system

Abstract

The system according to this embodiment aims to suggest the most suitable influencer and predict its effect. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a prediction unit, and a provision unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes the most suitable influencer based on the analysis results obtained by the analysis unit. The prediction unit predicts the effectiveness of the influencer proposed by the proposal unit. The provision unit provides a platform on which companies can compare and consider the influencers proposed by the proposal unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , ,

[0003] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the selection of influencers is time-consuming and the risk in case of being inappropriate is large. <00000​​​​​​​​​The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a prediction unit, and a provision unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes the most suitable influencers based on the analysis results obtained by the analysis unit. The prediction unit predicts the effectiveness of the influencers proposed by the proposal unit. The provision unit provides a platform on which companies can compare and consider the influencers proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable influencer and predict its effect. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An influencer selection system according to an embodiment of the present invention is a system that uses AI to streamline influencer selection and improve the success rate of marketing campaigns. This influencer selection system automatically collects SNS data and past campaign data, and the AI ​​analyzes the collected data to evaluate engagement and conversion. Based on this, the AI ​​lists the most suitable influencers and provides a platform that companies can easily compare and consider. Furthermore, the AI ​​predicts the effectiveness of the selected influencers and makes performance suggestions to maximize ROI. This mechanism significantly reduces the effort required for influencer selection and enables highly accurate, data-driven selection. In addition, seamless selection aligned with strategy and performance improvement are realized, and it is expected that competitiveness will be strengthened, brand value will be enhanced, and revenue will be increased. For example, the influencer selection system automatically collects SNS data and past campaign data. At this time, data from each SNS platform is integrated and detailed data such as the results of past campaigns, engagement rates, and conversion rates are collected. For example, the results of campaigns conducted in the past by a specific influencer are collected and analyzed based on that data. Next, the collected data is analyzed by the AI. AI evaluates engagement and conversions to determine the performance of each influencer. For example, the AI ​​analyzes how much engagement a particular influencer achieved and what their conversion rate was. Furthermore, the AI ​​lists the most suitable influencers. Based on the collected data and analysis results, the AI ​​selects the influencers best suited to a company's marketing strategy. For example, it lists influencers who have high engagement with a specific target audience. The AI ​​then provides a platform that allows companies to easily compare and evaluate these influencers. On this platform, detailed data and performance evaluations of the listed influencers can be viewed. For example, it can compare each influencer's past campaign results, engagement rates, and conversion rates.Finally, the AI ​​predicts the effectiveness of the selected influencers and provides performance suggestions to maximize ROI. Based on historical data and current trends, the AI ​​predicts how effective the selected influencers will be. For example, it predicts the ROI of a campaign conducted by a specific influencer and provides optimal performance suggestions based on the results. This significantly reduces the effort required for influencer selection and enables highly accurate, data-driven selection. Furthermore, it enables seamless selection aligned with strategy and performance improvement, leading to enhanced competitiveness, increased brand value, and expanded revenue. In this way, the influencer selection system can streamline influencer selection and improve the success rate of marketing campaigns.

[0029] The influencer selection system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a prediction unit, and a provision unit. The collection unit collects data. The collection unit can, for example, automatically collect SNS data and past campaign data. The collection unit collects data from each SNS platform using APIs or scraping techniques. For example, the collection unit collects the results of campaigns previously conducted by a specific influencer and performs analysis based on that data. The collection unit can also set the database update frequency and collect data periodically. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, evaluate engagement and conversions. The analysis unit evaluates the performance of each influencer based on engagement metrics such as the number of likes, comments, and shares. For example, the analysis unit analyzes how much engagement a specific influencer has gained. The analysis unit can also evaluate the performance of influencers based on conversion metrics such as the number of purchases, registrations, and click-through rates. For example, the analysis department analyzes the conversion rate achieved by a particular influencer. The proposal department proposes the most suitable influencers based on the analysis results obtained by the analysis department. The proposal department can, for example, list the most suitable influencers for a company's marketing strategy. The proposal department selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. For example, the proposal department lists influencers who have high engagement with a specific target group. The forecasting department predicts the effectiveness of the influencers proposed by the proposal department. The forecasting department can, for example, predict the ROI of the selected influencers. The forecasting department predicts how effective the selected influencers will be based on past data and current trends. For example, the forecasting department predicts the ROI of a campaign conducted by a particular influencer and makes optimal performance proposals based on the results. The provisioning department provides a platform that allows companies to compare and consider the influencers proposed by the proposal department.The service provider can, for example, display detailed data and performance evaluations of the listed influencers. The service provider can compare each influencer's past campaign results, engagement rates, and conversion rates. For example, the service provider can display influencer performance metrics and cost-effectiveness to allow companies to easily compare and consider them. As a result, the influencer selection system according to this embodiment can streamline influencer selection and improve the success rate of marketing campaigns.

[0030] The data collection unit collects data. For example, the data collection unit can automatically collect social media data and past campaign data. The data collection unit collects data from each social media platform using APIs and scraping techniques. Specifically, the data collection unit uses the APIs of each social media platform to obtain data such as the content of posts, follower count, and engagement rate of specific influencers. It can also extract necessary information from web pages using scraping techniques. For example, the data collection unit collects the results of past campaigns conducted by specific influencers and performs analysis based on that data. The data collection unit can also set the update frequency of the database and collect data regularly. This ensures that the data collection unit always has the latest data, allowing the analysis and proposal units to make decisions based on accurate information. Furthermore, the data collection unit can clean and normalize data to ensure data quality. For example, it has a function to automatically detect and correct duplicates and missing data in the collected data. This allows the data collection unit to provide high-quality data and improve the accuracy and reliability of the entire system.

