system

Abstract

An object of a system according to an exemplary embodiment is to analyze post contents and follower attributes of SNS users and select an optimum influencer.SOLUTION: A system includes a contribution content analysis part, a follower number analysis part, a follower attribute analysis part, and an engagement analysis part. The posted content analysis unit analyzes the posted content of the SNS user. The number-of-followers analysis unit analyzes the number of followers based on the post contents analyzed by the post content analysis unit. The follower attribute analysis unit analyzes follower attributes on the basis of the number of followers analyzed by the number-of-followers analysis unit. The engagement analysis unit analyzes engagement based on the follower attributes analyzed by the follower attribute 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] Conventional technology had the problem that it was difficult to perform detailed analysis of social media users' posts and follower attributes to select the most suitable influencers.

[0005] The system according to the embodiment aims to analyze the content posted by SNS users and the attributes of their followers, and to select the most suitable influencer. [Means for solving the problem]

[0006] The system according to the embodiment includes a post content analysis unit, a follower number analysis unit, a follower attribute analysis unit, and an engagement analysis unit. The post content analysis unit analyzes the post content of SNS users. The follower number analysis unit analyzes the number of followers based on the post content analyzed by the post content analysis unit. The follower attribute analysis unit analyzes follower attributes based on the number of followers analyzed by the follower number analysis unit. The engagement analysis unit analyzes engagement based on the follower attributes analyzed by the follower attribute analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze the content posted by SNS users and the attributes of their followers, and select the most suitable influencer. [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 influencer selection system according to an embodiment of the present invention uses generative AI to select the most suitable influencer for a company and improve the quality and quantity of marketing. This system analyzes the content of posts by social media users, the number of followers, follower attributes, and engagement, and proposes influencers that best suit the company's needs. This allows the influencer selection system to efficiently select the most suitable influencer for a company and improve the quality and quantity of marketing.

[0029] An influencer selection system according to an embodiment includes a post content analysis unit, a follower count analysis unit, a follower attribute analysis unit, and an engagement analysis unit. The post content analysis unit analyzes the content posted by SNS users. For example, the generation AI analyzes the post content using a text generation AI (e.g., LLM). The generation AI can also analyze the post content using a multimodal generation AI. The generation AI can also perform sentiment analysis of the post content and prioritize posts with positive sentiment. The follower count analysis unit analyzes the number of followers based on the post content analyzed by the post content analysis unit. For example, the generation AI analyzes the increase / decrease trend of the follower count and highly evaluates users with a rapid increase in followers. The generation AI can also analyze the distribution of the follower count and evaluate influence in a specific region or country. The follower attribute analysis unit analyzes follower attributes based on the number of followers analyzed by the follower number analysis unit. For example, the generation AI analyzes the occupations and interests of followers and evaluates users with a large number of followers who match a company's target demographic. The generation AI can also analyze the age and gender distribution of followers and evaluate users with a large number of followers who match a company's target demographic. The engagement analysis unit analyzes engagement based on the follower attributes analyzed by the follower attribute analysis unit. For example, the generation AI evaluates the quality of engagement by analyzing and evaluating the content and quality of comments. The generation AI can also analyze the timing of engagement and evaluate the speed of engagement after posting. As a result, the influencer selection system according to the embodiment can efficiently select the most suitable influencer for a company and improve the quality and quantity of marketing.

[0030] The post content analysis unit performs topic modeling of the post content, extracts topics related to the brand, and can evaluate based on those topics. The post content analysis unit, for example, uses a generation AI to perform topic modeling of the post content and extracts topics related to the brand. For example, in the case of a fashion brand, fashion-related topics are extracted and evaluation is made based on those topics. It also performs topic modeling of the post content and extracts topics related to the brand. Specifically, the generation AI analyzes the text of the post and builds a topic model. Topics related to the brand are extracted and evaluation is made based on those topics. It also uses a generation AI to perform topic modeling of the post content and extracts topics related to the brand. For example, the generation AI analyzes images and videos of the post, extracts topics related to the brand, and evaluation is made based on those topics. In this way, by extracting topics related to the company's brand and evaluating based on those topics, it is possible to select influencers that meet the company's needs.

[0031] The post content analysis unit can perform image analysis of the posted content and evaluate whether the content of the image matches the brand image. The post content analysis unit, for example, uses generation AI to perform image analysis of the posted content and evaluate whether the content of the image matches the brand image. For example, in the case of a fashion brand, fashion-related images are highly rated. The post content analysis unit also performs image analysis of the posted content and evaluates whether the content of the image matches the brand image. Specifically, the generation AI extracts image features and evaluates whether it matches the brand image. The generation AI also performs image analysis of the posted content and evaluates whether the content of the image matches the brand image. For example, the generation AI analyzes the color and composition of the image and evaluates whether it matches the brand image. This allows influencers who are suitable for the company's brand image to be selected by evaluating whether the content of the image matches the company's brand image.

[0032] The post content analysis unit can include video content in its analysis of post content and evaluate the video content and number of views. The post content analysis unit, for example, uses generation AI to include video content in its analysis of post content and evaluate the video content and number of views. For example, in the case of a fashion brand, fashion-related videos will be highly rated. The post content analysis unit also includes video content in its analysis of post content and evaluates the video content and number of views. Specifically, the generation AI analyzes the content of the video and evaluates it based on the number of views and engagement. The generation AI also uses generation AI to include video content in its analysis of post content and evaluate the video content and number of views. For example, the generation AI analyzes the video viewing time and number of comments and reflects this in the evaluation. This makes it possible to select influencers that meet the needs of a company by evaluating the content of the video content and the number of views.

[0033] The post content analysis unit can comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation. The post content analysis unit, for example, uses a generation AI to comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation. For example, it comprehensively evaluates posts on Instagram and Twitter. It also comprehensively analyzes the content posted on different SNS platforms and makes a comprehensive evaluation. Specifically, the generation AI analyzes posts on each platform and makes a comprehensive evaluation. It also uses the generation AI to comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation. For example, the generation AI integrates engagement data from each platform and reflects it in the evaluation. This makes it possible to comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation, allowing influencers who meet the needs of a company to be selected.

[0034] The follower count analysis unit can analyze the increase / decrease trend of the follower count and highly evaluate users whose followers are increasing rapidly. The follower count analysis unit, for example, uses a generation AI to analyze the increase / decrease trend of the follower count and highly evaluate users whose followers are increasing rapidly. For example, it identifies and highly evaluates users whose follower count has increased rapidly in a short period of time. It also analyzes the increase / decrease trend of the follower count and highly evaluates users whose followers are increasing rapidly. Specifically, the generation AI analyzes time-series data on the number of followers and detects an increase trend. It also uses the generation AI to analyze the increase / decrease trend of the follower count and highly evaluate users whose followers are increasing rapidly. For example, the generation AI calculates the increase rate of the number of followers and evaluates users with a high increase rate. In this way, by highly evaluating users whose followers are increasing rapidly, it is possible to select influential influencers.

