system

Abstract

The system according to this embodiment aims to analyze trend data, generate optimal hashtags, and present an optimal posting schedule based on the online activity time and interests of followers. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a generation unit, an analysis unit, and a presentation unit. The acquisition unit acquires trend data. The analysis unit analyzes the data acquired by the acquisition unit and identifies keywords and topics. The generation unit generates hashtags based on the keywords and topics identified by the analysis unit. The analysis unit analyzes the online activity time and interests of followers. The presentation unit presents an optimal posting schedule based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to post content that attracts interest on a corporate account, it is impossible to grasp trends, and it is difficult to select appropriate hashtags and effectively send messages to target audiences.

[0005] The system according to the embodiment aims to analyze trend data, generate optimal hashtags, and present an optimal posting schedule based on the online activity time and interests of followers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, an analysis unit, and a presentation unit. The acquisition unit acquires trend data. The analysis unit analyzes the data acquired by the acquisition unit and identifies keywords and topics. The generation unit generates hashtags based on the keywords and topics identified by the analysis unit. The analysis unit analyzes the online activity time and interests of followers. The presentation unit presents an optimal posting schedule based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze trend data, generate optimal hashtags, and suggest an optimal posting schedule based on the online activity time and interests of its followers. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages 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] <000> The smart device 14 comprises a computer 36, a receiving 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 receiving 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 AI agent system according to an embodiment of the present invention is a system that performs trend analysis, hashtag optimization, and follower behavior analysis on SNS posts of corporate accounts. This AI agent system is configured to solve the problems that corporate accounts face, such as difficulty in creating engaging posts, difficulty in grasping trends and selecting appropriate hashtags, and difficulty in effectively delivering messages to target audiences. First, the AI ​​agent system uses an API to acquire trend data and related posts from SNS platforms in real time. Next, the AI ​​agent system utilizes the latest natural language processing technology to classify the acquired text data and identify keywords and topics. This allows for rapid identification of new trends related to products and industries. Furthermore, the AI ​​agent system analyzes the company's product information and past posting data to dynamically generate hashtags that match the characteristics of the product and themes that lead to popularity. This optimizes hashtags, improves content visibility, and strengthens brand awareness. The AI ​​agent system also analyzes followers' online activity time and interests and automatically suggests the optimal posting schedule. In addition, the AI ​​agent system utilizes a machine learning model that learns patterns of successful cases based on past posting data to predict the optimal content for the next post. This enables the proposal of posting strategies based on scientific methods, resulting in an effective approach to consumers. For example, the AI ​​agent system analyzes a company's product information and past posting data to dynamically generate hashtags that match product characteristics and themes that contribute to their popularity. This optimizes hashtags, improves content visibility, and strengthens brand awareness. The AI ​​agent system also analyzes followers' online activity time and interests to automatically suggest the optimal posting schedule. Furthermore, the AI ​​agent system utilizes a machine learning model that learns patterns from successful cases based on past posting data to predict the optimal content for the next post. This enables the proposal of posting strategies based on scientific methods, resulting in an effective approach to consumers.This allows AI agent systems to improve the accuracy of strategies and execution, and enhance marketing ROI, based on data-driven facts.

[0029] The AI ​​agent system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, an analysis unit, and a presentation unit. The acquisition unit acquires trend data. The acquisition unit acquires trend data and related posts from an SNS platform in real time, for example, using an API. The acquisition unit can acquire trend data by utilizing an API provided by an SNS platform. The analysis unit analyzes the data acquired by the acquisition unit and identifies keywords and topics. The analysis unit classifies the acquired text data and identifies keywords and topics, for example, by utilizing natural language processing technology. Natural language processing technology can include, for example, morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates hashtags based on the keywords and topics identified by the analysis unit. The generation unit analyzes, for example, a company's product information and past posting data to dynamically generate hashtags that match the characteristics of the product and themes that lead to its popularity. The generation unit can generate hashtags by utilizing keyword combinations or popular hashtags, for example. The analysis unit analyzes the online activity time and interests of followers. The analysis unit, for example, analyzes follower log data to identify online activity times and interests. The analysis unit can also identify interests by conducting follower surveys. The presentation unit presents an optimal posting schedule based on the analysis results obtained by the analysis unit. The presentation unit presents an optimal posting schedule based, for example, on the peak activity times of followers and reactions to past posts. The presentation unit can automatically adjust the posting schedule to match the peak activity times of followers. As a result, the AI ​​agent system according to the embodiment can consistently perform everything from acquiring and analyzing trend data to generating hashtags, analyzing follower behavior, and presenting posting schedules. Some or all of the above-described processes in the AI ​​agent system may be performed using AI, for example, or without AI. For example, the AI ​​agent system can input data acquired by the acquisition unit into the AI ​​and have the AI ​​perform data analysis and hashtag generation.

[0030] The data acquisition unit acquires trend data. For example, the data acquisition unit uses APIs to acquire trend data and related posts from social media platforms in real time. Specifically, the data acquisition unit uses APIs provided by social media platforms to acquire trend data. This allows the data acquisition unit to collect currently trending keywords, hashtags, and related posts on social media in real time. The data acquisition unit centrally manages the data acquired through APIs and stores it in a database. Furthermore, the data acquisition unit can flexibly set the frequency and scope of data acquisition. For example, it can acquire data only during specific time periods or in specific regions. This allows the data acquisition unit to efficiently collect the necessary data and improve the overall system performance. The data acquisition unit also has a function to monitor the data acquisition status and issue alerts if an anomaly occurs. This allows the data acquisition unit to achieve stable data collection and improve the reliability of the system.

[0031] The analysis unit analyzes the data acquired by the acquisition unit to identify keywords and topics. For example, the analysis unit utilizes natural language processing techniques to classify the acquired text data and identify keywords and topics. Specifically, the analysis unit uses morphological analysis to divide text data into words and grammatical analysis to analyze sentence structure. Furthermore, it uses semantic analysis to understand the meaning of sentences and extract important keywords and topics. By combining these natural language processing techniques, the analysis unit can analyze the content of the acquired data in detail and identify trending keywords and topics. The analysis unit can also perform data analysis using AI. For example, it can use a deep learning model to learn the features of text data and achieve highly accurate keyword extraction and topic classification. This allows the analysis unit to analyze large amounts of data quickly and accurately, and respond immediately to changes in trends. The analysis unit can also predict trends based on past data. This allows for prediction of future trends and the implementation of early countermeasures.