[0031] The analytics department analyzes the data collected by the data collection department. For example, the analytics department can evaluate engagement and conversions. Specifically, it evaluates the performance of each influencer based on engagement metrics such as likes, comments, and shares. For instance, the analytics department analyzes the level of engagement a particular influencer has achieved. The analytics department can also evaluate influencer performance based on conversion metrics such as purchases, registrations, and click-through rates. For example, it can analyze the conversion rate achieved by a particular influencer. Furthermore, the analytics department can use AI to analyze data and gain more advanced insights. For example, it can use natural language processing technology to analyze the content of influencer posts and classify positive and negative reactions. It can also use machine learning algorithms to learn patterns from past data and predict future performance. This allows the analytics department to comprehensively evaluate influencer performance and provide information useful for a company's marketing strategy.

[0032] The Proposal Department proposes the most suitable influencers based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can list influencers best suited to a company's marketing strategy. Specifically, the Proposal Department selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. For example, the Proposal Department can list influencers who have high engagement with a specific target group. Furthermore, the Proposal Department can flexibly adjust the influencer selection criteria according to the company's brand image and the objectives of the campaign. For example, when promoting a product aimed at young people, they will prioritize selecting influencers popular with young people. The Proposal Department can also propose influencers who are expected to be highly effective in the future, taking into account their past performance and trends. This allows the Proposal Department to efficiently select the most suitable influencers for a company's marketing strategy and improve the success rate of the campaign.

[0033] The prediction unit predicts the effectiveness of influencers proposed by the proposal unit. For example, the prediction unit can predict the ROI of selected influencers. Specifically, it predicts how effective selected influencers will be based on historical data and current trends. For example, the prediction unit predicts the ROI of a campaign conducted by a particular influencer and makes optimal performance suggestions based on the results. Furthermore, the prediction unit can improve prediction accuracy using AI. For example, it can use machine learning algorithms to learn patterns from past campaign data and predict future performance with high accuracy. The prediction unit can also simulate multiple scenarios and identify the most likely risks and effects. In this way, the prediction unit can provide information that minimizes risks and maximizes effectiveness when companies select influencers.

[0034] The service provider provides a platform that allows companies to compare and evaluate influencers proposed by the proposal team. For example, the service provider can display detailed data and performance evaluations of the listed influencers. Specifically, it can compare each influencer's past campaign results, engagement rates, and conversion rates. For instance, the service provider displays influencer performance metrics and cost-effectiveness to facilitate easy comparison and evaluation. Furthermore, the service provider designs the user interface to be intuitive and easy for companies to use. For example, it can use drag-and-drop functionality to easily compare influencers. The service provider can also collect feedback from companies and continuously improve the platform's functionality and usability. This enables the service provider to help companies efficiently select the most suitable influencers and improve the success rate of their marketing campaigns.

[0035] The data collection unit can automatically collect social media data and historical campaign data. The unit collects data from various social media platforms, for example, using APIs or scraping techniques. The unit can collect data on the results of past campaigns conducted by specific influencers and perform analysis based on that data. The unit can also set the database update frequency and collect data regularly. This improves the efficiency of data collection by automatically collecting social media data and historical campaign data. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the unit can collect social media data using APIs and input that data into AI for analysis.

[0036] The analytics department can evaluate engagement and conversions. For example, the analytics department evaluates the performance of each influencer based on engagement metrics such as the number of likes, comments, and shares. The analytics department can analyze how much engagement a particular influencer has achieved. The analytics department can also evaluate the performance of influencers based on conversion metrics such as the number of purchases, registrations, and click-through rates. The analytics department can analyze what conversion rate a particular influencer has achieved. This allows for an accurate understanding of influencer performance by evaluating engagement and conversions. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input engagement metrics and conversion metrics into AI, which can then perform the analysis.

[0037] The proposal department can list the most suitable influencers for a company's marketing strategy. The proposal department selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. The proposal department can list influencers who have high engagement with a specific target group. This allows for effective campaigns by listing the most suitable influencers for a company's marketing strategy. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the number of followers and engagement rate of influencers into a generative AI, which can then list the most suitable influencers.

[0038] The prediction unit can predict the ROI of selected influencers. For example, the prediction unit predicts how effective the selected influencers will be based on historical data and current trends. The prediction unit can predict the ROI of a campaign conducted by a specific influencer and make optimal performance suggestions based on the results. This maximizes the return on investment by predicting the ROI of selected influencers. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or not using generative AI. For example, the prediction unit can input historical data and current trends into a generative AI, which can then predict the ROI.

[0039] The service provider can display detailed data and performance evaluations of the listed influencers. For example, the service provider can compare each influencer's past campaign results, engagement rates, and conversion rates. The service provider displays influencer performance metrics and cost-effectiveness so that companies can easily compare and consider them. This allows companies to easily compare and consider the listed influencers by displaying detailed data and performance evaluations. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input detailed data on influencers into a generative AI, which can then perform performance evaluations.

[0040] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past data collection history and collect data at that timing. Based on past data collection history, the data collection unit can select the optimal collection method (API, scraping, etc.). Furthermore, the data collection unit can analyze past data collection history and determine the priority of data to be collected. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, and the AI ​​can select the optimal collection method.

[0041] The data collection unit can filter data based on specific campaigns or events during data collection. For example, the data collection unit may target only data collected during a specific campaign period. The data collection unit can prioritize the collection of data related to specific events. Furthermore, the data collection unit can set filtering conditions for the data to be collected depending on the type of campaign or event. This allows for the collection of highly relevant data by filtering data based on specific campaigns or events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data related to specific campaigns or events into an AI, which can then perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. The data collection unit can also prioritize the collection of data related to a specific region. Furthermore, the data collection unit can filter the data to be collected based on geographical location information. This enables more accurate data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, which can then select highly relevant data.

[0043] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to specific hashtags on social media. The data collection unit can analyze the activity of specific users on social media and collect relevant data. The data collection unit can also analyze trends on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into AI, which can then collect relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific algorithm to engagement data. For conversion data, it can apply a different algorithm. The analysis unit can also select the optimal analysis algorithm depending on the data category. By applying the optimal analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can select the optimal analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze trends based on historical data. Furthermore, the analysis unit can determine the priority of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can determine the order of analysis.

