styles that have generated high engagement. The suggestion unit also analyzes the target audience data of past advertising campaigns to suggest the optimal posting timing and content. For example, posting effectively for a specific demographic group. This allows the system to suggest the optimal posting timing and content based on past advertising campaign data.

[0034] The analysis unit analyzes the attribute data of followers of similar accounts and can find commonalities based on that data. For example, the analysis unit uses a generation AI to analyze the age data of followers of similar accounts and identify posts that are popular among specific age groups. For example, it finds topics and styles that are popular among younger people. The analysis unit also analyzes the gender data of followers and identifies posts that are highly rated by each gender. For example, it finds visual styles and themes that are popular among female followers. The analysis unit also analyzes the interest data of followers and identifies posts that are highly rated by followers with common interests. For example, it finds posts related to specific hobbies or interests. This makes it possible to find commonalities based on the attribute data of followers of similar accounts.

[0035] The analysis unit can perform a detailed analysis of the engagement data of similar accounts and identify the most effective commonalities. For example, the analysis unit uses a generation AI to analyze the "like" data of similar accounts and identify the posts that received the most "likes." For example, it can find that certain image styles and captions are effective. The analysis unit also analyzes share data and identifies the posts that were shared the most. For example, it can find the characteristics of highly viral content. The analysis unit also analyzes comment data and identifies the posts that drew the most comments. For example, it can find that question-type posts that encourage dialogue with followers are effective. This makes it possible to identify the most effective commonalities based on the engagement data of similar accounts.

[0036] The analysis unit can analyze similar accounts in different industries and fields to find common crossover features. For example, the generation AI can analyze similar accounts in different industries to identify common success factors. For example, it can find visual styles common to accounts in the fashion and technology industries. The analysis unit can also analyze the content of posts by similar accounts in different fields to find common crossover features. For example, it can find engagement methods common to sports and entertainment accounts. The analysis unit can also analyze trends in different industries to find common trends. For example, it can find common trends in the health and wellness field. This allows the generation AI to analyze similar accounts in different industries and find common crossover features.

[0037] The analysis unit can analyze the visual elements used in posts by similar accounts and identify visual commonalities. For example, the analysis unit uses a generative AI to analyze color data of images posted by similar accounts and identify the most effective color combinations. For example, it can find that posts that frequently use bright colors are highly rated. The analysis unit can also analyze the composition of the posted images and identify the most effective composition. For example, it can find that compositions that place the subject in the center are highly rated. The analysis unit can also analyze the fonts used in the posts and identify the most effective font style. For example, it can find that handwritten-style fonts are highly rated. This makes it possible to identify visual commonalities based on the visual elements used in posts by similar accounts.

[0038] The analysis unit can perform detailed analysis of engagement data on a user's past posts and learn the most effective posting patterns. For example, the generation AI analyzes the number of likes and comments on a user's past posts to identify the posting pattern with the highest engagement. For example, it finds out that certain topics and styles are effective. The analysis unit also analyzes the number of shares on past posts to identify the posting pattern with the most shares. For example, it finds out the characteristics of highly viral content. The analysis unit also learns the most effective posting patterns based on engagement data on a user's past posts and suggests ideas for new posts. For example, it refers to the content of posts that have been successful in the past. This allows the generation AI to learn the most effective posting patterns based on engagement data on a user's past posts.

[0039] The analysis unit learns the attribute data of the user's followers and, based on that, can suggest the most suitable post content for the followers. For example, the analysis unit's generation AI learns the age data of the user's followers and suggests the most suitable post content for a specific age group. For example, it suggests topics and styles that are popular among younger people. The analysis unit also learns the gender data of followers and suggests the most suitable post content for each gender. For example, it suggests visual styles and themes that are popular among female followers. The analysis unit also learns the interest data of followers and suggests the most suitable post content for followers who share common interests. For example, it suggests posts related to specific hobbies or interests. This makes it possible to suggest the most suitable post content based on the attribute data of the user's followers.