[0032] The generation unit generates hashtags based on keywords and topics identified by the analysis unit. For example, the generation unit analyzes a company's product information and past posting data to dynamically generate hashtags that match the product's characteristics and themes that contribute to its popularity. Specifically, the generation unit generates effective hashtags using keyword combinations and popular hashtags. The generation unit can also generate hashtags using AI. For example, it can automatically generate hashtags related to keywords and topics using natural language generation technology. This allows the generation unit to quickly generate effective hashtags that are in line with trends, increasing their virality on social media. Furthermore, the generation unit has a function to evaluate the effectiveness of the generated hashtags and modify them as needed. This allows the generation unit to always provide the optimal hashtags and maximize the effectiveness of social media marketing.

[0033] The analytics department analyzes followers' online activity time and interests. For example, the analytics department analyzes follower log data to identify online activity time and interests. Specifically, the analytics department analyzes log data such as follower posting times, browsing history, and like and share history to identify follower online activity patterns. Furthermore, it can also conduct follower surveys to identify interests and concerns. The analytics department can use AI to analyze follower behavior data and identify followers' interests and concerns with high accuracy. For example, it can use clustering technology to group followers based on their interests and concerns and analyze the characteristics of each group. This allows the analytics department to target followers based on their interests and concerns and develop effective marketing strategies. In addition, the analytics department can identify followers' online activity times and suggest optimal posting times. This maximizes post engagement and enhances the effectiveness of social media marketing.

[0034] The presentation unit presents the optimal posting schedule based on the analysis results obtained by the analysis unit. For example, the presentation unit presents the optimal posting schedule based on follower activity peak times and reactions to past posts. Specifically, the presentation unit automatically adjusts the posting schedule to match follower activity peak times. The presentation unit can calculate the optimal posting schedule using AI. For example, it uses machine learning algorithms to learn from past posting data and follower activity data to predict the optimal posting time. This allows the presentation unit to provide a posting schedule that matches follower activity peak times, maximizing post engagement. The presentation unit also has a function to evaluate the effectiveness of the posting schedule and modify the schedule as needed. This allows the presentation unit to always provide the optimal posting schedule and maximize the effectiveness of social media marketing.

[0035] The acquisition unit can acquire trend data and related posts from SNS platforms in real time using the SNS platform's API. For example, the acquisition unit can acquire trend data from SNS platforms using the SNS platform's API. The acquisition unit can also acquire related posts from SNS platforms using the SNS platform's API. The acquisition unit can also acquire trend data from SNS platforms in real time using the ISNS platform's API. This allows for the acquisition of the latest information by acquiring trend data in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the data acquired using the API into AI and have AI perform data analysis.

[0036] The analysis unit can classify acquired text data and identify keywords and topics by utilizing natural language processing techniques. For example, the analysis unit can use morphological analysis to divide the text data into words. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the text data. The analysis unit can also use semantic analysis to analyze the meaning of the text data. This improves the accuracy of text data analysis by utilizing natural language processing techniques. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input acquired text data into AI and have the AI ​​perform keyword and topic identification.

[0037] The generation unit can analyze a company's product information and past posting data to dynamically generate hashtags that match the product's characteristics and themes that contribute to its popularity. For example, the generation unit can analyze a company's product catalog and generate hashtags based on the product's characteristics. The generation unit can also analyze past posting history and generate hashtags based on popular themes. The generation unit can also generate hashtags using keyword combinations. By generating hashtags that match the product's characteristics and themes that contribute to its popularity, the visibility of the content is improved. The company's product information and past posting data include, but are not limited to, product catalogs and social media posting history. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the company's product information and past posting data into AI and have the AI ​​perform hashtag generation.

[0038] The analytics department can analyze followers' online activity time and interests. For example, the analytics department can analyze followers' log data to identify their online activity time. The analytics department can also conduct follower surveys to identify their interests. The analytics department can also analyze followers' social media behavior to identify their interests. By analyzing followers' online activity time and interests, it is possible to suggest an optimal posting schedule. The analysis of followers' online activity time and interests includes, but is not limited to, log data analysis and surveys. 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 follower log data into an AI and have the AI ​​perform the identification of online activity time and interests.

[0039] The presentation unit can present an optimal posting schedule based on the analysis results obtained by the analysis unit. The presentation unit can, for example, automatically adjust the posting schedule to match the peak activity times of followers. The presentation unit can also present an optimal posting schedule based on, for example, the reactions to past posts. The presentation unit can also automatically adjust the posting schedule based on, for example, the peak activity times of followers and the reactions to past posts. This enables effective message delivery by presenting an optimal posting schedule. An optimal posting schedule includes, but is not limited to, the peak activity times of followers and the reactions to past posts. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the analysis results obtained by the analysis unit into AI and have the AI ​​perform the task of presenting an optimal posting schedule.

[0040] The generation unit can predict the next optimal post content by utilizing a machine learning model that learns patterns of successful cases based on past posting data. For example, the generation unit analyzes past posting data and identifies patterns of successful cases. The generation unit can also learn patterns of successful cases using a machine learning model, for example. The generation unit can also learn patterns of successful cases using a neural network, for example. This allows it to predict the next optimal post content by utilizing a machine learning model. Machine learning models include, but are not limited to, neural networks and support vector machines. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past posting data into an AI and have the AI ​​learn patterns of successful cases.

[0041] The acquisition unit can analyze the acquisition history of past trend data and select the optimal acquisition method. For example, the acquisition unit can analyze the acquisition frequency of past trend data and set the optimal acquisition interval. For example, the acquisition unit can analyze the time period of past trend data acquisition and acquire data at the most effective time period. For example, the acquisition unit can analyze the acquisition method of past trend data and select the most efficient API. In this way, the optimal acquisition method can be selected by analyzing the past acquisition history. The optimal acquisition method includes, but is not limited to, the results of the analysis of past data and the algorithm to be used. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the acquisition history of past trend data into AI and have AI select the optimal acquisition method.

[0042] The acquisition unit can filter trend data based on specific industries or regions when acquiring it. For example, the acquisition unit can acquire only trend data related to a specific industry. The acquisition unit can also acquire only trend data related to a specific region. For example, the acquisition unit can filter and acquire trend data related to both industry and region. This allows for the acquisition of highly relevant data by filtering based on specific industries or regions. Specific industries and regions include, but are not limited to, industry classification criteria or regional scope. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have AI perform the filtering process based on specific industries or regions when acquiring trend data.

[0043] The acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring trend data. For example, the acquisition unit can acquire highly relevant trend data based on the user's current location. The acquisition unit can also acquire highly relevant trend data based on the user's past travel history. For example, the acquisition unit can update the user's geographical location information in real time to acquire the most relevant trend data. This allows for the priority acquisition of highly relevant data by considering the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's geographical location information into AI and have AI acquire highly relevant data.