[0048] The proposal function can adjust the level of detail in a proposal based on the influencer's importance. For example, it can provide detailed proposals to highly important influencers and simplified proposals to less important influencers. Furthermore, the proposal function can prioritize proposals based on the influencer's importance. This allows for more effective proposals by adjusting the level of detail based on the influencer's importance. Some or all of the above processing in the proposal function may be performed using AI, or not. For example, the proposal function can input the influencer's importance into the AI, which can then adjust the level of detail in the proposal.

[0049] The suggestion unit can apply different suggestion algorithms depending on the influencer's category when making suggestions. For example, the suggestion unit can apply a specific algorithm to influencers with high engagement. It can also apply a different algorithm to influencers with high conversion rates. Furthermore, the suggestion unit can select the optimal suggestion algorithm depending on the influencer's category. This improves the accuracy of suggestions by applying the most suitable suggestion algorithm according to the influencer's category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the influencer's category into the AI, which can then select the optimal suggestion algorithm.

[0050] The proposal department can prioritize proposals based on the influencer's activity period. For example, the proposal department might prioritize suggesting influencers who are currently active. The proposal department can also suggest the most suitable influencer based on their past activity history. Furthermore, the proposal department can prioritize proposals according to the influencer's activity period. This allows for more effective proposals by prioritizing proposals based on the influencer's activity period. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the influencer's activity period into an AI, which can then determine the proposal priority.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the influencers when making proposals. For example, the proposal unit can prioritize proposals for highly relevant influencers. The proposal unit can postpone proposals for less relevant influencers. The proposal unit can also determine the order of proposals according to the relevance of the influencers. This allows for more effective proposals by adjusting the order of proposals based on the relevance of the influencers. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of influencers into AI, and the AI ​​can determine the order of proposals.

[0052] The prediction unit can analyze past data and select the optimal prediction method during the prediction process. For example, the prediction unit can select the most effective prediction method based on past data. The prediction unit can analyze past data and improve the accuracy of the prediction. The prediction unit can also determine the priority of predictions based on past data. This improves the accuracy of the prediction by analyzing past data and selecting the optimal prediction method. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past data into AI, and the AI ​​can select the optimal prediction method.

[0053] The prediction unit can customize its prediction methods based on the current trend during the prediction process. For example, the prediction unit can select the optimal prediction method based on the current trend. The prediction unit can analyze trend data to improve the accuracy of the prediction. Furthermore, the prediction unit can customize the prediction methods according to the trend. By customizing the prediction methods based on the current trend, the accuracy of the prediction is improved. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input current trend data into the AI, which can then select the optimal prediction method.

[0054] The prediction unit can select the optimal prediction method by considering geographical location information during prediction. For example, the prediction unit can select the optimal prediction method based on the user's current location. The prediction unit can prioritize providing predictions related to a specific region. Furthermore, the prediction unit can customize the prediction method based on geographical location information. This improves the accuracy of predictions by selecting the optimal prediction method by considering geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input geographical location information into AI, and the AI ​​can select the optimal prediction method.

[0055] The prediction unit can analyze social media activity and propose prediction methods during the prediction process. For example, the prediction unit can provide predictions related to specific hashtags on social media. The prediction unit can analyze the activity of specific users on social media and provide relevant predictions. The prediction unit can also analyze trends on social media and provide relevant predictions. By analyzing social media activity and proposing prediction methods, the accuracy of predictions is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input social media activity into AI, and the AI ​​can provide relevant predictions.

[0056] The service provider can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the service provider can select the optimal interface display method based on the user's past operation history. The service provider can prioritize providing display methods that the user has used in the past. Furthermore, the service provider can analyze the user's operation history and propose the most efficient display method. This allows for a more user-friendly interface by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's operation history into AI, which can then select the optimal display method.

[0057] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. By selecting the optimal display method considering the user's device information, a more user-friendly interface can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0058] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. By selecting the optimal display method considering the user's device information, a more user-friendly interface can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0059] The service provider can refer to the user's calendar information and make suggestions based on their schedule when displaying the interface. For example, the service provider can refer to the schedule registered in the user's calendar and provide the optimal interface display method. The service provider can suggest a display method related to a specific event from the user's calendar information. Furthermore, the service provider can suggest the optimal display method tailored to the schedule based on the user's calendar information. This allows for the provision of a more appropriate interface by referring to the user's calendar information and making suggestions based on their schedule. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's calendar information into AI, which can then select the optimal display method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The influencer selection system can further analyze the user's past selection history to suggest the most suitable influencers. For example, it can list similar influencers based on the performance data of influencers previously selected. It can also analyze the characteristics of influencers that users have previously given high ratings to and suggest influencers with similar characteristics. Furthermore, it can prioritize suggesting influencers who have been successful in specific campaigns based on past selection history. This enables highly accurate influencer selection utilizing past selection history, improving the success rate of marketing campaigns.

[0062] The influencer selection system can further adjust its selection criteria based on the influencer's activities. For example, it can prioritize listing influencers who are active in a specific field. By analyzing the content and frequency of influencer posts, it can select the most suitable influencer for a company's marketing strategy. It can also adjust selection criteria based on the engagement rate and reactions of the influencer's followers. This enables highly accurate selection based on the influencer's activities, improving the effectiveness of marketing campaigns.

[0063] The influencer selection system can further analyze influencer content and adjust selection criteria. For example, it can prioritize listing influencers who post content containing specific keywords. It can also analyze influencer content text to select influencers that align with a company's marketing strategy. Furthermore, it can evaluate influencer posting frequency and quality, adjusting selection criteria accordingly. This enables highly accurate selection based on influencer content, improving the effectiveness of marketing campaigns.

[0064] The influencer selection system can further adjust its selection criteria by considering the influencer's geographical information. For example, it can prioritize listing influencers with high influence in a specific region. By analyzing the influencer's geographical information, it can select the most suitable influencer for a company's target region. It can also evaluate engagement rates and conversion rates for each region and adjust the selection criteria accordingly. This enables highly accurate influencer selection based on geographical information, improving the effectiveness of marketing campaigns.