[0035] The follower count analysis unit can analyze the distribution of follower counts and evaluate influence in specific regions or countries. The follower count analysis unit, for example, uses generation AI to analyze the distribution of follower counts and evaluate influence in specific regions or countries. For example, users with a large number of followers in a specific country are highly rated. The follower count analysis unit also analyzes the distribution of follower counts and evaluates influence in specific regions or countries. Specifically, the generation AI analyzes the geographical data of followers and evaluates influence for each region. The generation AI also analyzes the distribution of follower counts and evaluates influence in specific regions or countries. For example, the generation AI analyzes the location information of followers and evaluates the number of followers for each region. This makes it possible to select region-specific influencers by evaluating influence in specific regions or countries.

[0036] The follower count analysis unit can evaluate the quality of the follower count and prioritize users with a large number of active followers. The follower count analysis unit, for example, uses a generation AI to evaluate the quality of the follower count and prioritize users with a large number of active followers. For example, it highly evaluates users with a large number of followers who frequently comment and like posts. It also evaluates the quality of the follower count and prioritizes users with a large number of active followers. Specifically, the generation AI analyzes follower activity data and evaluates the proportion of active followers. It also uses the generation AI to evaluate the quality of the follower count and prioritizes users with a large number of active followers. For example, the generation AI analyzes follower engagement data and evaluates the number of active followers. This allows for the selection of highly engaged influencers by prioritizing users with a large number of active followers.

[0037] The follower count analysis unit can take into account follower activity in its evaluation. The follower count analysis unit, for example, uses a generation AI to take into account follower activity in its analysis of the number of followers. For example, the evaluation is based on the frequency of followers' posts and the number of comments. The analysis of the number of followers is also taken into account follower activity in its evaluation. Specifically, the generation AI analyzes follower activity data and evaluates the percentage of active followers. The generation AI is also used to take into account follower activity in its analysis of the number of followers in its evaluation. For example, the generation AI analyzes follower engagement data and evaluates the number of active followers. In this way, by taking into account follower activity in its evaluation, it is possible to select influencers with high engagement.

[0038] The follower count analysis unit can evaluate by taking into account the influence of the followers on social media. The follower count analysis unit uses, for example, a generation AI to analyze the number of followers and evaluate by taking into account the influence of the followers on social media. For example, the evaluation is based on the number of followers of the follower. The evaluation is also performed by taking into account the influence of the followers on social media in the analysis of the number of followers. Specifically, the generation AI analyzes the follower count data of the followers and evaluates the proportion of highly influential followers. The generation AI is also used to analyze the number of followers and evaluate by taking into account the influence of the followers on social media. For example, the generation AI analyzes the engagement data of the followers and evaluates the number of highly influential followers. In this way, by taking into account the influence of the followers on social media in the evaluation, highly influential influencers can be selected.

[0039] The follower attribute analysis unit can analyze the occupations and interests of followers and evaluate users with many followers who match the company's target demographic. The follower attribute analysis unit, for example, uses generation AI to analyze the occupations and interests of followers and evaluate users with many followers who match the company's target demographic. For example, in the case of a fashion brand, users with many followers who are interested in fashion are highly evaluated. The unit also analyzes the occupations and interests of followers and evaluates users with many followers who match the company's target demographic. Specifically, the generation AI analyzes the follower's profile data and identifies the occupations and interests. The generation AI also analyzes the follower's occupations and interests and evaluates users with many followers who match the company's target demographic. For example, the generation AI analyzes the content of followers' posts and identifies the interests. This allows the unit to select influencers that meet the company's needs by analyzing the followers' occupations and interests and evaluating users with many followers who match the company's target demographic.

[0040] The follower attribute analysis unit can analyze the age and gender distribution of followers and evaluate users with a large number of followers who match the company's target demographic. The follower attribute analysis unit, for example, uses generation AI to analyze the age and gender distribution of followers and evaluate users with a large number of followers who match the company's target demographic. For example, for a company targeting young people, users with a large number of young followers will be highly evaluated. The generation AI also analyzes the age and gender distribution of followers and evaluates users with a large number of followers who match the company's target demographic. Specifically, the generation AI analyzes follower profile data to identify their age group and gender. The generation AI also uses the generation AI to analyze the age and gender distribution of followers and evaluate users with a large number of followers who match the company's target demographic. For example, the generation AI analyzes the content of followers' posts and identifies their age group and gender. This allows the analysis of the age and gender distribution of followers and evaluation of users with a large number of followers who match the company's target demographic, thereby enabling the selection of influencers that meet the company's needs.

[0041] The follower attribute analysis unit can analyze followers' purchasing history and consumption behavior and evaluate users with many followers who are interested in a company's products. The follower attribute analysis unit, for example, uses generation AI to analyze followers' purchasing history and consumption behavior and evaluate users with many followers who are interested in the company's products. For example, in the case of a fashion brand, users with many followers who have purchased fashion-related products are highly evaluated. The unit also analyzes followers' purchasing history and consumption behavior and evaluates users with many followers who are interested in the company's products. Specifically, the generation AI analyzes followers' purchasing data and identifies interests. The generation AI also analyzes followers' purchasing history and consumption behavior and evaluates users with many followers who are interested in the company's products. For example, the generation AI analyzes followers' online shopping data and identifies interests. This allows the unit to select influencers that meet the company's needs by analyzing followers' purchasing history and consumption behavior and evaluating users with many followers who are interested in the company's products.

[0042] The follower attribute analysis unit can evaluate by taking into account the geographical distribution of followers in the analysis of follower attributes. The follower attribute analysis unit, for example, uses a generation AI to evaluate by taking into account the geographical distribution of followers in the analysis of follower attributes. For example, it may highly evaluate users who have many followers in a specific region. It also evaluates by taking into account the geographical distribution of followers in the analysis of follower attributes. Specifically, the generation AI analyzes the location information of followers and evaluates the number of followers for each region. It also uses the generation AI to evaluate by taking into account the geographical distribution of followers in the analysis of follower attributes. For example, the generation AI analyzes the geographical data of followers and evaluates the influence for each region. In this way, by taking into account the geographical distribution of followers in the evaluation, it is possible to select region-specialized influencers.

[0043] The follower attribute analysis unit can evaluate follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. The follower attribute analysis unit, for example, uses a generation AI to evaluate follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. For example, it may highly evaluate users who have many followers who are active during specific time periods. It also evaluates follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. Specifically, the generation AI analyzes follower activity time data and identifies active time periods. It also uses the generation AI to evaluate follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. For example, the generation AI analyzes followers' posting times and engagement times and reflects this in its evaluation. In this way, by taking into account the time periods when followers are active on SNS when evaluating them, it is possible to select influencers with high engagement.

[0044] The engagement analysis unit can evaluate the quality of engagement and analyze and evaluate the content and quality of comments. The engagement analysis unit, for example, uses a generation AI to evaluate the quality of engagement and analyze and evaluate the content and quality of comments. For example, constructive comments and detailed feedback are highly rated. The engagement analysis unit also evaluates the quality of engagement and analyzes and evaluates the content and quality of comments. Specifically, the generation AI analyzes the text of comments and evaluates the quality of the content. The generation AI also evaluates the quality of engagement and analyzes and evaluates the content and quality of comments. For example, the generation AI analyzes the sentiment and topic of comments and evaluates their quality. In this way, by analyzing and evaluating the content and quality of comments, it is possible to select influencers with high engagement.