[0044] The acquisition unit can analyze a user's social media activity and acquire relevant data when acquiring trend data. For example, the acquisition unit can analyze a user's past posts and acquire relevant trend data. The acquisition unit can also analyze the activity of a user's followers and acquire relevant trend data. The acquisition unit can also analyze a user's interests on social media and acquire relevant trend data. In this way, relevant data can be acquired by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posting frequency, the number of likes and shares, etc. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's social media activity data into AI and have the AI ​​perform the acquisition of relevant data.

[0045] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Data importance includes, but is not limited to, data frequency and relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data importance into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0046] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit can also apply a speech analysis algorithm to speech data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, text data, image data, and speech data. Some or all of the above processing 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 have the AI ​​perform the application of an appropriate analysis algorithm.

[0047] The analysis unit can determine the priority of analysis based on the data acquisition time during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the most recent data while referring to past data. The analysis unit may also dynamically adjust the priority of analysis according to the data acquisition time. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data acquisition time. The data acquisition time includes, but is not limited to, data timestamps and acquisition frequency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition time into the AI ​​and have the AI ​​determine the priority of analysis.

[0048] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust 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. The relevance of the data includes, but is not limited to, co-occurrence network analysis and correlation analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0049] The generation unit can adjust the level of detail generated based on the importance of the product when generating hashtags. For example, the generation unit generates detailed hashtags for high-importance products. For example, the generation unit can also generate concise hashtags for low-importance products. The generation unit can also dynamically adjust the level of detail generated according to the importance of the product. This enables efficient hashtag generation by adjusting the level of detail based on the importance of the product. Product importance includes, but is not limited to, sales data and customer ratings. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the product into the AI ​​and have the AI ​​perform the adjustment of the level of detail generated.

[0050] The generation unit can apply different generation algorithms depending on the product category when generating hashtags. For example, the generation unit can generate technical hashtags for technology products. For example, the generation unit can also generate trend-based hashtags for fashion products. For example, the generation unit can generate hashtags related to health and taste for food products. This improves the accuracy of hashtag generation by applying the appropriate generation algorithm according to the product category. Product categories include, but are not limited to, electronic devices, clothing, and food. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the product category into the AI ​​and have the AI ​​apply the appropriate generation algorithm.

[0051] The generation unit can determine the priority of hashtag generation based on the product submission date. For example, the generation unit will prioritize generating hashtags for the newest products. The generation unit can also generate hashtags for older products for reference purposes only. The generation unit can also dynamically adjust the generation priority according to the product submission date. This allows for priority generation of hashtags for the newest products by determining the generation priority based on the product submission date. The product submission date includes, but is not limited to, the release date and campaign period. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input the product submission date into the AI ​​and have the AI ​​determine the generation priority.

[0052] The generation unit can adjust the order of hashtag generation based on the relevance of the products. For example, the generation unit can prioritize generating hashtags for highly relevant products. For example, the generation unit can also postpone generating hashtags for less relevant products. The generation unit can also dynamically adjust the order of generation according to the relevance of the products. This allows for efficient hashtag generation by adjusting the order of generation based on the relevance of the products. The relevance of products includes, but is not limited to, co-occurrence network analysis and correlation analysis. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of products into AI and have AI perform the adjustment of the generation order.

[0053] The analysis unit can improve the accuracy of its analysis by considering the relationships between followers. For example, the analysis unit can analyze the relationships between followers and identify influential followers. The analysis unit can also analyze the characteristics of follower groups by considering the relationships between followers. The analysis unit can also improve the accuracy of predicting follower behavior based on the relationships between followers. This improves the accuracy of the analysis by considering the relationships between followers. The relationships between followers include, but are not limited to, network analysis and correlation analysis among followers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input follower relationship data into AI and have the AI ​​perform the improvement of the analysis accuracy.

[0054] The analysis unit can perform analysis while considering the attribute information of followers. For example, the analysis unit can analyze behavioral patterns based on attribute information such as the age and gender of followers. The analysis unit can also analyze behavioral patterns by considering, for example, the interests and concerns of followers. The analysis unit can also analyze behavioral patterns based on, for example, the geographical attribute information of followers. This allows for more detailed analysis by considering the attribute information of followers. The attribute information of followers includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the attribute information of followers into AI and have the AI ​​perform the analysis.

[0055] The analysis unit can perform analyses while considering the geographical distribution of followers. For example, the analysis unit can analyze behavioral patterns by region based on the geographical distribution of followers. The analysis unit can also formulate marketing strategies by region, taking into account the geographical distribution of followers. The analysis unit can also analyze trends by region, taking into account the geographical distribution of followers. This allows for the analysis of behavioral patterns by region by considering the geographical distribution of followers. The geographical distribution of followers includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input geographical distribution data of followers into AI and have the AI ​​perform the analysis.

[0056] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for followers during the analysis process. For example, the analysis unit can refer to literature related to followers' interests and analyze their behavioral patterns. The analysis unit can also refer to literature related to followers' attributes and analyze their behavioral patterns. The analysis unit can also refer to literature related to followers' behavior and analyze their behavioral patterns. This improves the accuracy of the analysis by referring to relevant literature for followers. Relevant literature for followers includes, but is not limited to, academic papers and industry reports. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevant literature data for followers into an AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0057] The presentation unit can select the optimal display method by referring to the user's past posting history when presenting information. For example, the presentation unit can display the optimal posting schedule based on the user's past posting history. The presentation unit can also select the optimal display method by referring to the user's past posting history. The presentation unit can also analyze the user's past posting history and suggest the optimal display method. This allows the optimal display method to be selected by referring to the user's past posting history. The user's past posting history includes, but is not limited to, social media posts and blog articles. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past posting history into AI and have the AI ​​select the optimal display method.

[0058] The display unit can select the optimal display method when presenting information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. This allows the optimal display method to be selected by considering the user's device information. User device information includes, but is not limited to, the type of device and the OS version. Some or all of the processing described above in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The data acquisition unit can filter trend data based on specific time periods. For example, the unit can acquire only trend data for a specific time period. For example, the unit can acquire trend data for nighttime hours. For example, the unit can acquire trend data for weekend hours. This allows for the acquisition of highly relevant data by filtering based on specific time periods. Specific time periods include, but are not limited to, nighttime, weekends, and specific event periods.