[0065] The influencer selection system can further analyze influencers' past campaign data and adjust selection criteria. For example, it can prioritize listing influencers who have achieved high results in past campaigns. By thoroughly analyzing influencers' past campaign data, it can select the most suitable influencers for a company's marketing strategy. It can also select influencers who are effective for specific target groups based on past campaign data. This enables highly accurate influencer selection based on past campaign data, improving the effectiveness of marketing campaigns.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The data collection unit collects data. The data collection unit can automatically collect, for example, social media data and past campaign data. The data collection unit collects data from each social media platform using APIs or scraping techniques. For example, the data collection unit collects the results of past campaigns conducted by a specific influencer and performs analysis based on that data. The data collection unit can also set the database update frequency and collect data regularly. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department can evaluate, for example, engagement and conversions. The analysis department evaluates the performance of each influencer based on engagement metrics such as the number of likes, comments, and shares. For example, the analysis department analyzes how much engagement a particular influencer has achieved. The analysis department can also evaluate the performance of influencers based on conversion metrics such as the number of purchases, registrations, and click-through rates. For example, the analysis department analyzes what conversion rate a particular influencer has achieved. Step 3: The proposal team proposes the most suitable influencers based on the analysis results obtained by the analysis team. For example, the proposal team can list influencers who are best suited to a company's marketing strategy. The proposal team selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. For example, the proposal team can list influencers who have high engagement with a specific target group. Step 4: The prediction unit predicts the effectiveness of the influencers proposed by the proposal unit. For example, the prediction unit can predict the ROI of the selected influencers. Based on historical data and current trends, the prediction unit predicts how effective the selected influencers will be. For example, the prediction unit predicts the ROI of a campaign conducted by a specific influencer and makes optimal performance recommendations based on the results. Step 5: The provider team provides a platform that allows companies to compare and evaluate the influencers proposed by the proposal team. For example, the provider team can display detailed data and performance evaluations of the listed influencers. The provider team can compare each influencer's past campaign results, engagement rates, conversion rates, etc. For example, the provider team displays influencer performance metrics and cost-effectiveness so that companies can easily compare and evaluate them.

[0068] (Example of form 2) An influencer selection system according to an embodiment of the present invention is a system that uses AI to streamline influencer selection and improve the success rate of marketing campaigns. This influencer selection system automatically collects SNS data and past campaign data, and the AI ​​analyzes the collected data to evaluate engagement and conversion. Based on this, the AI ​​lists the most suitable influencers and provides a platform that companies can easily compare and consider. Furthermore, the AI ​​predicts the effectiveness of the selected influencers and makes performance suggestions to maximize ROI. This mechanism significantly reduces the effort required for influencer selection and enables highly accurate, data-driven selection. In addition, seamless selection aligned with strategy and performance improvement are realized, and it is expected that competitiveness will be strengthened, brand value will be enhanced, and revenue will be increased. For example, the influencer selection system automatically collects SNS data and past campaign data. At this time, data from each SNS platform is integrated and detailed data such as the results of past campaigns, engagement rates, and conversion rates are collected. For example, the results of campaigns conducted in the past by a specific influencer are collected and analyzed based on that data. Next, the collected data is analyzed by the AI. AI evaluates engagement and conversions to determine the performance of each influencer. For example, the AI ​​analyzes how much engagement a particular influencer achieved and what their conversion rate was. Furthermore, the AI ​​lists the most suitable influencers. Based on the collected data and analysis results, the AI ​​selects the influencers best suited to a company's marketing strategy. For example, it lists influencers who have high engagement with a specific target audience. The AI ​​then provides a platform that allows companies to easily compare and evaluate these influencers. On this platform, detailed data and performance evaluations of the listed influencers can be viewed. For example, it can compare each influencer's past campaign results, engagement rates, and conversion rates.Finally, the AI ​​predicts the effectiveness of the selected influencers and provides performance suggestions to maximize ROI. Based on historical data and current trends, the AI ​​predicts how effective the selected influencers will be. For example, it predicts the ROI of a campaign conducted by a specific influencer and provides optimal performance suggestions based on the results. This significantly reduces the effort required for influencer selection and enables highly accurate, data-driven selection. Furthermore, it enables seamless selection aligned with strategy and performance improvement, leading to enhanced competitiveness, increased brand value, and expanded revenue. In this way, the influencer selection system can streamline influencer selection and improve the success rate of marketing campaigns.

[0069] The influencer selection system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, a prediction unit, and a provision unit. The collection unit collects data. The collection unit can, for example, automatically collect SNS data and past campaign data. The collection unit collects data from each SNS platform using APIs or scraping techniques. For example, the collection unit collects the results of campaigns previously conducted by a specific influencer and performs analysis based on that data. The collection unit can also set the database update frequency and collect data periodically. The analysis unit analyzes the data collected by the collection unit. The analysis unit can, for example, evaluate engagement and conversions. The analysis unit evaluates the performance of each influencer based on engagement metrics such as the number of likes, comments, and shares. For example, the analysis unit analyzes how much engagement a specific influencer has gained. The analysis unit can also evaluate the performance of influencers based on conversion metrics such as the number of purchases, registrations, and click-through rates. For example, the analysis department analyzes the conversion rate achieved by a particular influencer. The proposal department proposes the most suitable influencers based on the analysis results obtained by the analysis department. The proposal department can, for example, list the most suitable influencers for a company's marketing strategy. The proposal department selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. For example, the proposal department lists influencers who have high engagement with a specific target group. The forecasting department predicts the effectiveness of the influencers proposed by the proposal department. The forecasting department can, for example, predict the ROI of the selected influencers. The forecasting department predicts how effective the selected influencers will be based on past data and current trends. For example, the forecasting department predicts the ROI of a campaign conducted by a particular influencer and makes optimal performance proposals based on the results. The provisioning department provides a platform that allows companies to compare and consider the influencers proposed by the proposal department.The service provider can, for example, display detailed data and performance evaluations of the listed influencers. The service provider can compare each influencer's past campaign results, engagement rates, and conversion rates. For example, the service provider can display influencer performance metrics and cost-effectiveness to allow companies to easily compare and consider them. As a result, the influencer selection system according to this embodiment can streamline influencer selection and improve the success rate of marketing campaigns.