[0045] The engagement analysis unit can analyze the timing of engagement and evaluate the speed of engagement after posting. The engagement analysis unit, for example, uses generation AI to analyze the timing of engagement and evaluate the speed of engagement after posting. For example, it highly evaluates users who experience a lot of engagement immediately after posting. It also analyzes the timing of engagement and evaluates the speed of engagement after posting. Specifically, the generation AI analyzes time series data on engagement and evaluates the speed. It also uses generation AI to analyze the timing of engagement and evaluate the speed of engagement after posting. For example, the generation AI analyzes the time when engagement occurs and evaluates the speed. In this way, by evaluating the speed of engagement after posting, it is possible to select influencers who respond quickly.

[0046] The engagement analysis unit can analyze the continuity of engagement and evaluate users who maintain high engagement over a long period of time. The engagement analysis unit, for example, uses a generation AI to analyze the continuity of engagement and evaluate users who maintain high engagement over a long period of time. For example, it highly evaluates users who maintain high engagement for several months. It also analyzes the continuity of engagement and evaluates users who maintain high engagement over a long period of time. Specifically, the generation AI analyzes time-series data on engagement and evaluates continuity. It also uses the generation AI to analyze the continuity of engagement and evaluate users who maintain high engagement over a long period of time. For example, the generation AI analyzes fluctuations in engagement and evaluates continuity. In this way, by evaluating users who maintain high engagement over a long period of time, it is possible to select influencers with sustainable influence.

[0047] The engagement analysis unit can take into account the type of engagement in its evaluation. The engagement analysis unit, for example, uses a generation AI to take into account the type of engagement in its analysis of engagement and make an evaluation. For example, it may make an evaluation based on the number of shares or retweets. It may also take into account the type of engagement in its analysis of engagement and make an evaluation. Specifically, the generation AI analyzes engagement type data and evaluates the influence of each type. It may also use a generation AI to take into account the type of engagement in its analysis of engagement and make an evaluation. For example, the generation AI may analyze the engagement rate for each type of engagement and reflect this in its evaluation. In this way, by taking into account the type of engagement in its evaluation, it is possible to select highly influential influencers.

[0048] The engagement analysis unit can make an evaluation taking into account the location where the engagement occurred. The engagement analysis unit, for example, uses generation AI to make an evaluation taking into account the location where the engagement occurred. For example, engagement on a specific post or campaign may be highly rated. The evaluation also takes into account the location where the engagement occurred. Specifically, the generation AI analyzes data on the location where the engagement occurred and evaluates the influence of each location. The generation AI also uses generation AI to make an evaluation taking into account the location where the engagement occurred. For example, the generation AI analyzes engagement on a specific campaign or event and reflects this in the evaluation. In this way, by making an evaluation taking into account the location where the engagement occurred, it is possible to select influencers who have influence on specific posts or campaigns.

[0049] The optimal influencer suggestion unit can analyze the past campaign performance of the proposed influencer and prioritize suggesting influencers with a high success rate. The optimal influencer suggestion unit, for example, uses generation AI to analyze the past campaign performance of the proposed influencer and prioritize suggesting influencers with a high success rate. For example, it highly rates influencers who have achieved high engagement in the past. It also analyzes the past campaign performance of the proposed influencer and prioritizes suggesting influencers with a high success rate. Specifically, the generation AI analyzes campaign performance data and calculates the success rate. It also uses generation AI to analyze the past campaign performance of the proposed influencer and prioritize suggesting influencers with a high success rate. For example, the generation AI analyzes the ROI (return on investment) of past campaigns and reflects this in the evaluation. This allows the company's marketing effectiveness to be maximized by analyzing past campaign performance and prioritize suggesting influencers with a high success rate.

[0050] The optimal influencer suggestion unit can analyze the posting schedule of the proposed influencer and make suggestions that are tailored to the company's campaign period. For example, the optimal influencer suggestion unit uses generation AI to analyze the posting schedule of the proposed influencer and make suggestions that are tailored to the company's campaign period. For example, it highly rates influencers who post many times during the campaign period. It also analyzes the posting schedule of the proposed influencer and makes suggestions that are tailored to the company's campaign period. Specifically, the generation AI analyzes the influencer's posting history and identifies the schedule. It also uses generation AI to analyze the posting schedule of the proposed influencer and make suggestions that are tailored to the company's campaign period. For example, the generation AI analyzes the influencer's posting frequency and timing and reflects this in the evaluation. This allows the marketing effect to be maximized by analyzing the influencer's posting schedule and making suggestions that are tailored to the company's campaign period.

[0051] The optimal influencer suggestion unit can analyze the purchasing intent of the followers of the proposed influencer and prioritize suggesting influencers with followers with high purchasing intent. The optimal influencer suggestion unit, for example, uses a generation AI to analyze the purchasing intent of the followers of the proposed influencer and prioritize suggesting influencers with followers with high purchasing intent. For example, it highly rates influencers whose followers frequently purchase products. It also analyzes the purchasing intent of the followers of the proposed influencer and prioritizes suggesting influencers with followers with high purchasing intent. Specifically, the generation AI analyzes the purchase history of followers and identifies purchasing intent. It also uses the generation AI to analyze the purchasing intent of the followers of the proposed influencer and prioritizes suggesting influencers with followers with high purchasing intent. For example, the generation AI analyzes the online shopping data of followers and reflects this in the evaluation. In this way, by analyzing followers' purchasing intent and preferentially suggesting influencers with followers with high purchasing intent, it is possible to improve a company's sales.

[0052] The optimal influencer recommendation unit can explain in detail the reasons for recommending the recommended influencer, making the recommendation in a way that is easy for companies to understand. The optimal influencer recommendation unit, for example, uses generation AI to explain in detail the reasons for recommending the recommended influencer, making the recommendation in a way that is easy for companies to understand. For example, the influencer's past performance and follower attributes are explained in detail. The reason for recommending the recommended influencer is also explained in detail, making the recommendation in a way that is easy for companies to understand. Specifically, the generation AI analyzes the influencer's data and provides the reasons for the recommendation in report format. The generation AI also uses the generation AI to explain in detail the reasons for recommending the recommended influencer, making the recommendation in a way that is easy for companies to understand. For example, the generation AI specifically explains the influencer's strengths and success stories. This allows the reason for the recommendation to be explained in detail, making the recommendation in a way that is easy for companies to understand, thereby supporting corporate decision-making.

[0053] The optimal influencer proposal unit can make a proposal for a proposed influencer by taking into account success stories from other companies. The optimal influencer proposal unit, for example, uses a generation AI to make a proposal for a proposed influencer by taking into account success stories from other companies. For example, the proposal is made based on success stories from other companies in the same industry. The proposal for a proposed influencer is also made by taking into account success stories from other companies. Specifically, the generation AI analyzes campaign data from other companies and identifies success stories. The generation AI is also used to make a proposal for a proposed influencer by taking into account success stories from other companies. For example, the generation AI analyzes ROI (return on investment) data from other companies and reflects this in the evaluation. This makes it possible to support a company's marketing strategy by making proposals that take into account success stories from other companies.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The influencer selection system can further include a purchase history analysis unit that analyzes users' past purchase histories. The purchase history analysis unit can analyze users' past purchase histories and identify users who are interested in specific products or services. For example, in the case of a fashion brand, users who have previously purchased fashion-related products are highly rated. The purchase history analysis unit can also analyze users' purchase frequency and purchase amounts and identify users with a high purchasing intent. This allows influencers to be selected based on purchase histories, enabling a company to implement a marketing strategy that matches its target demographic.