[0061] The analytics department can improve the accuracy of follower behavior analysis by considering the follower's past behavioral history. For example, the analytics department can analyze a follower's past posting history to identify behavioral patterns. The analytics department can also analyze a follower's past likes and shares to identify their interests. The analytics department can also analyze a follower's past comments to identify their interests. By considering the follower's past behavioral history, the accuracy of the analysis is improved.

[0062] The analysis unit can adjust the level of detail of the analysis based on the reliability of the data during the analysis. For example, a detailed analysis can be performed on highly reliable data. A simplified analysis can be performed on less reliable data. The level of detail of the analysis can also be dynamically adjusted according to the reliability of the data. This allows for efficient analysis by adjusting the level of detail based on the reliability of the data. Data reliability includes, but is not limited to, the source of the data and the consistency of the data.

[0063] The generation unit can improve the accuracy of hashtag generation by considering the user's past posts. For example, the generation unit can analyze the user's past posts and generate relevant hashtags. The generation unit can also, for example, refer to the user's past posting history to generate the most suitable hashtags. The generation unit can also, for example, generate trending hashtags based on the user's past posts. This improves the accuracy of generation by considering the user's past posts.

[0064] The analytics department can improve the accuracy of its analysis by considering the device information of followers when analyzing their behavior. For example, the analytics department can analyze the types of devices that followers use and identify behavioral patterns. The analytics department can also analyze the frequency of follower device use and identify their interests. The analytics department can also analyze the usage patterns of followers' devices and identify their interests. In this way, considering the device information of followers improves the accuracy of the analysis.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The acquisition unit acquires trend data. The acquisition unit can acquire trend data and related posts in real time from a social networking platform, for example, by using an API. The acquisition unit can acquire trend data from a social networking platform, for example, by using the social networking platform's API. Step 2: The analysis unit analyzes the data acquired by the acquisition unit to identify keywords and topics. The analysis unit classifies the acquired text data and identifies keywords and topics, for example, by utilizing natural language processing techniques. Natural language processing techniques can include, for example, morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates hashtags based on keywords and topics identified by the analysis unit. For example, the generation unit analyzes a company's product information and past posting data to dynamically generate hashtags that match the product's characteristics and themes that contribute to its popularity. The generation unit can generate hashtags using, for example, keyword combinations or popular hashtags. Step 4: The analytics department analyzes the online activity time and interests of followers. For example, the analytics department analyzes follower log data to identify online activity time and interests. The analytics department can also identify interests by conducting follower surveys, for example. Step 5: The presentation unit presents the optimal posting schedule based on the analysis results obtained by the analysis unit. The presentation unit presents the optimal posting schedule based, for example, on the peak activity times of followers and the reactions to past posts. The presentation unit can, for example, automatically adjust the posting schedule to match the peak activity times of followers.

[0067] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that performs trend analysis, hashtag optimization, and follower behavior analysis on SNS posts of corporate accounts. This AI agent system is configured to solve the problems that corporate accounts face, such as difficulty in creating engaging posts, difficulty in grasping trends and selecting appropriate hashtags, and difficulty in effectively delivering messages to target audiences. First, the AI ​​agent system uses an API to acquire trend data and related posts from SNS platforms in real time. Next, the AI ​​agent system utilizes the latest natural language processing technology to classify the acquired text data and identify keywords and topics. This allows for rapid identification of new trends related to products and industries. Furthermore, the AI ​​agent system analyzes the company's product information and past posting data to dynamically generate hashtags that match the characteristics of the product and themes that lead to popularity. This optimizes hashtags, improves content visibility, and strengthens brand awareness. The AI ​​agent system also analyzes followers' online activity time and interests and automatically suggests the optimal posting schedule. In addition, the AI ​​agent system utilizes a machine learning model that learns patterns of successful cases based on past posting data to predict the optimal content for the next post. This enables the proposal of posting strategies based on scientific methods, resulting in an effective approach to consumers. For example, the AI ​​agent system analyzes a company's product information and past posting data to dynamically generate hashtags that match product characteristics and themes that contribute to their popularity. This optimizes hashtags, improves content visibility, and strengthens brand awareness. The AI ​​agent system also analyzes followers' online activity time and interests to automatically suggest the optimal posting schedule. Furthermore, the AI ​​agent system utilizes a machine learning model that learns patterns from successful cases based on past posting data to predict the optimal content for the next post. This enables the proposal of posting strategies based on scientific methods, resulting in an effective approach to consumers.This allows AI agent systems to improve the accuracy of strategies and execution, and enhance marketing ROI, based on data-driven facts.

[0068] The AI ​​agent system according to this embodiment comprises an acquisition unit, an analysis unit, a generation unit, an analysis unit, and a presentation unit. The acquisition unit acquires trend data. The acquisition unit can acquire trend data and related posts from an SNS platform in real time, for example, by using the SNS platform's API. The analysis unit analyzes the data acquired by the acquisition unit and identifies keywords and topics. The analysis unit can classify the acquired text data and identify keywords and topics, for example, by utilizing natural language processing technology. Natural language processing technology can include, for example, morphological analysis, grammatical analysis, and semantic analysis. The generation unit generates hashtags based on the keywords and topics identified by the analysis unit. The generation unit can, for example, analyze a company's product information and past posting data to dynamically generate hashtags that match the characteristics of the product and themes that lead to its popularity. The generation unit can generate hashtags, for example, by using keyword combinations or popular hashtags. The analysis unit analyzes the online activity time and interests of followers. The analysis unit, for example, analyzes follower log data to identify online activity times and interests. The analysis unit can also identify interests by conducting follower surveys. The presentation unit presents an optimal posting schedule based on the analysis results obtained by the analysis unit. The presentation unit presents an optimal posting schedule based, for example, on the peak activity times of followers and reactions to past posts. The presentation unit can automatically adjust the posting schedule to match the peak activity times of followers. As a result, the AI ​​agent system according to the embodiment can consistently perform everything from acquiring and analyzing trend data to generating hashtags, analyzing follower behavior, and presenting posting schedules. Some or all of the above-described processes in the AI ​​agent system may be performed using AI, for example, or without AI. For example, the AI ​​agent system can input data acquired by the acquisition unit into the AI ​​and have the AI ​​perform data analysis and hashtag generation.

[0069] The data acquisition unit acquires trend data. For example, the data acquisition unit uses APIs to acquire trend data and related posts from social media platforms in real time. Specifically, the data acquisition unit uses APIs provided by social media platforms to acquire trend data. This allows the data acquisition unit to collect currently trending keywords, hashtags, and related posts on social media in real time. The data acquisition unit centrally manages the data acquired through APIs and stores it in a database. Furthermore, the data acquisition unit can flexibly set the frequency and scope of data acquisition. For example, it can acquire data only during specific time periods or in specific regions. This allows the data acquisition unit to efficiently collect the necessary data and improve the overall system performance. The data acquisition unit also has a function to monitor the data acquisition status and issue alerts if an anomaly occurs. This allows the data acquisition unit to achieve stable data collection and improve the reliability of the system.