[0040] The analysis unit can learn about the user's other online activities and suggest post content based on that. For example, the analysis unit uses a generative AI to analyze the user's blog posts and suggest social media posts related to the blog content. For example, the analysis unit may share products or services introduced in the blog on social media. The analysis unit may also analyze the content of the user's website and suggest social media posts that match the website's theme. For example, the analysis unit may summarize and post information provided on the website on social media. The analysis unit may also analyze the content of posts on other social media accounts and suggest posts that are consistent across different platforms. For example, posts on Twitter may also be used on Instagram and Facebook. This makes it possible to suggest post content based on the user's other online activities.

[0041] The analysis unit can learn data from the user's competing accounts and suggest post content to compete against the competitors. For example, the generation AI in the analysis unit analyzes the post content of the competing accounts and suggests effective post content to compete against the competitors. For example, it refers to the hashtags and keywords used by the competitors. The analysis unit also analyzes the engagement data of the competing accounts and suggests the timing of posts to compete against the competitors. For example, it posts during the time period when the competitors receive the most engagement. The analysis unit also analyzes the follower attributes of the competing accounts and suggests post content to appeal to the competitors' followers. For example, it covers topics that interest the competitors' followers. This makes it possible to suggest post content to compete against the competitors based on the data from the user's competing accounts.

[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 manager system can also collect the user's health data and suggest optimal posting timing and content based on the user's health condition. For example, it can collect heart rate and exercise data from the user's fitness tracker and suggest posting during the relaxed time after exercise. It can also analyze the user's sleep data and suggest posting in the morning after a sufficient rest. It can also collect the user's dietary data and suggest posting during the time when the user feels most satisfied after a meal. This makes it possible to suggest optimal posting timing and content based on the user's health condition.

[0044] The SNS manager system can also suggest optimal post content based on the user's hobbies and interests. For example, if the user likes music, posts containing the latest music trends and concert information can be suggested. If the user likes traveling, posts containing recommended travel spots and travel tips can be suggested. Furthermore, if the user enjoys cooking, posts containing new recipes and cooking tips can be suggested. In this way, the SNS manager system can suggest optimal post content based on the user's hobbies and interests.

[0045] The SNS manager system can also learn data from the user's competing accounts and suggest content to post to compete against the competitors. For example, it can analyze the content posted by the competing accounts and suggest effective content to post to compete against the competitors. It can also analyze the engagement data of the competing accounts and suggest the timing of posts to compete against the competitors. It can also analyze the follower attributes of the competing accounts and suggest content to post to appeal to the competitors' followers. This makes it possible to suggest content to post to compete against the competitors based on the data from the user's competing accounts.

[0046] The SNS manager system can also learn about the user's other online activities and suggest posts based on that information. For example, it can analyze the user's blog posts and suggest SNS posts related to the blog content. It can also analyze the content of the user's website and suggest SNS posts that match the website's theme. It can also analyze posts from other SNS accounts and suggest posts that are consistent across different platforms. This allows it to suggest posts based on the user's other online activities.

[0047] The SNS manager system can also analyze the user's past advertising campaign data to suggest the most effective posting timing and content. For example, it can analyze the click-through rate and conversion rate of past advertising campaigns to suggest the most effective posting timing. It can also analyze the creative elements (images, text, videos) used in past advertising campaigns to suggest the most effective posting content. It can also analyze the target audience data of past advertising campaigns to suggest the optimal posting timing and content. This makes it possible to suggest the optimal posting timing and content based on past advertising campaign data.

[0048] The SNS manager system can also learn attribute data of the user's followers and, based on that, suggest the most suitable post content for the followers. For example, it can learn the followers' age data and suggest the most suitable post content for a specific age group. It can also learn the followers' gender data and suggest the most suitable post content for each gender. It can also learn the followers' interest data and suggest the most suitable post content for followers with common interests. This makes it possible to suggest the most suitable post content based on the attribute data of the user's followers.