[0070] The data collection unit collects data. For example, the data collection unit can automatically collect social media data and past campaign data. The data collection unit collects data from each social media platform using APIs and scraping techniques. Specifically, the data collection unit uses the APIs of each social media platform to obtain data such as the content of posts, follower count, and engagement rate of specific influencers. It can also extract necessary information from web pages using scraping techniques. For example, the data collection unit collects the results of past campaigns conducted by specific influencers and performs analysis based on that data. The data collection unit can also set the update frequency of the database and collect data regularly. This ensures that the data collection unit always has the latest data, allowing the analysis and proposal units to make decisions based on accurate information. Furthermore, the data collection unit can clean and normalize data to ensure data quality. For example, it has a function to automatically detect and correct duplicates and missing data in the collected data. This allows the data collection unit to provide high-quality data and improve the accuracy and reliability of the entire system.

[0071] The analytics department analyzes the data collected by the data collection department. For example, the analytics department can evaluate engagement and conversions. Specifically, it evaluates the performance of each influencer based on engagement metrics such as likes, comments, and shares. For instance, the analytics department analyzes the level of engagement a particular influencer has achieved. The analytics department can also evaluate influencer performance based on conversion metrics such as purchases, registrations, and click-through rates. For example, it can analyze the conversion rate achieved by a particular influencer. Furthermore, the analytics department can use AI to analyze data and gain more advanced insights. For example, it can use natural language processing technology to analyze the content of influencer posts and classify positive and negative reactions. It can also use machine learning algorithms to learn patterns from past data and predict future performance. This allows the analytics department to comprehensively evaluate influencer performance and provide information useful for a company's marketing strategy.

[0072] The Proposal Department proposes the most suitable influencers based on the analysis results obtained by the Analysis Department. For example, the Proposal Department can list influencers best suited to a company's marketing strategy. Specifically, the Proposal Department selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. For example, the Proposal Department can list influencers who have high engagement with a specific target group. Furthermore, the Proposal Department can flexibly adjust the influencer selection criteria according to the company's brand image and the objectives of the campaign. For example, when promoting a product aimed at young people, they will prioritize selecting influencers popular with young people. The Proposal Department can also propose influencers who are expected to be highly effective in the future, taking into account their past performance and trends. This allows the Proposal Department to efficiently select the most suitable influencers for a company's marketing strategy and improve the success rate of the campaign.

[0073] The prediction unit predicts the effectiveness of influencers proposed by the proposal unit. For example, the prediction unit can predict the ROI of selected influencers. Specifically, it predicts how effective selected influencers will be based on historical data and current trends. For example, the prediction unit predicts the ROI of a campaign conducted by a particular influencer and makes optimal performance suggestions based on the results. Furthermore, the prediction unit can improve prediction accuracy using AI. For example, it can use machine learning algorithms to learn patterns from past campaign data and predict future performance with high accuracy. The prediction unit can also simulate multiple scenarios and identify the most likely risks and effects. In this way, the prediction unit can provide information that minimizes risks and maximizes effectiveness when companies select influencers.

[0074] The service provider provides a platform that allows companies to compare and evaluate influencers proposed by the proposal team. For example, the service provider can display detailed data and performance evaluations of the listed influencers. Specifically, it can compare each influencer's past campaign results, engagement rates, and conversion rates. For instance, the service provider displays influencer performance metrics and cost-effectiveness to facilitate easy comparison and evaluation. Furthermore, the service provider designs the user interface to be intuitive and easy for companies to use. For example, it can use drag-and-drop functionality to easily compare influencers. The service provider can also collect feedback from companies and continuously improve the platform's functionality and usability. This enables the service provider to help companies efficiently select the most suitable influencers and improve the success rate of their marketing campaigns.

[0075] The data collection unit can automatically collect social media data and historical campaign data. The unit collects data from various social media platforms, for example, using APIs or scraping techniques. The unit can collect data on the results of past campaigns conducted by specific influencers and perform analysis based on that data. The unit can also set the database update frequency and collect data regularly. This improves the efficiency of data collection by automatically collecting social media data and historical campaign data. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the unit can collect social media data using APIs and input that data into AI for analysis.

[0076] The analytics department can evaluate engagement and conversions. For example, the analytics department evaluates the performance of each influencer based on engagement metrics such as the number of likes, comments, and shares. The analytics department can analyze how much engagement a particular influencer has achieved. The analytics department can also evaluate the performance of influencers based on conversion metrics such as the number of purchases, registrations, and click-through rates. The analytics department can analyze what conversion rate a particular influencer has achieved. This allows for an accurate understanding of influencer performance by evaluating engagement and conversions. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input engagement metrics and conversion metrics into AI, which can then perform the analysis.

[0077] The proposal department can list the most suitable influencers for a company's marketing strategy. The proposal department selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. The proposal department can list influencers who have high engagement with a specific target group. This allows for effective campaigns by listing the most suitable influencers for a company's marketing strategy. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or not. For example, the proposal department can input the number of followers and engagement rate of influencers into a generative AI, which can then list the most suitable influencers.

[0078] The prediction unit can predict the ROI of selected influencers. For example, the prediction unit predicts how effective the selected influencers will be based on historical data and current trends. The prediction unit can predict the ROI of a campaign conducted by a specific influencer and make optimal performance suggestions based on the results. This maximizes the return on investment by predicting the ROI of selected influencers. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or not using generative AI. For example, the prediction unit can input historical data and current trends into a generative AI, which can then predict the ROI.