[0056] The influencer selection system may further include a health data analysis unit that analyzes users' health data. The health data analysis unit may analyze users' health data and identify health-conscious users. For example, in the case of a fitness brand, data related to exercise habits and dietary habits may be analyzed to highly evaluate health-conscious users. The health data analysis unit may also analyze users' health conditions and fitness levels to identify users with specific health goals. This allows for effective marketing to be conducted to a target demographic with high health consciousness by selecting influencers based on health data.

[0057] The influencer selection system can further include a lifestyle data analysis unit that analyzes users' lifestyle data. The lifestyle data analysis unit can analyze users' lifestyle data and identify users with specific lifestyles. For example, in the case of an outdoor brand, data related to outdoor activities can be analyzed to highly evaluate users who are highly outdoor-oriented. The lifestyle data analysis unit can also analyze users' hobbies and interests and identify users who match a specific lifestyle. This allows for effective marketing to be conducted to target demographics with specific lifestyles by selecting influencers based on lifestyle data.

[0058] The influencer selection system may further include a location information analysis unit that analyzes user location information. The location information analysis unit can analyze the user location information and identify users who frequently visit specific regions or locations. For example, when conducting region-specific marketing, users who frequently visit specific regions are highly evaluated. The location information analysis unit can also analyze users' movement patterns and identify users who are interested in specific events or locations. This makes it possible to realize a region-specific marketing strategy by selecting influencers based on location information.

[0059] The influencer selection system may further include a device usage data analysis unit that analyzes user device usage data. The device usage data analysis unit may analyze user device usage data and identify users who frequently use specific devices. For example, when marketing a mobile app, users who frequently use smartphones may be highly evaluated. The device usage data analysis unit may also analyze user device usage patterns and identify users who are interested in specific apps or services. This allows for effective marketing to target demographics that use specific devices by selecting influencers based on device usage data.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The post content analysis unit analyzes the content posted by SNS users. For example, the generation AI analyzes the content posted using text generation AI (e.g., LLM). The generation AI can also analyze the content posted using multimodal generation AI. The generation AI can also perform sentiment analysis of the content posted and prioritize posts with positive sentiment. Step 2: The follower count analysis unit analyzes the number of followers based on the content of posts analyzed by the post content analysis unit. For example, the generation AI analyzes the trend of increases and decreases in the number of followers and highly evaluates users who have a rapid increase in followers. The generation AI can also analyze the distribution of follower numbers and evaluate influence in specific regions or countries. Step 3: The follower attribute analysis unit analyzes follower attributes based on the number of followers analyzed by the follower count analysis unit. For example, the generation AI analyzes the followers' occupations and interests, and evaluates users with many followers who match the company's target demographic. The generation AI can also analyze the age and gender distribution of followers, and evaluate users with many followers who match the company's target demographic. Step 4: The engagement analysis unit analyzes engagement based on the follower attributes analyzed by the follower attribute analysis unit. For example, the generation AI evaluates the quality of engagement by analyzing and evaluating the content and quality of comments. The generation AI can also analyze the timing of engagement and evaluate the speed of engagement after posting.

[0062] (Example 2) The influencer selection system according to an embodiment of the present invention uses generative AI to select the most suitable influencer for a company and improve the quality and quantity of marketing. This system analyzes the content of posts by social media users, the number of followers, follower attributes, and engagement, and proposes influencers that best suit the company's needs. This allows the influencer selection system to efficiently select the most suitable influencer for a company and improve the quality and quantity of marketing.

[0063] An influencer selection system according to an embodiment includes a post content analysis unit, a follower count analysis unit, a follower attribute analysis unit, and an engagement analysis unit. The post content analysis unit analyzes the content posted by SNS users. For example, the generation AI analyzes the post content using a text generation AI (e.g., LLM). The generation AI can also analyze the post content using a multimodal generation AI. The generation AI can also perform sentiment analysis of the post content and prioritize posts with positive sentiment. The follower count analysis unit analyzes the number of followers based on the post content analyzed by the post content analysis unit. For example, the generation AI analyzes the increase / decrease trend of the follower count and highly evaluates users with a rapid increase in followers. The generation AI can also analyze the distribution of the follower count and evaluate influence in a specific region or country. The follower attribute analysis unit analyzes follower attributes based on the number of followers analyzed by the follower number analysis unit. For example, the generation AI analyzes the occupations and interests of followers and evaluates users with a large number of followers who match a company's target demographic. The generation AI can also analyze the age and gender distribution of followers and evaluate users with a large number of followers who match a company's target demographic. The engagement analysis unit analyzes engagement based on the follower attributes analyzed by the follower attribute analysis unit. For example, the generation AI evaluates the quality of engagement by analyzing and evaluating the content and quality of comments. The generation AI can also analyze the timing of engagement and evaluate the speed of engagement after posting. As a result, the influencer selection system according to the embodiment can efficiently select the most suitable influencer for a company and improve the quality and quantity of marketing.

[0064] The post content analysis unit can perform sentiment analysis of the post content and prioritize evaluation of posts with positive sentiment. The post content analysis unit, for example, uses generation AI to perform sentiment analysis of the post content and prioritize evaluation of posts with positive sentiment. For example, posts expressing joy or excitement are highly rated, and posts with negative sentiment are low rated. The post content analysis unit also performs sentiment analysis of the post content and prioritize evaluation of posts with positive sentiment. Specifically, the generation AI analyzes the text of the post and calculates a sentiment score. Posts with a high sentiment score are prioritized in the evaluation. The generation AI also performs sentiment analysis of the post content and prioritize evaluation of posts with positive sentiment. For example, the generation AI analyzes images and videos of the post and prioritizes content that expresses positive sentiment. In this way, by preferentially evaluating posts with positive sentiment, a company's brand image can be improved.

[0065] The post content analysis unit performs topic modeling of the post content, extracts topics related to the brand, and can evaluate based on those topics. The post content analysis unit, for example, uses a generation AI to perform topic modeling of the post content and extracts topics related to the brand. For example, in the case of a fashion brand, fashion-related topics are extracted and evaluation is made based on those topics. It also performs topic modeling of the post content and extracts topics related to the brand. Specifically, the generation AI analyzes the text of the post and builds a topic model. Topics related to the brand are extracted and evaluation is made based on those topics. It also uses a generation AI to perform topic modeling of the post content and extracts topics related to the brand. For example, the generation AI analyzes images and videos of the post, extracts topics related to the brand, and evaluation is made based on those topics. In this way, by extracting topics related to the company's brand and evaluating based on those topics, it is possible to select influencers that meet the company's needs.