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

[0050] Step 1: The data collection unit collects data related to the user's social media account. For example, the data collection unit may use an API to collect post content, follower count, and engagement data. The data collection unit may also use scraping technology to collect data from the web. Specifically, the data collection unit uses the API of the social media platform to obtain user post content and follower activity data. When using scraping technology, the data collection unit analyzes the HTML structure of the webpage and extracts the required data. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis to identify trends in the data. The analysis unit can also use machine learning algorithms to analyze the data and find patterns. Specifically, the analysis unit uses regression analysis to analyze the relationship between posting timing and engagement, and uses clustering techniques to classify follower behavior patterns. Step 3: The suggestion unit suggests optimal posting timing and content based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests posting timing based on followers' active time periods and suggests post content that matches followers' interests based on past engagement data. Specifically, the suggestion unit identifies followers' active time periods and notifies the user to post during those times, analyzes the post content that followers responded most to based on past engagement data, and suggests similar content.

[0051] (Example 2) The SNS manager system according to the embodiment of the present invention collects data related to users' SNS accounts, analyzes it using a generation AI, and suggests optimal posting timing and content. This allows the SNS manager system to streamline the management and operation of SNS and promote monetization.

[0052] An SNS manager system according to an embodiment includes a data collection unit, an analysis unit, and a suggestion unit. The data collection unit collects data related to a user's SNS account. For example, the data collection unit uses an API to collect post content, follower count, and engagement data. The data collection unit can also collect data from the web using scraping technology. For example, the data collection unit uses the API of an SNS platform to obtain user post content and follower activity data. When using scraping technology, the data collection unit analyzes the HTML structure of a webpage and extracts necessary data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis to identify data trends. The analysis unit can also analyze the data and find patterns using a machine learning algorithm. For example, the analysis unit uses regression analysis to analyze the relationship between post timing and engagement. When using a machine learning algorithm, the analysis unit uses a clustering method to classify follower behavior patterns. The suggestion unit suggests optimal post timing and content based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests post timing based on followers' active time periods. The suggestion unit can also suggest post content that matches the interests of followers based on past engagement data. For example, the suggestion unit identifies the active time periods of followers and notifies the user to post during those times. Based on the past engagement data, the suggestion unit analyzes the post content that followers responded most to and suggests similar content. This allows the SNS manager system according to the embodiment to improve the efficiency of SNS management and operation and promote monetization. For example, the SNS manager system automatically optimizes the user's posting schedule and maximizes engagement. The user can post content that attracts the interest of followers based on the suggested post content. The SNS manager system supports user monetization and improves the effectiveness of SNS operation.

[0053] The data collection unit can collect real-time behavioral data of users. For example, the data collection unit collects location information from a user's smartphone in real time and suggests the optimal posting timing when the user is in a specific location. For example, if the user is at an event venue, the data collection unit notifies the user that posting on the spot would be effective. The data collection unit also analyzes the user's activity log and suggests the optimal posting timing based on specific behavioral patterns. For example, the data collection unit suggests that the user post a health-related post immediately after finishing a run. The data collection unit also works with the user's calendar app to suggest the optimal posting timing based on their schedule. For example, the data collection unit finds a time to attract followers' attention by posting before or after a meeting or event. This makes it possible to suggest the optimal posting timing based on the user's real-time behavioral data.

[0054] The analysis unit can analyze the behavioral patterns of a user's followers and suggest posting times that match the time periods when followers are most active. For example, the analysis unit's generation AI can analyze followers' login times and activity and suggest posting during the time periods when followers are most active. For example, it can recommend posting in the evening when many followers are logged in. The analysis unit can also analyze followers' past engagement data and suggest posting times that match the time periods when responses are highest. For example, it can identify the time periods when followers leave many likes and comments. The analysis unit can also enable the generation AI to take into account the region and time zone of followers and suggest posting during the time periods when followers in each region are most active. For example, it can create a posting schedule that matches followers in different time zones. This allows engagement to be maximized by posting during the time periods when followers are most active.

[0055] The suggestion unit can use the emotion estimation function to analyze the user's current emotional state and suggest optimal post content to elicit positive emotions. For example, the suggestion unit uses a generation AI to analyze the user's facial expressions and estimate the user's current emotional state. For example, if the user is smiling, the suggestion unit suggests posts with positive content. The suggestion unit also analyzes the user's voice tone to estimate the emotional state. For example, if the user is excited, the suggestion unit suggests energetic post content that reflects that emotion. The suggestion unit also analyzes the user's emotional responses to past posts and suggests post content to elicit positive emotions. For example, the suggestion unit refers to posts that have received many likes in the past. This makes it possible to suggest optimal post content based on the user's emotional state.