[0079] The service provider can display detailed data and performance evaluations of the listed influencers. For example, the service provider can compare each influencer's past campaign results, engagement rates, and conversion rates. The service provider displays influencer performance metrics and cost-effectiveness so that companies can easily compare and consider them. This allows companies to easily compare and consider the listed influencers by displaying detailed data and performance evaluations. Some or all of the above processing in the service provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the service provider can input detailed data on influencers into a generative AI, which can then perform performance evaluations.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit will refrain from collecting data and resume collection when the user is relaxed. If the user is relaxed, the data collection unit will actively collect data to obtain detailed information. Also, if the user is in a hurry, the data collection unit can quickly collect only the minimum necessary data. By adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0081] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past data collection history and collect data at that timing. Based on past data collection history, the data collection unit can select the optimal collection method (API, scraping, etc.). Furthermore, the data collection unit can analyze past data collection history and determine the priority of data to be collected. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into AI, and the AI ​​can select the optimal collection method.

[0082] The data collection unit can filter data based on specific campaigns or events during data collection. For example, the data collection unit may target only data collected during a specific campaign period. The data collection unit can prioritize the collection of data related to specific events. Furthermore, the data collection unit can set filtering conditions for the data to be collected depending on the type of campaign or event. This allows for the collection of highly relevant data by filtering data based on specific campaigns or events. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input data related to specific campaigns or events into an AI, which can then perform the filtering.

[0083] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. If the user is relaxed, the data collection unit can prioritize collecting detailed data. If the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. In this way, important data can be collected preferentially by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0084] The data collection unit can prioritize the collection of highly relevant data by considering geographical location information during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's current location. The data collection unit can also prioritize the collection of data related to a specific region. Furthermore, the data collection unit can filter the data to be collected based on geographical location information. This enables more accurate data collection by prioritizing the collection of highly relevant data by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input geographical location information into AI, which can then select highly relevant data.

[0085] The data collection unit can analyze social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to specific hashtags on social media. The data collection unit can analyze the activity of specific users on social media and collect relevant data. The data collection unit can also analyze trends on social media and collect relevant data. This allows for the efficient collection of relevant data by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media activity into AI, which can then collect relevant data.

[0086] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. Furthermore, the analysis unit can determine the priority of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the importance of the data into the AI, which can then adjust the level of detail of the analysis.

[0088] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific algorithm to engagement data. For conversion data, it can apply a different algorithm. The analysis unit can also select the optimal analysis algorithm depending on the data category. By applying the optimal analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can select the optimal analysis algorithm.

[0089] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0090] The analysis unit can determine the priority of analysis based on the data collection timing. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit can also analyze trends based on historical data. Furthermore, the analysis unit can determine the priority of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis.

[0091] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. The analysis unit can postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can determine the order of analysis.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can present simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can present detailed suggestions. If the user is in a hurry, the suggestion unit can present concise suggestions. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotion.

[0093] The proposal function can adjust the level of detail in a proposal based on the influencer's importance. For example, it can provide detailed proposals to highly important influencers and simplified proposals to less important influencers. Furthermore, the proposal function can prioritize proposals based on the influencer's importance. This allows for more effective proposals by adjusting the level of detail based on the influencer's importance. Some or all of the above processing in the proposal function may be performed using AI, or not. For example, the proposal function can input the influencer's importance into the AI, which can then adjust the level of detail in the proposal.

[0094] The suggestion unit can apply different suggestion algorithms depending on the influencer's category when making suggestions. For example, the suggestion unit can apply a specific algorithm to influencers with high engagement. It can also apply a different algorithm to influencers with high conversion rates. Furthermore, the suggestion unit can select the optimal suggestion algorithm depending on the influencer's category. This improves the accuracy of suggestions by applying the most suitable suggestion algorithm according to the influencer's category. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the influencer's category into the AI, which can then select the optimal suggestion algorithm.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be made. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can then estimate the emotions.

[0096] The proposal department can prioritize proposals based on the influencer's activity period. For example, the proposal department might prioritize suggesting influencers who are currently active. The proposal department can also suggest the most suitable influencer based on their past activity history. Furthermore, the proposal department can prioritize proposals according to the influencer's activity period. This allows for more effective proposals by prioritizing proposals based on the influencer's activity period. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the influencer's activity period into an AI, which can then determine the proposal priority.

[0097] The proposal unit can adjust the order of proposals based on the relevance of the influencers when making proposals. For example, the proposal unit can prioritize proposals for highly relevant influencers. The proposal unit can postpone proposals for less relevant influencers. The proposal unit can also determine the order of proposals according to the relevance of the influencers. This allows for more effective proposals by adjusting the order of proposals based on the relevance of the influencers. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the relevance of influencers into AI, and the AI ​​can determine the order of proposals.

[0098] The prediction unit can estimate the user's emotions and adjust its prediction method based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and easy-to-understand prediction result. If the user is relaxed, the prediction unit can provide a detailed prediction result. Also, if the user is in a hurry, the prediction unit can provide a concise prediction result. By adjusting the prediction method according to the user's emotions, more appropriate predictions become possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0099] The prediction unit can analyze past data and select the optimal prediction method during the prediction process. For example, the prediction unit can select the most effective prediction method based on past data. The prediction unit can analyze past data and improve the accuracy of the prediction. The prediction unit can also determine the priority of predictions based on past data. This improves the accuracy of the prediction by analyzing past data and selecting the optimal prediction method. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input past data into AI, and the AI ​​can select the optimal prediction method.

[0100] The prediction unit can customize its prediction methods based on the current trend during the prediction process. For example, the prediction unit can select the optimal prediction method based on the current trend. The prediction unit can analyze trend data to improve the accuracy of the prediction. Furthermore, the prediction unit can customize the prediction methods according to the trend. By customizing the prediction methods based on the current trend, the accuracy of the prediction is improved. Some or all of the above-described processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input current trend data into the AI, which can then select the optimal prediction method.

[0101] The prediction unit can estimate the user's emotions and determine the priority of predictions based on the estimated emotions. For example, if the user is nervous, the prediction unit can prioritize providing high-importance predictions. If the user is relaxed, the prediction unit can prioritize providing detailed predictions. Also, if the user is in a hurry, the prediction unit can prioritize providing predictions that can be delivered quickly. This allows for more appropriate predictions by prioritizing predictions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, or not using AI. For example, the prediction unit can input user emotion data into a generative AI, which can then perform emotion estimation.