[0066] The post content analysis unit can perform image analysis of the posted content and evaluate whether the content of the image matches the brand image. The post content analysis unit, for example, uses generation AI to perform image analysis of the posted content and evaluate whether the content of the image matches the brand image. For example, in the case of a fashion brand, fashion-related images are highly rated. The post content analysis unit also performs image analysis of the posted content and evaluates whether the content of the image matches the brand image. Specifically, the generation AI extracts image features and evaluates whether it matches the brand image. The generation AI also performs image analysis of the posted content and evaluates whether the content of the image matches the brand image. For example, the generation AI analyzes the color and composition of the image and evaluates whether it matches the brand image. This allows influencers who are suitable for the company's brand image to be selected by evaluating whether the content of the image matches the company's brand image.

[0067] The post content analysis unit can include video content in its analysis of post content and evaluate the video content and number of views. The post content analysis unit, for example, uses generation AI to include video content in its analysis of post content and evaluate the video content and number of views. For example, in the case of a fashion brand, fashion-related videos will be highly rated. The post content analysis unit also includes video content in its analysis of post content and evaluates the video content and number of views. Specifically, the generation AI analyzes the content of the video and evaluates it based on the number of views and engagement. The generation AI also uses generation AI to include video content in its analysis of post content and evaluate the video content and number of views. For example, the generation AI analyzes the video viewing time and number of comments and reflects this in the evaluation. This makes it possible to select influencers that meet the needs of a company by evaluating the content of the video content and the number of views.

[0068] The post content analysis unit can comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation. The post content analysis unit, for example, uses a generation AI to comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation. For example, it comprehensively evaluates posts on Instagram and Twitter. It also comprehensively analyzes the content posted on different SNS platforms and makes a comprehensive evaluation. Specifically, the generation AI analyzes posts on each platform and makes a comprehensive evaluation. It also uses the generation AI to comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation. For example, the generation AI integrates engagement data from each platform and reflects it in the evaluation. This makes it possible to comprehensively analyze the content posted on different SNS platforms and make a comprehensive evaluation, allowing influencers who meet the needs of a company to be selected.

[0069] The post content analysis unit can use the emotion estimation function to analyze followers' emotional reactions to the post content and prioritize posts that receive a lot of positive reactions. The post content analysis unit can, for example, use the generation AI and the emotion estimation function to analyze followers' emotional reactions to the post content and prioritize posts that receive a lot of positive reactions. For example, it can highly evaluate posts that receive a lot of comments expressing joy or excitement. The emotion estimation function can also be used to analyze followers' emotional reactions to the post content and prioritize posts that receive a lot of positive reactions. Specifically, the generation AI analyzes followers' comments and reactions and calculates an emotion score. The generation AI can also use the emotion estimation function to analyze followers' emotional reactions to the post content and prioritize posts that receive a lot of positive reactions. For example, the generation AI can analyze followers' emotions in real time and reflect them in the evaluation. This allows a company's brand image to be improved by analyzing followers' emotional reactions and prioritize posts that receive a lot of positive reactions.

[0070] The follower count analysis unit can analyze the increase / decrease trend of the follower count and highly evaluate users whose followers are increasing rapidly. The follower count analysis unit, for example, uses a generation AI to analyze the increase / decrease trend of the follower count and highly evaluate users whose followers are increasing rapidly. For example, it identifies and highly evaluates users whose follower count has increased rapidly in a short period of time. It also analyzes the increase / decrease trend of the follower count and highly evaluates users whose followers are increasing rapidly. Specifically, the generation AI analyzes time-series data on the number of followers and detects an increase trend. It also uses the generation AI to analyze the increase / decrease trend of the follower count and highly evaluate users whose followers are increasing rapidly. For example, the generation AI calculates the increase rate of the number of followers and evaluates users with a high increase rate. In this way, by highly evaluating users whose followers are increasing rapidly, it is possible to select influential influencers.

[0071] The follower count analysis unit can analyze the distribution of follower counts and evaluate influence in specific regions or countries. The follower count analysis unit, for example, uses generation AI to analyze the distribution of follower counts and evaluate influence in specific regions or countries. For example, users with a large number of followers in a specific country are highly rated. The follower count analysis unit also analyzes the distribution of follower counts and evaluates influence in specific regions or countries. Specifically, the generation AI analyzes the geographical data of followers and evaluates influence for each region. The generation AI also analyzes the distribution of follower counts and evaluates influence in specific regions or countries. For example, the generation AI analyzes the location information of followers and evaluates the number of followers for each region. This makes it possible to select region-specific influencers by evaluating influence in specific regions or countries.

[0072] The follower count analysis unit can evaluate the quality of the follower count and prioritize users with a large number of active followers. The follower count analysis unit, for example, uses a generation AI to evaluate the quality of the follower count and prioritize users with a large number of active followers. For example, it highly evaluates users with a large number of followers who frequently comment and like posts. It also evaluates the quality of the follower count and prioritizes users with a large number of active followers. Specifically, the generation AI analyzes follower activity data and evaluates the proportion of active followers. It also uses the generation AI to evaluate the quality of the follower count and prioritizes users with a large number of active followers. For example, the generation AI analyzes follower engagement data and evaluates the number of active followers. This allows for the selection of highly engaged influencers by prioritizing users with a large number of active followers.

[0073] The follower count analysis unit can take into account follower activity in its evaluation. The follower count analysis unit, for example, uses a generation AI to take into account follower activity in its analysis of the number of followers. For example, the evaluation is based on the frequency of followers' posts and the number of comments. The analysis of the number of followers is also taken into account follower activity in its evaluation. Specifically, the generation AI analyzes follower activity data and evaluates the percentage of active followers. The generation AI is also used to take into account follower activity in its analysis of the number of followers in its evaluation. For example, the generation AI analyzes follower engagement data and evaluates the number of active followers. In this way, by taking into account follower activity in its evaluation, it is possible to select influencers with high engagement.

[0074] The follower count analysis unit can evaluate by taking into account the influence of the followers on social media. The follower count analysis unit uses, for example, a generation AI to analyze the number of followers and evaluate by taking into account the influence of the followers on social media. For example, the evaluation is based on the number of followers of the follower. The evaluation is also performed by taking into account the influence of the followers on social media in the analysis of the number of followers. Specifically, the generation AI analyzes the follower count data of the followers and evaluates the proportion of highly influential followers. The generation AI is also used to analyze the number of followers and evaluate by taking into account the influence of the followers on social media. For example, the generation AI analyzes the engagement data of the followers and evaluates the number of highly influential followers. In this way, by taking into account the influence of the followers on social media in the evaluation, highly influential influencers can be selected.

[0075] The follower count analysis unit can use the emotion estimation function to analyze users' emotional reactions to increases or decreases in the number of followers, and prioritize users with many positive reactions. The follower count analysis unit can, for example, use the generation AI and the emotion estimation function to analyze users' emotional reactions to increases or decreases in the number of followers, and prioritize users with many positive reactions. For example, the unit highly evaluates users who express joy or excitement when the number of followers increases. The emotion estimation function can also be used to analyze users' emotional reactions to increases or decreases in the number of followers, and prioritize users with many positive reactions. Specifically, the generation AI analyzes users' comments and reactions and calculates an emotion score. The generation AI can also use the emotion estimation function to analyze users' emotional reactions to increases or decreases in the number of followers, and prioritize users with many positive reactions. For example, the generation AI can analyze users' emotions in real time and reflect them in the evaluation. This allows the company's brand image to be improved by analyzing users' emotional reactions to increases or decreases in the number of followers and prioritize users with many positive reactions.