[0070] The analysis unit analyzes the data acquired by the acquisition unit to identify keywords and topics. For example, the analysis unit utilizes natural language processing techniques to classify the acquired text data and identify keywords and topics. Specifically, the analysis unit uses morphological analysis to divide text data into words and grammatical analysis to analyze sentence structure. Furthermore, it uses semantic analysis to understand the meaning of sentences and extract important keywords and topics. By combining these natural language processing techniques, the analysis unit can analyze the content of the acquired data in detail and identify trending keywords and topics. The analysis unit can also perform data analysis using AI. For example, it can use a deep learning model to learn the features of text data and achieve highly accurate keyword extraction and topic classification. This allows the analysis unit to analyze large amounts of data quickly and accurately, and respond immediately to changes in trends. The analysis unit can also predict trends based on past data. This allows for prediction of future trends and the implementation of early countermeasures.

[0071] The generation unit generates hashtags based on keywords and topics identified by the analysis unit. For example, the generation unit analyzes a company's product information and past posting data to dynamically generate hashtags that match the product's characteristics and themes that contribute to its popularity. Specifically, the generation unit generates effective hashtags using keyword combinations and popular hashtags. The generation unit can also generate hashtags using AI. For example, it can automatically generate hashtags related to keywords and topics using natural language generation technology. This allows the generation unit to quickly generate effective hashtags that are in line with trends, increasing their virality on social media. Furthermore, the generation unit has a function to evaluate the effectiveness of the generated hashtags and modify them as needed. This allows the generation unit to always provide the optimal hashtags and maximize the effectiveness of social media marketing.

[0072] The analytics department analyzes followers' online activity time and interests. For example, the analytics department analyzes follower log data to identify online activity time and interests. Specifically, the analytics department analyzes log data such as follower posting times, browsing history, and like and share history to identify follower online activity patterns. Furthermore, it can also conduct follower surveys to identify interests and concerns. The analytics department can use AI to analyze follower behavior data and identify followers' interests and concerns with high accuracy. For example, it can use clustering technology to group followers based on their interests and concerns and analyze the characteristics of each group. This allows the analytics department to target followers based on their interests and concerns and develop effective marketing strategies. In addition, the analytics department can identify followers' online activity times and suggest optimal posting times. This maximizes post engagement and enhances the effectiveness of social media marketing.

[0073] The presentation unit presents the optimal posting schedule based on the analysis results obtained by the analysis unit. For example, the presentation unit presents the optimal posting schedule based on follower activity peak times and reactions to past posts. Specifically, the presentation unit automatically adjusts the posting schedule to match follower activity peak times. The presentation unit can calculate the optimal posting schedule using AI. For example, it uses machine learning algorithms to learn from past posting data and follower activity data to predict the optimal posting time. This allows the presentation unit to provide a posting schedule that matches follower activity peak times, maximizing post engagement. The presentation unit also has a function to evaluate the effectiveness of the posting schedule and modify the schedule as needed. This allows the presentation unit to always provide the optimal posting schedule and maximize the effectiveness of social media marketing.

[0074] The acquisition unit can acquire trend data and related posts from SNS platforms in real time using an API. For example, the acquisition unit can acquire trend data from SNS platforms using the SNS platform's API. The acquisition unit can also acquire related posts from SNS platforms using the SNS platform's API. The acquisition unit can also acquire trend data from SNS platforms in real time using the SNS platform's API. This allows the system to reflect the latest information by acquiring trend data in real time. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the data acquired using the API into an AI and have the AI ​​perform data analysis.

[0075] The analysis unit can classify acquired text data and identify keywords and topics by utilizing natural language processing techniques. For example, the analysis unit can use morphological analysis to divide the text data into words. The analysis unit can also use grammatical analysis to analyze the grammatical structure of the text data. The analysis unit can also use semantic analysis to analyze the meaning of the text data. This improves the accuracy of text data analysis by utilizing natural language processing techniques. Natural language processing techniques include, but are not limited to, morphological analysis, grammatical analysis, and semantic analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input acquired text data into AI and have the AI ​​perform keyword and topic identification.

[0076] The generation unit can analyze a company's product information and past posting data to dynamically generate hashtags that match the product's characteristics and themes that contribute to its popularity. For example, the generation unit can analyze a company's product catalog and generate hashtags based on the product's characteristics. The generation unit can also analyze past posting history and generate hashtags based on popular themes. The generation unit can also generate hashtags using keyword combinations. By generating hashtags that match the product's characteristics and themes that contribute to its popularity, the visibility of the content is improved. The company's product information and past posting data include, but are not limited to, product catalogs and social media posting history. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the company's product information and past posting data into AI and have the AI ​​perform hashtag generation.

[0077] The analytics department can analyze followers' online activity time and interests. For example, the analytics department can analyze followers' log data to identify their online activity time. The analytics department can also conduct follower surveys to identify their interests. The analytics department can also analyze followers' social media behavior to identify their interests. By analyzing followers' online activity time and interests, it is possible to suggest an optimal posting schedule. The analysis of followers' online activity time and interests includes, but is not limited to, log data analysis and surveys. 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 follower log data into an AI and have the AI ​​perform the identification of online activity time and interests.

[0078] The presentation unit can present an optimal posting schedule based on the analysis results obtained by the analysis unit. The presentation unit can, for example, automatically adjust the posting schedule to match the peak activity times of followers. The presentation unit can also present an optimal posting schedule based on, for example, the reactions to past posts. The presentation unit can also automatically adjust the posting schedule based on, for example, the peak activity times of followers and the reactions to past posts. This enables effective message delivery by presenting an optimal posting schedule. An optimal posting schedule includes, but is not limited to, the peak activity times of followers and the reactions to past posts. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input the analysis results obtained by the analysis unit into AI and have the AI ​​perform the task of presenting an optimal posting schedule.

[0079] The generation unit can predict the next optimal post content by utilizing a machine learning model that learns patterns of successful cases based on past posting data. For example, the generation unit analyzes past posting data and identifies patterns of successful cases. The generation unit can also learn patterns of successful cases using a machine learning model, for example. The generation unit can also learn patterns of successful cases using a neural network, for example. This allows it to predict the next optimal post content by utilizing a machine learning model. Machine learning models include, but are not limited to, neural networks and support vector machines. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past posting data into an AI and have the AI ​​learn patterns of successful cases.