[0056] The suggestion unit can suggest the optimal posting timing and content for each different social media platform. For example, the generation AI in the suggestion unit analyzes the algorithms of each social media platform and suggests the optimal posting timing for each platform. For example, on Twitter, real-time is important, so posts are made during times when followers are most active. The suggestion unit also analyzes user engagement data for each platform and suggests the optimal posting content. For example, on Instagram, visual content is important, so high-quality images and videos are suggested. The generation AI in the suggestion unit also analyzes trends on each platform and suggests posting content that matches the trends. For example, on Facebook, it recommends posts related to currently popular news and events. This makes it possible to suggest the optimal posting timing and content for each different social media platform.

[0057] The suggestion unit can analyze a user's past advertising campaign data and suggest the most effective posting timing and content. For example, the generation AI analyzes the click rates and conversion rates of past advertising campaigns to suggest the most effective posting timing. For example, posting during a time period that recorded a high click rate in the past. The suggestion unit also analyzes the creative elements (images, text, videos) used in past advertising campaigns to suggest the most effective posting content. For example, reusing image styles that have generated high engagement. The suggestion unit also analyzes the target audience data of past advertising campaigns to suggest the optimal posting timing and content. For example, posting effectively for a specific demographic group. This allows the system to suggest the optimal posting timing and content based on past advertising campaign data.

[0058] The suggestion unit uses the emotion estimation function to analyze the emotional state of a user's followers in real time and suggest post content that matches the followers' emotions. For example, the generation AI in the suggestion unit analyzes the followers' comments and messages to estimate their emotional state. For example, if a follower is expressing positive emotions, the suggestion unit suggests post content that matches that emotion. The suggestion unit also analyzes the followers' reactions (likes, shares, comments) to estimate their emotional state. For example, it takes into account posts that have received many likes from followers. The suggestion unit also monitors the followers' emotional state in real time using the generation AI and suggests optimal post content based on that data. For example, if a follower is expressing negative emotions, the suggestion unit suggests posts that include encouraging messages. This makes it possible to suggest optimal post content based on the followers' emotional state.

[0059] The analysis unit analyzes the attribute data of followers of similar accounts and can find commonalities based on that data. For example, the analysis unit uses a generation AI to analyze the age data of followers of similar accounts and identify posts that are popular among specific age groups. For example, it finds topics and styles that are popular among younger people. The analysis unit also analyzes the gender data of followers and identifies posts that are highly rated by each gender. For example, it finds visual styles and themes that are popular among female followers. The analysis unit also analyzes the interest data of followers and identifies posts that are highly rated by followers with common interests. For example, it finds posts related to specific hobbies or interests. This makes it possible to find commonalities based on the attribute data of followers of similar accounts.

[0060] The analysis unit can perform a detailed analysis of the engagement data of similar accounts and identify the most effective commonalities. For example, the analysis unit uses a generation AI to analyze the "like" data of similar accounts and identify the posts that received the most "likes." For example, it can find that certain image styles and captions are effective. The analysis unit also analyzes share data and identifies the posts that were shared the most. For example, it can find the characteristics of highly viral content. The analysis unit also analyzes comment data and identifies the posts that drew the most comments. For example, it can find that question-type posts that encourage dialogue with followers are effective. This makes it possible to identify the most effective commonalities based on the engagement data of similar accounts.

[0061] The analysis unit uses the emotion estimation function to analyze followers' emotional responses to posts from similar accounts and can find commonalities that elicit positive emotions. For example, the analysis unit uses the generation AI to analyze comments from followers on posts from similar accounts and identify posts that elicit positive emotional responses. For example, it finds posts with many comments of gratitude and joy. The analysis unit also analyzes follower reactions (likes, shares) and identifies posts with many reactions that show positive emotions. For example, it finds posts with many heart-shaped reactions. The analysis unit also uses the generation AI to monitor followers' emotional responses in real time and identify commonalities that elicit positive emotions. For example, it finds posts that contain humor that makes followers smile. This makes it possible to find commonalities that elicit positive emotions based on the emotional responses of followers of similar accounts.