[0076] The follower attribute analysis unit can analyze the occupations and interests of followers and evaluate users with many followers who match the company's target demographic. The follower attribute analysis unit, for example, uses generation AI to analyze the occupations and interests of followers and evaluate users with many followers who match the company's target demographic. For example, in the case of a fashion brand, users with many followers who are interested in fashion are highly evaluated. The unit also analyzes the occupations and interests of followers and evaluates users with many followers who match the company's target demographic. Specifically, the generation AI analyzes the follower's profile data and identifies the occupations and interests. The generation AI also analyzes the follower's occupations and interests and evaluates users with many followers who match the company's target demographic. For example, the generation AI analyzes the content of followers' posts and identifies the interests. This allows the unit to select influencers that meet the company's needs by analyzing the followers' occupations and interests and evaluating users with many followers who match the company's target demographic.

[0077] The follower attribute analysis unit can analyze the age and gender distribution of followers and evaluate users with a large number of followers who match the company's target demographic. The follower attribute analysis unit, for example, uses generation AI to analyze the age and gender distribution of followers and evaluate users with a large number of followers who match the company's target demographic. For example, for a company targeting young people, users with a large number of young followers will be highly evaluated. The generation AI also analyzes the age and gender distribution of followers and evaluates users with a large number of followers who match the company's target demographic. Specifically, the generation AI analyzes follower profile data to identify their age group and gender. The generation AI also uses the generation AI to analyze the age and gender distribution of followers and evaluate users with a large number of followers who match the company's target demographic. For example, the generation AI analyzes the content of followers' posts and identifies their age group and gender. This allows the analysis of the age and gender distribution of followers and evaluation of users with a large number of followers who match the company's target demographic, thereby enabling the selection of influencers that meet the company's needs.

[0078] The follower attribute analysis unit can analyze followers' purchasing history and consumption behavior and evaluate users with many followers who are interested in a company's products. The follower attribute analysis unit, for example, uses generation AI to analyze followers' purchasing history and consumption behavior and evaluate users with many followers who are interested in the company's products. For example, in the case of a fashion brand, users with many followers who have purchased fashion-related products are highly evaluated. The unit also analyzes followers' purchasing history and consumption behavior and evaluates users with many followers who are interested in the company's products. Specifically, the generation AI analyzes followers' purchasing data and identifies interests. The generation AI also analyzes followers' purchasing history and consumption behavior and evaluates users with many followers who are interested in the company's products. For example, the generation AI analyzes followers' online shopping data and identifies interests. This allows the unit to select influencers that meet the company's needs by analyzing followers' purchasing history and consumption behavior and evaluating users with many followers who are interested in the company's products.

[0079] The follower attribute analysis unit can evaluate by taking into account the geographical distribution of followers in the analysis of follower attributes. The follower attribute analysis unit, for example, uses a generation AI to evaluate by taking into account the geographical distribution of followers in the analysis of follower attributes. For example, it may highly evaluate users who have many followers in a specific region. It also evaluates by taking into account the geographical distribution of followers in the analysis of follower attributes. Specifically, the generation AI analyzes the location information of followers and evaluates the number of followers for each region. It also uses the generation AI to evaluate by taking into account the geographical distribution of followers in the analysis of follower attributes. For example, the generation AI analyzes the geographical data of followers and evaluates the influence for each region. In this way, by taking into account the geographical distribution of followers in the evaluation, it is possible to select region-specialized influencers.

[0080] The follower attribute analysis unit can evaluate follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. The follower attribute analysis unit, for example, uses a generation AI to evaluate follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. For example, it may highly evaluate users who have many followers who are active during specific time periods. It also evaluates follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. Specifically, the generation AI analyzes follower activity time data and identifies active time periods. It also uses the generation AI to evaluate follower attributes by taking into account the time periods when followers are active on SNS when analyzing them. For example, the generation AI analyzes followers' posting times and engagement times and reflects this in its evaluation. In this way, by taking into account the time periods when followers are active on SNS when evaluating them, it is possible to select influencers with high engagement.

[0081] The follower attribute analysis unit can use the emotion estimation function to analyze users' emotional reactions to follower attributes and prioritize users with followers who have many positive reactions. The follower attribute analysis unit can, for example, use the generation AI and the emotion estimation function to analyze users' emotional reactions to follower attributes and prioritize users with followers who have many positive reactions. For example, it can highly evaluate users who express joy or excitement about follower attributes. It can also use the emotion estimation function to analyze users' emotional reactions to follower attributes and prioritize users with followers who have many positive reactions. Specifically, the generation AI analyzes followers' comments and reactions and calculates an emotion score. It can also use the generation AI and the emotion estimation function to analyze users' emotional reactions to follower attributes and prioritize users with followers who have many positive reactions. For example, the generation AI can analyze followers' emotions in real time and reflect them in the evaluation. This allows a company's brand image to be improved by analyzing users' emotional reactions to follower attributes and prioritize users with followers who have many positive reactions.

[0082] The engagement analysis unit can evaluate the quality of engagement and analyze and evaluate the content and quality of comments. The engagement analysis unit, for example, uses a generation AI to evaluate the quality of engagement and analyze and evaluate the content and quality of comments. For example, constructive comments and detailed feedback are highly rated. The engagement analysis unit also evaluates the quality of engagement and analyzes and evaluates the content and quality of comments. Specifically, the generation AI analyzes the text of comments and evaluates the quality of the content. The generation AI also evaluates the quality of engagement and analyzes and evaluates the content and quality of comments. For example, the generation AI analyzes the sentiment and topic of comments and evaluates their quality. In this way, by analyzing and evaluating the content and quality of comments, it is possible to select influencers with high engagement.

[0083] The engagement analysis unit can analyze the timing of engagement and evaluate the speed of engagement after posting. The engagement analysis unit, for example, uses generation AI to analyze the timing of engagement and evaluate the speed of engagement after posting. For example, it highly evaluates users who experience a lot of engagement immediately after posting. It also analyzes the timing of engagement and evaluates the speed of engagement after posting. Specifically, the generation AI analyzes time series data on engagement and evaluates the speed. It also uses generation AI to analyze the timing of engagement and evaluate the speed of engagement after posting. For example, the generation AI analyzes the time when engagement occurs and evaluates the speed. In this way, by evaluating the speed of engagement after posting, it is possible to select influencers who respond quickly.

[0084] The engagement analysis unit can analyze the continuity of engagement and evaluate users who maintain high engagement over a long period of time. The engagement analysis unit, for example, uses a generation AI to analyze the continuity of engagement and evaluate users who maintain high engagement over a long period of time. For example, it highly evaluates users who maintain high engagement for several months. It also analyzes the continuity of engagement and evaluates users who maintain high engagement over a long period of time. Specifically, the generation AI analyzes time-series data on engagement and evaluates continuity. It also uses the generation AI to analyze the continuity of engagement and evaluate users who maintain high engagement over a long period of time. For example, the generation AI analyzes fluctuations in engagement and evaluates continuity. In this way, by evaluating users who maintain high engagement over a long period of time, it is possible to select influencers with sustainable influence.