[0080] The acquisition unit can estimate the user's emotions and adjust the timing of trend data acquisition based on the estimated user emotions. For example, if the user is excited, the acquisition unit can acquire trend data in real time and reflect it immediately. For example, if the user is relaxed, the acquisition unit can acquire trend data at regular intervals to provide stable information. For example, if the user is stressed, the acquisition unit can reduce the acquisition frequency and acquire only important trends. This allows for data acquisition at a more appropriate time by adjusting the timing of trend data acquisition according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input user emotion data into AI and have the AI ​​adjust the timing of trend data acquisition.

[0081] The acquisition unit can analyze the acquisition history of past trend data and select the optimal acquisition method. For example, the acquisition unit can analyze the acquisition frequency of past trend data and set the optimal acquisition interval. For example, the acquisition unit can analyze the time period of past trend data acquisition and acquire data at the most effective time period. For example, the acquisition unit can analyze the acquisition method of past trend data and select the most efficient API. In this way, the optimal acquisition method can be selected by analyzing the past acquisition history. The optimal acquisition method includes, but is not limited to, the results of the analysis of past data and the algorithm to be used. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the acquisition history of past trend data into AI and have AI select the optimal acquisition method.

[0082] The acquisition unit can filter trend data based on specific industries or regions when acquiring it. For example, the acquisition unit can acquire only trend data related to a specific industry. The acquisition unit can also acquire only trend data related to a specific region. For example, the acquisition unit can filter and acquire trend data related to both industry and region. This allows for the acquisition of highly relevant data by filtering based on specific industries or regions. Specific industries and regions include, but are not limited to, industry classification criteria or regional scope. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have AI perform the filtering process based on specific industries or regions when acquiring trend data.

[0083] The data acquisition unit can estimate the user's emotions and determine the priority of trend data to acquire based on the estimated user emotions. For example, if the user is excited, the data acquisition unit may prioritize acquiring the latest trend data. For example, if the user is relaxed, the data acquisition unit may also prioritize acquiring stable trend data. For example, if the user is stressed, the data acquisition unit may also prioritize acquiring only important trend data. This allows for the acquisition of more important data by prioritizing trend data according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data acquisition unit may be performed using AI, or not using AI. For example, the data acquisition unit can input user emotion data into an AI and have the AI ​​determine the priority of trend data.

[0084] The acquisition unit can prioritize the acquisition of highly relevant data by considering the user's geographical location information when acquiring trend data. For example, the acquisition unit can acquire highly relevant trend data based on the user's current location. The acquisition unit can also acquire highly relevant trend data based on the user's past travel history. For example, the acquisition unit can update the user's geographical location information in real time to acquire the most relevant trend data. This allows for the priority acquisition of highly relevant data by considering the user's geographical location information. The user's geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or without AI. For example, the acquisition unit can input the user's geographical location information into AI and have AI acquire highly relevant data.

[0085] The acquisition unit can analyze a user's social media activity and acquire relevant data when acquiring trend data. For example, the acquisition unit can analyze a user's past posts and acquire relevant trend data. The acquisition unit can also analyze the activity of a user's followers and acquire relevant trend data. The acquisition unit can also analyze a user's interests on social media and acquire relevant trend data. In this way, relevant data can be acquired by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posting frequency, the number of likes and shares, etc. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input the user's social media activity data into AI and have the AI ​​perform the acquisition of 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 relaxed, the analysis unit can provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. For example, if the user is excited, the analysis unit can also provide visually stimulating analysis results. By adjusting the presentation of the analysis according to the user's emotions, more appropriate analysis results can be provided. User emotions include, but are not limited to, emotion analysis algorithms and survey results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the presentation of the analysis.

[0087] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can also perform a simplified analysis on data with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Data importance includes, but is not limited to, data frequency and relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data importance into the AI ​​and have the AI ​​perform the adjustment of 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 natural language processing algorithm to text data. For example, the analysis unit can also apply an image analysis algorithm to image data. For example, the analysis unit can also apply a speech analysis algorithm to speech data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Data categories include, but are not limited to, text data, image data, and speech data. Some or all of the above processing 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 have the AI ​​perform the application of an appropriate 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 perform a short, concise analysis. For example, if the user is relaxed, the analysis unit can perform a detailed analysis. For example, if the user is excited, the analysis unit can perform a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, more appropriate analysis results can be provided. User emotions include, but are not limited to, emotion analysis algorithms and survey results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the length of the analysis.

[0090] The analysis unit can determine the priority of analysis based on the data acquisition time during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the most recent data while referring to past data. The analysis unit may also dynamically adjust the priority of analysis according to the data acquisition time. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data acquisition time. The data acquisition time includes, but is not limited to, data timestamps and acquisition frequency. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data acquisition time into the AI ​​and have the AI ​​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. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can also dynamically adjust 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. The relevance of the data includes, but is not limited to, co-occurrence network analysis and correlation analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into the AI ​​and have the AI ​​adjust the order of analysis.

[0092] The generation unit can estimate the user's emotions and adjust the expression of the generated hashtags based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate friendly hashtags. For example, if the user is in a hurry, the generation unit can also generate concise and easy-to-understand hashtags. For example, if the user is excited, the generation unit can also generate visually stimulating hashtags. This allows for the generation of more appropriate hashtags by adjusting the expression of hashtags according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the expression of hashtags.

[0093] The generation unit can adjust the level of detail generated based on the importance of the product when generating hashtags. For example, the generation unit generates detailed hashtags for high-importance products. For example, the generation unit can also generate concise hashtags for low-importance products. The generation unit can also dynamically adjust the level of detail generated according to the importance of the product. This enables efficient hashtag generation by adjusting the level of detail based on the importance of the product. Product importance includes, but is not limited to, sales data and customer ratings. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the product into the AI ​​and have the AI ​​perform the adjustment of the level of detail generated.

[0094] The generation unit can apply different generation algorithms depending on the product category when generating hashtags. For example, the generation unit can generate technical hashtags for technology products. For example, the generation unit can also generate trend-based hashtags for fashion products. For example, the generation unit can generate hashtags related to health and taste for food products. This improves the accuracy of hashtag generation by applying the appropriate generation algorithm according to the product category. Product categories include, but are not limited to, electronic devices, clothing, and food. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the product category into the AI ​​and have the AI ​​apply the appropriate generation algorithm.