[0062] The analysis unit can analyze similar accounts in different industries and fields to find common crossover features. For example, the generation AI can analyze similar accounts in different industries to identify common success factors. For example, it can find visual styles common to accounts in the fashion and technology industries. The analysis unit can also analyze the content of posts by similar accounts in different fields to find common crossover features. For example, it can find engagement methods common to sports and entertainment accounts. The analysis unit can also analyze trends in different industries to find common trends. For example, it can find common trends in the health and wellness field. This allows the generation AI to analyze similar accounts in different industries and find common crossover features.

[0063] The analysis unit can analyze the visual elements used in posts by similar accounts and identify visual commonalities. For example, the analysis unit uses a generative AI to analyze color data of images posted by similar accounts and identify the most effective color combinations. For example, it can find that posts that frequently use bright colors are highly rated. The analysis unit can also analyze the composition of the posted images and identify the most effective composition. For example, it can find that compositions that place the subject in the center are highly rated. The analysis unit can also analyze the fonts used in the posts and identify the most effective font style. For example, it can find that handwritten-style fonts are highly rated. This makes it possible to identify visual commonalities based on the visual elements used in posts by similar accounts.

[0064] The analysis unit uses the emotion estimation function to monitor the emotional state of followers of similar accounts in real time and find commonalities based on emotions. For example, the analysis unit uses the generation AI to analyze comments from followers of similar accounts in real time and monitor their emotional state. For example, it identifies posts that elicit a lot of positive comments. The analysis unit also analyzes followers' reactions (likes, shares) in real time and monitors their emotional state. For example, it identifies posts that elicit a lot of heart-shaped reactions. The analysis unit also uses the generation AI to monitor followers' emotional state in real time and identify commonalities based on emotions based on that data. For example, it finds posts that contain humor that make followers smile. This makes it possible to find commonalities based on emotions based on the emotional states of followers of similar accounts.

[0065] The analysis unit can perform detailed analysis of engagement data on a user's past posts and learn the most effective posting patterns. For example, the generation AI analyzes the number of likes and comments on a user's past posts to identify the posting pattern with the highest engagement. For example, it finds out that certain topics and styles are effective. The analysis unit also analyzes the number of shares on past posts to identify the posting pattern with the most shares. For example, it finds out the characteristics of highly viral content. The analysis unit also learns the most effective posting patterns based on engagement data on a user's past posts and suggests ideas for new posts. For example, it refers to the content of posts that have been successful in the past. This allows the generation AI to learn the most effective posting patterns based on engagement data on a user's past posts.

[0066] The analysis unit learns the attribute data of the user's followers and, based on that, can suggest the most suitable post content for the followers. For example, the analysis unit's generation AI learns the age data of the user's followers and suggests the most suitable post content for a specific age group. For example, it suggests topics and styles that are popular among younger people. The analysis unit also learns the gender data of followers and suggests the most suitable post content for each gender. For example, it suggests visual styles and themes that are popular among female followers. The analysis unit also learns the interest data of followers and suggests the most suitable post content for followers who share common interests. For example, it suggests posts related to specific hobbies or interests. This makes it possible to suggest the most suitable post content based on the attribute data of the user's followers.

[0067] The analysis unit uses the emotion estimation function to analyze followers' emotional reactions to the user's past posts and can suggest post content that elicits positive emotions. For example, the analysis unit uses a generation AI to analyze followers' comments on the user's past posts and identify post content that elicits positive emotional reactions. For example, it finds posts with many comments of gratitude and joy. The analysis unit also analyzes followers' reactions (likes, shares) and identifies post content with many reactions that show positive emotions. For example, it finds posts with many heart mark reactions. The analysis unit also uses a generation AI to monitor followers' emotional reactions in real time and suggest post content that elicits positive emotions. For example, it suggests posts that include humor that will make followers smile. This makes it possible to suggest post content that elicits positive emotions based on followers' emotional reactions to the user's past posts.