[0085] The engagement analysis unit can take into account the type of engagement in its evaluation. The engagement analysis unit, for example, uses a generation AI to take into account the type of engagement in its analysis of engagement and make an evaluation. For example, it may make an evaluation based on the number of shares or retweets. It may also take into account the type of engagement in its analysis of engagement and make an evaluation. Specifically, the generation AI analyzes engagement type data and evaluates the influence of each type. It may also use a generation AI to take into account the type of engagement in its analysis of engagement and make an evaluation. For example, the generation AI may analyze the engagement rate for each type of engagement and reflect this in its evaluation. In this way, by taking into account the type of engagement in its evaluation, it is possible to select highly influential influencers.

[0086] The engagement analysis unit can make an evaluation taking into account the location where the engagement occurred. The engagement analysis unit, for example, uses generation AI to make an evaluation taking into account the location where the engagement occurred. For example, engagement on a specific post or campaign may be highly rated. The evaluation also takes into account the location where the engagement occurred. Specifically, the generation AI analyzes data on the location where the engagement occurred and evaluates the influence of each location. The generation AI also uses generation AI to make an evaluation taking into account the location where the engagement occurred. For example, the generation AI analyzes engagement on a specific campaign or event and reflects this in the evaluation. In this way, by making an evaluation taking into account the location where the engagement occurred, it is possible to select influencers who have influence on specific posts or campaigns.

[0087] The engagement analysis unit can use the emotion estimation function to analyze followers' emotional responses to engagement and prioritize users with engagement that has a lot of positive responses. The engagement analysis unit can, for example, use the generation AI and the emotion estimation function to analyze followers' emotional responses to engagement and prioritize users with engagement that has a lot of positive responses. For example, engagement with a lot of comments expressing joy or excitement is highly rated. The emotion estimation function can also be used to analyze followers' emotional responses to engagement and prioritize users with engagement that has a lot of positive responses. Specifically, the generation AI analyzes followers' comments and reactions and calculates an emotion score. The generation AI can also use the emotion estimation function to analyze followers' emotional responses to engagement and prioritize users with engagement that has a lot of positive responses. For example, the generation AI can analyze followers' emotions in real time and reflect them in the evaluation. This allows a company's brand image to be improved by analyzing followers' emotional responses to engagement and prioritize users with engagement that has a lot of positive responses.

[0088] The optimal influencer suggestion unit can analyze the past campaign performance of the proposed influencer and prioritize suggesting influencers with a high success rate. The optimal influencer suggestion unit, for example, uses generation AI to analyze the past campaign performance of the proposed influencer and prioritize suggesting influencers with a high success rate. For example, it highly rates influencers who have achieved high engagement in the past. It also analyzes the past campaign performance of the proposed influencer and prioritizes suggesting influencers with a high success rate. Specifically, the generation AI analyzes campaign performance data and calculates the success rate. It also uses generation AI to analyze the past campaign performance of the proposed influencer and prioritize suggesting influencers with a high success rate. For example, the generation AI analyzes the ROI (return on investment) of past campaigns and reflects this in the evaluation. This allows the company's marketing effectiveness to be maximized by analyzing past campaign performance and prioritize suggesting influencers with a high success rate.

[0089] The optimal influencer suggestion unit can analyze the posting schedule of the proposed influencer and make suggestions that are tailored to the company's campaign period. For example, the optimal influencer suggestion unit uses generation AI to analyze the posting schedule of the proposed influencer and make suggestions that are tailored to the company's campaign period. For example, it highly rates influencers who post many times during the campaign period. It also analyzes the posting schedule of the proposed influencer and makes suggestions that are tailored to the company's campaign period. Specifically, the generation AI analyzes the influencer's posting history and identifies the schedule. It also uses generation AI to analyze the posting schedule of the proposed influencer and make suggestions that are tailored to the company's campaign period. For example, the generation AI analyzes the influencer's posting frequency and timing and reflects this in the evaluation. This allows the marketing effect to be maximized by analyzing the influencer's posting schedule and making suggestions that are tailored to the company's campaign period.

[0090] The optimal influencer suggestion unit can analyze the purchasing intent of the followers of the proposed influencer and prioritize suggesting influencers with followers with high purchasing intent. The optimal influencer suggestion unit, for example, uses a generation AI to analyze the purchasing intent of the followers of the proposed influencer and prioritize suggesting influencers with followers with high purchasing intent. For example, it highly rates influencers whose followers frequently purchase products. It also analyzes the purchasing intent of the followers of the proposed influencer and prioritizes suggesting influencers with followers with high purchasing intent. Specifically, the generation AI analyzes the purchase history of followers and identifies purchasing intent. It also uses the generation AI to analyze the purchasing intent of the followers of the proposed influencer and prioritizes suggesting influencers with followers with high purchasing intent. For example, the generation AI analyzes the online shopping data of followers and reflects this in the evaluation. In this way, by analyzing followers' purchasing intent and preferentially suggesting influencers with followers with high purchasing intent, it is possible to improve a company's sales.

[0091] The optimal influencer recommendation unit can explain in detail the reasons for recommending the recommended influencer, making the recommendation in a way that is easy for companies to understand. The optimal influencer recommendation unit, for example, uses generation AI to explain in detail the reasons for recommending the recommended influencer, making the recommendation in a way that is easy for companies to understand. For example, the influencer's past performance and follower attributes are explained in detail. The reason for recommending the recommended influencer is also explained in detail, making the recommendation in a way that is easy for companies to understand. Specifically, the generation AI analyzes the influencer's data and provides the reasons for the recommendation in report format. The generation AI also uses the generation AI to explain in detail the reasons for recommending the recommended influencer, making the recommendation in a way that is easy for companies to understand. For example, the generation AI specifically explains the influencer's strengths and success stories. This allows the reason for the recommendation to be explained in detail, making the recommendation in a way that is easy for companies to understand, thereby supporting corporate decision-making.

[0092] The optimal influencer proposal unit can make a proposal for a proposed influencer by taking into account success stories from other companies. The optimal influencer proposal unit, for example, uses a generation AI to make a proposal for a proposed influencer by taking into account success stories from other companies. For example, the proposal is made based on success stories from other companies in the same industry. The proposal for a proposed influencer is also made by taking into account success stories from other companies. Specifically, the generation AI analyzes campaign data from other companies and identifies success stories. The generation AI is also used to make a proposal for a proposed influencer by taking into account success stories from other companies. For example, the generation AI analyzes ROI (return on investment) data from other companies and reflects this in the evaluation. This makes it possible to support a company's marketing strategy by making proposals that take into account success stories from other companies.