[0095] The generation unit can estimate the user's emotions and adjust the length of the generated hashtags based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise hashtags. If the user is relaxed, the generation unit can also generate longer hashtags with detailed descriptions. If the user is excited, the generation unit can also generate visually stimulating hashtags. By adjusting the length of hashtags according to the user's emotions, more appropriate hashtags can be generated. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into an AI and have the AI ​​adjust the length of the hashtags.

[0096] The generation unit can determine the priority of hashtag generation based on the product submission date. For example, the generation unit will prioritize generating hashtags for the newest products. The generation unit can also generate hashtags for older products for reference purposes only. The generation unit can also dynamically adjust the generation priority according to the product submission date. This allows for priority generation of hashtags for the newest products by determining the generation priority based on the product submission date. The product submission date includes, but is not limited to, the release date and campaign period. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not. For example, the generation unit can input the product submission date into the AI ​​and have the AI ​​determine the generation priority.

[0097] The generation unit can adjust the order of hashtag generation based on the relevance of the products. For example, the generation unit can prioritize generating hashtags for highly relevant products. For example, the generation unit can also postpone generating hashtags for less relevant products. The generation unit can also dynamically adjust the order of generation according to the relevance of the products. This allows for efficient hashtag generation by adjusting the order of generation based on the relevance of the products. The relevance of products includes, but is not limited to, co-occurrence network analysis and correlation analysis. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of products into AI and have AI perform the adjustment of the generation order.

[0098] The analysis unit can estimate the user's emotions and adjust the criteria for follower behavior analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed follower behavior analysis. For example, if the user is in a hurry, the analysis unit can perform a concise follower behavior analysis that gets straight to the point. For example, if the user is excited, the analysis unit can perform a visually stimulating follower behavior analysis. This allows for more appropriate analysis results by adjusting the criteria for follower behavior analysis according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the criteria for follower behavior analysis.

[0099] The analysis unit can improve the accuracy of its analysis by considering the relationships between followers. For example, the analysis unit can analyze the relationships between followers and identify influential followers. The analysis unit can also analyze the characteristics of follower groups by considering the relationships between followers. The analysis unit can also improve the accuracy of predicting follower behavior based on the relationships between followers. This improves the accuracy of the analysis by considering the relationships between followers. The relationships between followers include, but are not limited to, network analysis and correlation analysis among followers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input follower relationship data into AI and have the AI ​​perform the improvement of the analysis accuracy.

[0100] The analysis unit can perform analysis while considering the attribute information of followers. For example, the analysis unit can analyze behavioral patterns based on attribute information such as the age and gender of followers. The analysis unit can also analyze behavioral patterns by considering, for example, the interests and concerns of followers. The analysis unit can also analyze behavioral patterns based on, for example, the geographical attribute information of followers. This allows for more detailed analysis by considering the attribute information of followers. The attribute information of followers includes, but is not limited to, age, gender, and occupation. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the attribute information of followers into AI and have the AI ​​perform the analysis.

[0101] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize displaying detailed analysis results. For example, if the user is in a hurry, the analysis unit may prioritize displaying concise analysis results. For example, if the user is excited, the analysis unit may prioritize displaying visually stimulating analysis results. By adjusting the display order of the analysis results according to the user's emotions, a more appropriate display becomes possible. User emotions include, but are not limited to, emotion analysis algorithms and survey results. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the display order of the analysis results.

[0102] The analysis unit can perform analyses while considering the geographical distribution of followers. For example, the analysis unit can analyze behavioral patterns by region based on the geographical distribution of followers. The analysis unit can also formulate marketing strategies by region, taking into account the geographical distribution of followers. The analysis unit can also analyze trends by region, taking into account the geographical distribution of followers. This allows for the analysis of behavioral patterns by region by considering the geographical distribution of followers. The geographical distribution of followers includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input geographical distribution data of followers into AI and have the AI ​​perform the analysis.

[0103] The analysis unit can improve the accuracy of its analysis by referring to relevant literature for followers during the analysis process. For example, the analysis unit can refer to literature related to followers' interests and analyze their behavioral patterns. The analysis unit can also refer to literature related to followers' attributes and analyze their behavioral patterns. The analysis unit can also refer to literature related to followers' behavior and analyze their behavioral patterns. This improves the accuracy of the analysis by referring to relevant literature for followers. Relevant literature for followers includes, but is not limited to, academic papers and industry reports. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevant literature data for followers into an AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0104] The presentation unit can estimate the user's emotions and adjust how the posting schedule is displayed based on the estimated emotions. For example, if the user is relaxed, the presentation unit can display a detailed posting schedule. For example, if the user is in a hurry, the presentation unit can also display a concise posting schedule. For example, if the user is excited, the presentation unit can also display a visually stimulating posting schedule. This allows for a more appropriate display by adjusting how the posting schedule is displayed according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, or not using AI. For example, the presentation unit can input user emotion data into an AI and have the AI ​​adjust how the posting schedule is displayed.

[0105] The presentation unit can select the optimal display method by referring to the user's past posting history when presenting information. For example, the presentation unit can display the optimal posting schedule based on the user's past posting history. The presentation unit can also select the optimal display method by referring to the user's past posting history. The presentation unit can also analyze the user's past posting history and suggest the optimal display method. This allows the optimal display method to be selected by referring to the user's past posting history. The user's past posting history includes, but is not limited to, social media posts and blog articles. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input the user's past posting history into AI and have the AI ​​select the optimal display method.

[0106] The presentation unit can estimate the user's emotions and adjust the posting schedule operation steps based on the estimated user emotions. For example, if the user is relaxed, the presentation unit can provide detailed operation steps. For example, if the user is in a hurry, the presentation unit can also provide concise operation steps. For example, if the user is excited, the presentation unit can also provide visually stimulating operation steps. This allows for more appropriate operation by adjusting the posting schedule operation steps according to the user's emotions. User emotions include, but are not limited to, emotion analysis algorithms and surveys. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input user emotion data into AI and have the AI ​​perform the adjustment of the posting schedule operation steps.

[0107] The display unit can select the optimal display method when presenting information, taking into account the user's device information. For example, if the user is using a smartphone, the display unit provides a display method that matches the screen size. For example, if the user is using a tablet, the display unit can also provide a display method optimized for a larger screen. For example, if the user is using a smartwatch, the display unit can also provide a concise and highly visible display method. This allows the optimal display method to be selected by considering the user's device information. User device information includes, but is not limited to, the type of device and the OS version. Some or all of the processing described above in the display unit may be performed using, for example, AI, or not using AI. For example, the display unit can input the user's device information into AI and have the AI ​​select the optimal display method.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is excited, the analysis unit can prioritize analyzing the latest trend data. If the user is relaxed, the analysis unit can also perform a detailed analysis including historical data. If the user is stressed, the analysis unit can prioritize analyzing only the most important data. By adjusting the analysis priority according to the user's emotions, more appropriate analysis results can be provided. The user's emotions are estimated using emotion analysis algorithms, surveys, and other methods.