[0068] The analysis unit can learn about the user's other online activities and suggest post content based on that. For example, the analysis unit uses a generative AI to analyze the user's blog posts and suggest social media posts related to the blog content. For example, the analysis unit may share products or services introduced in the blog on social media. The analysis unit may also analyze the content of the user's website and suggest social media posts that match the website's theme. For example, the analysis unit may summarize and post information provided on the website on social media. The analysis unit may also analyze the content of posts on other social media accounts and suggest posts that are consistent across different platforms. For example, posts on Twitter may also be used on Instagram and Facebook. This makes it possible to suggest post content based on the user's other online activities.

[0069] The analysis unit can learn data from the user's competing accounts and suggest post content to compete against the competitors. For example, the generation AI in the analysis unit analyzes the post content of the competing accounts and suggests effective post content to compete against the competitors. For example, it refers to the hashtags and keywords used by the competitors. The analysis unit also analyzes the engagement data of the competing accounts and suggests the timing of posts to compete against the competitors. For example, it posts during the time period when the competitors receive the most engagement. The analysis unit also analyzes the follower attributes of the competing accounts and suggests post content to appeal to the competitors' followers. For example, it covers topics that interest the competitors' followers. This makes it possible to suggest post content to compete against the competitors based on the data from the user's competing accounts.

[0070] The analysis unit uses an emotion estimation function to monitor the user's current emotional state in real time and suggest post content based on the emotion. For example, the analysis unit uses a generation AI to analyze the user's facial expressions in real time to estimate the user's current emotional state. For example, if the user is smiling, it suggests posts with positive content. The analysis unit also analyzes the user's voice tone in real time to estimate the user's emotional state. For example, if the user is excited, it suggests energetic post content that reflects that emotion. The analysis unit also uses a generation AI to monitor the user's emotional responses to past posts in real time and suggest post content that elicits positive emotions. For example, it refers to posts that have received many likes in the past. This makes it possible to suggest post content based on the user's current emotional state.

[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 manager system can also collect the user's health data and suggest optimal posting timing and content based on the user's health condition. For example, it can collect heart rate and exercise data from the user's fitness tracker and suggest posting during the relaxed time after exercise. It can also analyze the user's sleep data and suggest posting in the morning after a sufficient rest. It can also collect the user's dietary data and suggest posting during the time when the user feels most satisfied after a meal. This makes it possible to suggest optimal posting timing and content based on the user's health condition.

[0073] The SNS manager system can also suggest optimal post content based on the user's hobbies and interests. For example, if the user likes music, posts containing the latest music trends and concert information can be suggested. If the user likes traveling, posts containing recommended travel spots and travel tips can be suggested. Furthermore, if the user enjoys cooking, posts containing new recipes and cooking tips can be suggested. In this way, the SNS manager system can suggest optimal post content based on the user's hobbies and interests.

[0074] The SNS manager system can also perform detailed analysis of followers' reactions to a user's past posts to learn the most effective posting patterns. For example, it can analyze the number of likes and comments on past posts to identify the posting pattern with the highest engagement. It can also analyze the number of shares on past posts to identify the posting pattern with the most shares. It can also analyze followers' emotional reactions to past posts to identify posting patterns that evoke positive emotions. This makes it possible to learn the most effective posting patterns based on followers' reactions to a user's past posts.

[0075] The SNS manager system can also learn data from the user's competing accounts and suggest content to post to compete against the competitors. For example, it can analyze the content posted by the competing accounts and suggest effective content to post to compete against the competitors. It can also analyze the engagement data of the competing accounts and suggest the timing of posts to compete against the competitors. It can also analyze the follower attributes of the competing accounts and suggest content to post to appeal to the competitors' followers. This makes it possible to suggest content to post to compete against the competitors based on the data from the user's competing accounts.

[0076] The SNS manager system can also learn about the user's other online activities and suggest posts based on that information. For example, it can analyze the user's blog posts and suggest SNS posts related to the blog content. It can also analyze the content of the user's website and suggest SNS posts that match the website's theme. It can also analyze posts from other SNS accounts and suggest posts that are consistent across different platforms. This allows it to suggest posts based on the user's other online activities.