[0102] The prediction unit can select the optimal prediction method by considering geographical location information during prediction. For example, the prediction unit can select the optimal prediction method based on the user's current location. The prediction unit can prioritize providing predictions related to a specific region. Furthermore, the prediction unit can customize the prediction method based on geographical location information. This improves the accuracy of predictions by selecting the optimal prediction method by considering geographical location information. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input geographical location information into AI, and the AI ​​can select the optimal prediction method.

[0103] The prediction unit can analyze social media activity and propose prediction methods during the prediction process. For example, the prediction unit can provide predictions related to specific hashtags on social media. The prediction unit can analyze the activity of specific users on social media and provide relevant predictions. The prediction unit can also analyze trends on social media and provide relevant predictions. By analyzing social media activity and proposing prediction methods, the accuracy of predictions is improved. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input social media activity into AI, and the AI ​​can provide relevant predictions.

[0104] The service provider can estimate the user's emotions and adjust the interface display based on the estimated emotions. For example, if the user is tense, the service provider can provide a simple and highly visible interface. If the user is relaxed, the service provider can provide an interface with detailed information. If the user is in a hurry, the service provider can provide a concise interface. By adjusting the interface display according to the user's emotions, a more appropriate interface can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, which can then perform emotion estimation.

[0105] The service provider can select the optimal display method by referring to the user's past operation history when displaying the interface. For example, the service provider can select the optimal interface display method based on the user's past operation history. The service provider can prioritize providing display methods that the user has used in the past. Furthermore, the service provider can analyze the user's operation history and propose the most efficient display method. This allows for a more user-friendly interface by selecting the optimal display method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's operation history into AI, which can then select the optimal display method.

[0106] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. By selecting the optimal display method considering the user's device information, a more user-friendly interface can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0107] The service provider can estimate the user's emotions and adjust the interface's operating procedures based on the estimated emotions. For example, if the user is tense, the service provider can provide simple and intuitive operating procedures. If the user is relaxed, the service provider can provide detailed operating procedures. Furthermore, if the user is in a hurry, the service provider can provide procedures that allow for quick operation. In this way, a more user-friendly interface can be provided by adjusting the interface's operating procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0108] The service provider can select the optimal display method when displaying the interface, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. If the user is using a tablet, the service provider can provide a display method optimized for a larger screen. Furthermore, if the user is using a smartwatch, the service provider can provide a concise and highly visible display method. By selecting the optimal display method considering the user's device information, a more user-friendly interface can be provided. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's device information into AI, and the AI ​​can select the optimal display method.

[0109] The service provider can refer to the user's calendar information and make suggestions based on their schedule when displaying the interface. For example, the service provider can refer to the schedule registered in the user's calendar and provide the optimal interface display method. The service provider can suggest a display method related to a specific event from the user's calendar information. Furthermore, the service provider can suggest the optimal display method tailored to the schedule based on the user's calendar information. This allows for the provision of a more appropriate interface by referring to the user's calendar information and making suggestions based on their schedule. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's calendar information into AI, which can then select the optimal display method.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The influencer selection system can further estimate the user's emotions and adjust the influencer selection criteria based on those emotions. For example, if a user is stressed, the system will prioritize listing influencers with high engagement rates. If a user is relaxed, the system can prioritize selecting influencers with high ROI. Furthermore, if a user is in a hurry, the system can provide immediate suggestions based on past data to deliver results quickly. This enables flexible influencer selection tailored to the user's emotions, leading to more effective marketing campaigns.

[0112] The influencer selection system can further analyze the user's past selection history to suggest the most suitable influencers. For example, it can list similar influencers based on the performance data of influencers previously selected. It can also analyze the characteristics of influencers that users have previously given high ratings to and suggest influencers with similar characteristics. Furthermore, it can prioritize suggesting influencers who have been successful in specific campaigns based on past selection history. This enables highly accurate influencer selection utilizing past selection history, improving the success rate of marketing campaigns.

[0113] The influencer selection system can further estimate the user's emotions and adjust the influencer's performance evaluation based on those emotions. For example, if the user is nervous, the system provides a simple and easy-to-understand evaluation result. If the user is relaxed, the system can provide a detailed evaluation result. If the user is in a hurry, the system can provide a concise evaluation result. This enables flexible performance evaluation that responds to the user's emotions, resulting in more appropriate influencer selection.

[0114] The influencer selection system can further adjust its selection criteria based on the influencer's activities. For example, it can prioritize listing influencers who are active in a specific field. By analyzing the content and frequency of influencer posts, it can select the most suitable influencer for a company's marketing strategy. It can also adjust selection criteria based on the engagement rate and reactions of the influencer's followers. This enables highly accurate selection based on the influencer's activities, improving the effectiveness of marketing campaigns.

[0115] The influencer selection system can further estimate the user's emotions and adjust the influencer's effectiveness prediction based on those emotions. For example, if the user is stressed, the system provides a simple and easy-to-understand prediction. If the user is relaxed, the system can provide a detailed prediction. If the user is in a hurry, the system can provide a concise prediction. This enables flexible effectiveness predictions that respond to the user's emotions, resulting in more appropriate influencer selection.

[0116] The influencer selection system can further analyze the sentiment of influencers' followers and adjust the selection criteria. For example, it can prioritize listing influencers whose followers have positive sentiments. It can analyze follower comments and reactions to evaluate the degree of positivity. It can also exclude influencers whose followers have negative sentiments. This enables highly accurate influencer selection based on follower sentiments, improving the effectiveness of marketing campaigns.

[0117] The influencer selection system can further analyze influencer content and adjust selection criteria. For example, it can prioritize listing influencers who post content containing specific keywords. It can also analyze influencer content text to select influencers that align with a company's marketing strategy. Furthermore, it can evaluate influencer posting frequency and quality, adjusting selection criteria accordingly. This enables highly accurate selection based on influencer content, improving the effectiveness of marketing campaigns.

[0118] The influencer selection system can further estimate the user's emotions and adjust how it displays the influencer selection results based on those emotions. For example, if the user is nervous, the system can provide a simple and easy-to-read display. If the user is relaxed, the system can provide a more detailed display. If the user is in a hurry, the system can provide a concise display. This allows for flexible display methods tailored to the user's emotions, resulting in more appropriate influencer selection.