[0093] The optimal influencer suggestion unit can use an emotion estimation function to analyze a company's emotional response to the proposed influencer and prioritize suggesting influencers with many positive responses. The optimal influencer suggestion unit can, for example, use a generation AI and the emotion estimation function to analyze a company's emotional response to the proposed influencer and prioritize suggesting influencers with many positive responses. For example, the unit highly rates influencers to whom the company has a favorable response. The emotion estimation function also analyzes a company's emotional response to the proposed influencer and prioritizes suggesting influencers with many positive responses. Specifically, the generation AI analyzes the company's comments and feedback and calculates an emotion score. The generation AI also uses the emotion estimation function to analyze a company's emotional response to the proposed influencer and prioritizes suggesting influencers with many positive responses. For example, the generation AI analyzes the company's emotions in real time and reflects them in the evaluation. This can support a company's marketing strategy by analyzing a company's emotional response and prioritize suggesting influencers with many positive responses.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The influencer selection system can further include a purchase history analysis unit that analyzes users' past purchase histories. The purchase history analysis unit can analyze users' past purchase histories and identify users who are interested in specific products or services. For example, in the case of a fashion brand, users who have previously purchased fashion-related products are highly rated. The purchase history analysis unit can also analyze users' purchase frequency and purchase amounts and identify users with a high purchasing intent. This allows influencers to be selected based on purchase histories, enabling a company to implement a marketing strategy that matches its target demographic.

[0096] The influencer selection system may further include a health data analysis unit that analyzes users' health data. The health data analysis unit may analyze users' health data and identify health-conscious users. For example, in the case of a fitness brand, data related to exercise habits and dietary habits may be analyzed to highly evaluate health-conscious users. The health data analysis unit may also analyze users' health conditions and fitness levels to identify users with specific health goals. This allows for effective marketing to be conducted to a target demographic with high health consciousness by selecting influencers based on health data.

[0097] The influencer selection system can further include a lifestyle data analysis unit that analyzes users' lifestyle data. The lifestyle data analysis unit can analyze users' lifestyle data and identify users with specific lifestyles. For example, in the case of an outdoor brand, data related to outdoor activities can be analyzed to highly evaluate users who are highly outdoor-oriented. The lifestyle data analysis unit can also analyze users' hobbies and interests and identify users who match a specific lifestyle. This allows for effective marketing to be conducted to target demographics with specific lifestyles by selecting influencers based on lifestyle data.

[0098] The influencer selection system may further include a location information analysis unit that analyzes user location information. The location information analysis unit can analyze the user location information and identify users who frequently visit specific regions or locations. For example, when conducting region-specific marketing, users who frequently visit specific regions are highly evaluated. The location information analysis unit can also analyze users' movement patterns and identify users who are interested in specific events or locations. This makes it possible to realize a region-specific marketing strategy by selecting influencers based on location information.

[0099] The influencer selection system may further include a device usage data analysis unit that analyzes user device usage data. The device usage data analysis unit may analyze user device usage data and identify users who frequently use specific devices. For example, when marketing a mobile app, users who frequently use smartphones may be highly evaluated. The device usage data analysis unit may also analyze user device usage patterns and identify users who are interested in specific apps or services. This allows for effective marketing to target demographics that use specific devices by selecting influencers based on device usage data.

[0100] The influencer selection system can further use a user emotion estimation function to analyze the user's emotional state. For example, it can analyze the user's posts and comments and calculate an emotion score. By prioritizing users with high emotion scores, it is possible to identify users with positive emotions. The emotion estimation function can also be used to analyze the user's emotional state in real time and track emotional fluctuations. As a result, by selecting influencers using the emotion estimation function, it is possible to conduct effective marketing to a target demographic with positive emotions.

[0101] The influencer selection system can further use a user emotion estimation function to analyze users' emotional responses. For example, it can analyze users' comments and reactions and calculate an emotion score. By prioritizing users with high emotion scores, it is possible to identify users with positive emotions. The emotion estimation function can also be used to analyze users' emotional responses in real time and track emotional fluctuations. As a result, by selecting influencers using the emotion estimation function, it is possible to conduct effective marketing to target demographics with positive emotions.

[0102] The influencer selection system can further use a user emotion estimation function to analyze users' emotion trends. For example, it can identify emotion trends by analyzing users' posts and comments. By preferentially evaluating users with positive emotion trends, it can identify users with positive emotions. The emotion estimation function can also be used to analyze users' emotion trends in real time and track emotional fluctuations. As a result, by selecting influencers using the emotion estimation function, it is possible to conduct effective marketing to target demographics with positive emotions.

[0103] The influencer selection system can further use a user emotion estimation function to analyze a user's emotion patterns. For example, it can identify emotion patterns by analyzing the content of user posts and comments. By preferentially evaluating users with positive emotion patterns, it is possible to identify users with positive emotions. The emotion estimation function can also be used to analyze a user's emotion patterns in real time and track emotional fluctuations. As a result, by selecting influencers using the emotion estimation function, it is possible to conduct effective marketing to a target demographic with positive emotions.

[0104] The influencer selection system can further use a user emotion estimation function to analyze users' emotional insights. For example, it can identify emotional insights by analyzing users' posts and comments. By preferentially evaluating users with positive emotional insights, it can identify users with positive emotions. It can also use the emotion estimation function to analyze users' emotional insights in real time and track emotional fluctuations. As a result, by selecting influencers using the emotion estimation function, it is possible to conduct effective marketing to target demographics with positive emotions.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The post content analysis unit analyzes the content posted by SNS users. For example, the generation AI analyzes the content posted using text generation AI (e.g., LLM). The generation AI can also analyze the content posted using multimodal generation AI. The generation AI can also perform sentiment analysis of the content posted and prioritize posts with positive sentiment. Step 2: The follower count analysis unit analyzes the number of followers based on the content of posts analyzed by the post content analysis unit. For example, the generation AI analyzes the trend of increases and decreases in the number of followers and highly evaluates users who have a rapid increase in followers. The generation AI can also analyze the distribution of follower numbers and evaluate influence in specific regions or countries. Step 3: The follower attribute analysis unit analyzes follower attributes based on the number of followers analyzed by the follower count analysis unit. For example, the generation AI analyzes the followers' occupations and interests, and evaluates users with many followers who match the company's target demographic. The generation AI can also analyze the age and gender distribution of followers, and evaluate users with many followers who match the company's target demographic. Step 4: The engagement analysis unit analyzes engagement based on the follower attributes analyzed by the follower attribute analysis unit. For example, the generation AI evaluates the quality of engagement by analyzing and evaluating the content and quality of comments. The generation AI can also analyze the timing of engagement and evaluate the speed of engagement after posting.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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]

[0174] 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 post content analysis unit that analyzes the content posted by SNS users; a follower number analysis unit that analyzes the number of followers based on the post content analyzed by the post content analysis unit; a follower attribute analysis unit that analyzes follower attributes based on the number of followers analyzed by the follower number analysis unit; an engagement analysis unit that analyzes engagement based on the follower attributes analyzed by the follower attribute analysis unit. A system characterized by:

2. The post content analysis unit Sentiment analysis of the content of the posts is performed, and posts with positive sentiment are prioritized.

2. The system of claim 1.

3. The post content analysis unit Topic modeling is performed on the content of the posts, topics related to the brand are extracted, and evaluation is based on those topics.

2. The system of claim 1.

4. The post content analysis unit Conduct an image analysis of the posted content and evaluate whether the content of the image matches the brand image.

2. The system of claim 1.

5. The post content analysis unit The analysis of the posted content will also include video content, and the content and number of views of the video will be evaluated.

2. The system of claim 1.

6. The post content analysis unit Comprehensive analysis of the content posted on different SNS platforms and comprehensive evaluation 2. The system of claim 1.

Images