[0077] The SNS manager system can also monitor the user's emotional state in real time and suggest content to post based on the user's emotions. For example, the system can analyze the user's facial expressions in real time to estimate the user's current emotional state. It can also analyze the user's tone of voice in real time to estimate the user's emotional state. It can also monitor the user's emotional responses to past posts in real time to suggest content to post that elicits positive emotions. This allows the system to suggest content to post based on the user's current emotional state.

[0078] The SNS manager system can also monitor the emotional state of a user's followers in real time and suggest post content that matches their emotions. For example, it can analyze followers' comments and messages to infer their emotional state. It can also analyze followers' reactions (likes, shares, comments) to infer their emotional state. It can also monitor followers' emotional state in real time and suggest optimal post content based on that data. This makes it possible to suggest optimal post content based on followers' emotional state.

[0079] The SNS manager system can also analyze the user's past advertising campaign data to suggest the most effective posting timing and content. For example, it can analyze the click-through rate and conversion rate of past advertising campaigns to suggest the most effective posting timing. It can also analyze the creative elements (images, text, videos) used in past advertising campaigns to suggest the most effective posting content. It can also analyze the target audience data of past advertising campaigns to suggest the optimal posting timing and content. This makes it possible to suggest the optimal posting timing and content based on past advertising campaign data.

[0080] The SNS manager system can also learn attribute data of the user's followers and, based on that, suggest the most suitable post content for the followers. For example, it can learn the followers' age data and suggest the most suitable post content for a specific age group. It can also learn the followers' gender data and suggest the most suitable post content for each gender. It can also learn the followers' interest data and suggest the most suitable post content for followers with common interests. This makes it possible to suggest the most suitable post content based on the attribute data of the user's followers.

[0081] The SNS manager system can also analyze followers' emotional responses to a user's past posts and suggest content to post that elicits positive emotions. For example, it can analyze followers' comments on past posts to identify content to post that elicits positive emotional responses. It can also analyze followers' reactions (likes, shares) to identify content to post that has many reactions that show positive emotions. It can also monitor followers' emotional responses in real time and suggest content to post that elicits positive emotions. This makes it possible to suggest content to post that elicits positive emotions based on followers' emotional responses to the user's past posts.

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

[0083] Step 1: The data collection unit collects data related to the user's social media account. For example, the data collection unit may use an API to collect post content, follower count, and engagement data. The data collection unit may also use scraping technology to collect data from the web. Specifically, the data collection unit uses the API of the social media platform to obtain user post content and follower activity data. When using scraping technology, the data collection unit analyzes the HTML structure of the webpage and extracts the required data. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses statistical analysis to identify trends in the data. The analysis unit can also use machine learning algorithms to analyze the data and find patterns. Specifically, the analysis unit uses regression analysis to analyze the relationship between posting timing and engagement, and uses clustering techniques to classify follower behavior patterns. Step 3: The suggestion unit suggests optimal posting timing and content based on the results of the analysis by the analysis unit. For example, the suggestion unit suggests posting timing based on followers' active time periods and suggests post content that matches followers' interests based on past engagement data. Specifically, the suggestion unit identifies followers' active time periods and notifies the user to post during those times, analyzes the post content that followers responded most to based on past engagement data, and suggests similar content.

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

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

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] In the robot 414, 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 robot 414 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects data related to a user's SNS account; an analysis unit that analyzes the data collected by the data collection unit; a suggestion unit that suggests optimal posting timing and content based on the results of the analysis by the analysis unit. A system characterized by:

2. The data collection unit Collecting real-time behavioral data of said users 2. The system of claim 1.

3. The analysis unit Analyzing the behavioral patterns of the user's followers and suggesting the posting timing according to the time period when the followers are most active 2. The system of claim 1.

4. The proposal unit Analyze the user's current emotional state and suggest optimal posting content to elicit positive emotions 2. The system of claim 1.

5. The proposal unit Suggest the best posting timing and content for each of the different social media platforms 2. The system of claim 1.

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