[0119] The influencer selection system can further adjust its selection criteria by considering the influencer's geographical information. For example, it can prioritize listing influencers with high influence in a specific region. By analyzing the influencer's geographical information, it can select the most suitable influencer for a company's target region. It can also evaluate engagement rates and conversion rates for each region and adjust the selection criteria accordingly. This enables highly accurate influencer selection based on geographical information, improving the effectiveness of marketing campaigns.

[0120] The influencer selection system can further analyze influencers' past campaign data and adjust selection criteria. For example, it can prioritize listing influencers who have achieved high results in past campaigns. By thoroughly analyzing influencers' past campaign data, it can select the most suitable influencers for a company's marketing strategy. It can also select influencers who are effective for specific target groups based on past campaign data. This enables highly accurate influencer selection based on past campaign data, improving the effectiveness of marketing campaigns.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The data collection unit collects data. The data collection unit can automatically collect, for example, social media data and past campaign data. The data collection unit collects data from each social media platform using APIs or scraping techniques. For example, the data collection unit collects the results of past campaigns conducted by a specific influencer and performs analysis based on that data. The data collection unit can also set the database update frequency and collect data regularly. Step 2: The analysis department analyzes the data collected by the data collection department. The analysis department can evaluate, for example, engagement and conversions. The analysis department evaluates the performance of each influencer based on engagement metrics such as the number of likes, comments, and shares. For example, the analysis department analyzes how much engagement a particular influencer has achieved. The analysis department can also evaluate the performance of influencers based on conversion metrics such as the number of purchases, registrations, and click-through rates. For example, the analysis department analyzes what conversion rate a particular influencer has achieved. Step 3: The proposal team proposes the most suitable influencers based on the analysis results obtained by the analysis team. For example, the proposal team can list influencers who are best suited to a company's marketing strategy. The proposal team selects the most suitable influencers based on criteria such as the number of followers, engagement rate, and degree of match with the target audience. For example, the proposal team can list influencers who have high engagement with a specific target group. Step 4: The prediction unit predicts the effectiveness of the influencers proposed by the proposal unit. For example, the prediction unit can predict the ROI of the selected influencers. Based on historical data and current trends, the prediction unit predicts how effective the selected influencers will be. For example, the prediction unit predicts the ROI of a campaign conducted by a specific influencer and makes optimal performance recommendations based on the results. Step 5: The provider team provides a platform that allows companies to compare and evaluate the influencers proposed by the proposal team. For example, the provider team can display detailed data and performance evaluations of the listed influencers. The provider team can compare each influencer's past campaign results, engagement rates, conversion rates, etc. For example, the provider team displays influencer performance metrics and cost-effectiveness so that companies can easily compare and evaluate them.

[0123] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0126] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, prediction unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and automatically collects SNS data and past campaign data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates engagement and conversion based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists the most suitable influencers for the company's marketing strategy. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the effectiveness of the selected influencers. The provision unit is implemented by the control unit 46A of the smart device 14 and provides a platform that allows the company to compare and consider influencers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, prediction unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and automatically collects SNS data and past campaign data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates engagement and conversion based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists the most suitable influencers for the company's marketing strategy. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the effectiveness of the selected influencers. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides a platform for companies to compare and consider influencers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, prediction unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and automatically collects SNS data and past campaign data. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates engagement and conversion based on the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and lists the most suitable influencers for the company's marketing strategy. The prediction unit is implemented by the specific processing unit 290 of the data processing unit 12 and predicts the effectiveness of the selected influencers. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides a platform that allows the company to compare and consider influencers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 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.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, prediction unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and automatically collects SNS data and historical campaign data. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and evaluates engagement and conversion based on the collected data. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and lists the most suitable influencers for the company's marketing strategy. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and predicts the effectiveness of the selected influencers. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and provides a platform that allows the company to compare and consider influencers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0176] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable influencer. A prediction unit that predicts the effect of the influencer proposed by the proposal unit, The system comprises a provisioning unit that provides a platform for companies to compare and evaluate the influencers proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Automatically collects social media data and past campaign data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Evaluating engagement and conversions The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, List the most suitable influencers for your company's marketing strategy. The system described in Appendix 1, characterized by the features described herein. (Note 5) The prediction unit, Predicting the ROI of selected influencers The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Display detailed data and performance ratings for the listed influencers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filter it based on specific campaigns and events. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, prioritize the collection of highly relevant data, taking geographical location information into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, social media activity is analyzed and relevant data is gathered. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the influencer. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the influencer's category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, prioritize it based on the influencer's activity schedule. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the influencers. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, It estimates the user's emotions and adjusts the prediction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The prediction unit, When making predictions, historical data is analyzed to select the optimal prediction method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The prediction unit, When making predictions, customize the prediction method based on the current trend. The system described in Appendix 1, characterized by the features described herein. (Note 28) The prediction unit, It estimates the user's emotions and determines the priority of predictions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, When making predictions, the optimal prediction method is selected by considering geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When making predictions, we analyze social media activity and propose methods for making predictions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface display based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When displaying the interface, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, It estimates the user's emotions and adjusts the interface operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When displaying the interface, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When displaying the interface, the system references the user's calendar information to provide schedule-based suggestions. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the most suitable influencer. A prediction unit that predicts the effect of the influencer proposed by the proposal unit, The system comprises a provisioning unit that provides a platform for companies to compare and evaluate the influencers proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Automatically collects social media data and past campaign data. The system according to feature 1.

3. The aforementioned analysis unit is Evaluating engagement and conversions The system according to feature 1.

4. The aforementioned proposal section is, List the most suitable influencers for your company's marketing strategy. The system according to feature 1.

5. The prediction unit, Predicting the ROI of selected influencers The system according to feature 1.

6. The aforementioned supply unit is, Display detailed data and performance ratings for the listed influencers. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filter it based on specific campaigns and events. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images