[0110] The data acquisition unit can filter trend data based on specific time periods. For example, the unit can acquire only trend data for a specific time period. For example, the unit can acquire trend data for nighttime hours. For example, the unit can acquire trend data for weekend hours. This allows for the acquisition of highly relevant data by filtering based on specific time periods. Specific time periods include, but are not limited to, nighttime, weekends, and specific event periods.

[0111] The generation unit can estimate the user's emotions and adjust the tone of the hashtags it generates based on those emotions. For example, if the user is relaxed, the generation unit can generate hashtags with a friendly tone. If the user is in a hurry, the generation unit can also generate hashtags with a concise and clear tone. If the user is excited, the generation unit can also generate hashtags with an energetic tone. This allows for the generation of more appropriate hashtags by adjusting the tone according to the user's emotions. The user's emotions are estimated using methods such as emotion analysis algorithms and surveys.

[0112] The notification system can estimate the user's emotions and adjust the notification method for the posting schedule based on those emotions. For example, if the user is relaxed, the notification system can provide detailed notifications. If the user is in a hurry, the notification system can provide concise notifications. If the user is excited, the notification system can provide visually stimulating notifications. By adjusting the notification method according to the user's emotions, more appropriate notifications can be provided. The user's emotions are estimated using emotion analysis algorithms, surveys, and other methods.

[0113] The analytics department can improve the accuracy of follower behavior analysis by considering the follower's past behavioral history. For example, the analytics department can analyze a follower's past posting history to identify behavioral patterns. The analytics department can also analyze a follower's past likes and shares to identify their interests. The analytics department can also analyze a follower's past comments to identify their interests. By considering the follower's past behavioral history, the accuracy of the analysis is improved.

[0114] The data acquisition unit can estimate the user's emotions and adjust the categories of trend data to acquire based on the estimated emotions. For example, if the user is relaxed, the unit can prioritize acquiring entertainment-related trend data. If the user is in a hurry, the unit can prioritize acquiring business-related trend data. If the user is excited, the unit can prioritize acquiring sports-related trend data. By adjusting the categories of trend data according to the user's emotions, more appropriate data can be acquired. The user's emotions are estimated using emotion analysis algorithms, surveys, etc.

[0115] The analysis unit can adjust the level of detail of the analysis based on the reliability of the data during the analysis. For example, a detailed analysis can be performed on highly reliable data. A simplified analysis can be performed on less reliable data. The level of detail of the analysis can also be dynamically adjusted according to the reliability of the data. This allows for efficient analysis by adjusting the level of detail based on the reliability of the data. Data reliability includes, but is not limited to, the source of the data and the consistency of the data.

[0116] The generation unit can improve the accuracy of hashtag generation by considering the user's past posts. For example, the generation unit can analyze the user's past posts and generate relevant hashtags. The generation unit can also, for example, refer to the user's past posting history to generate the most suitable hashtags. The generation unit can also, for example, generate trending hashtags based on the user's past posts. This improves the accuracy of generation by considering the user's past posts.

[0117] The notification unit can estimate the user's emotions and adjust the notification frequency of the posting schedule based on the estimated emotions. For example, if the user is relaxed, the notification unit can send frequent notifications. If the user is in a hurry, the notification unit can reduce the notification frequency. If the user is excited, the notification unit can send only important notifications. This allows for more appropriate notifications by adjusting the notification frequency according to the user's emotions. The user's emotions are estimated using emotion analysis algorithms, surveys, etc.

[0118] The analytics department can improve the accuracy of its analysis by considering the device information of followers when analyzing their behavior. For example, the analytics department can analyze the types of devices that followers use and identify behavioral patterns. The analytics department can also analyze the frequency of follower device use and identify their interests. The analytics department can also analyze the usage patterns of followers' devices and identify their interests. In this way, considering the device information of followers improves the accuracy of the analysis.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The acquisition unit acquires trend data. The acquisition unit can acquire trend data and related posts in real time from a social networking platform, for example, by using an API. The acquisition unit can acquire trend data from a social networking platform, for example, by using the social networking platform's API. Step 2: The analysis unit analyzes the data acquired by the acquisition unit to identify keywords and topics. The analysis unit classifies the acquired text data and identifies keywords and topics, for example, by utilizing natural language processing techniques. Natural language processing techniques can include, for example, morphological analysis, grammatical analysis, and semantic analysis. Step 3: The generation unit generates hashtags based on keywords and topics identified by the analysis unit. For example, the generation unit analyzes a company's product information and past posting data to dynamically generate hashtags that match the product's characteristics and themes that contribute to its popularity. The generation unit can generate hashtags using, for example, keyword combinations or popular hashtags. Step 4: The analytics department analyzes the online activity time and interests of followers. For example, the analytics department analyzes follower log data to identify online activity time and interests. The analytics department can also identify interests by conducting follower surveys, for example. Step 5: The presentation unit presents the optimal posting schedule based on the analysis results obtained by the analysis unit. The presentation unit presents the optimal posting schedule based, for example, on the peak activity times of followers and the reactions to past posts. The presentation unit can, for example, automatically adjust the posting schedule to match the peak activity times of followers.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and presentation unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit acquires trend data from the SNS platform via the communication I / F 44 of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is implemented by the control unit 46A of the smart device 14 and generates hashtags based on the analysis results. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the behavior of followers. The presentation unit is implemented by the control unit 46A of the smart device 14 and presents the optimal posting schedule. 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.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and presentation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires trend data from an SNS platform via the communication I / F 44 of the smart glasses 214. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the acquired data. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates hashtags based on the analysis results. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the behavior of followers. The presentation unit is implemented by the control unit 46A of the smart glasses 214 and presents an optimal posting schedule. 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.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and presentation unit, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the acquisition unit acquires trend data from the SNS platform via the communication I / F 44 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the acquired data. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates hashtags based on the analysis results. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the behavior of followers. The presentation unit is implemented by the control unit 46A of the headset terminal 314 and presents the optimal posting schedule. 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.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the acquisition unit, analysis unit, generation unit, and presentation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires trend data from an SNS platform via the communication I / F 44 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the acquired data. The generation unit is implemented by the control unit 46A of the robot 414 and generates hashtags based on the analysis results. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the behavior of followers. The presentation unit is implemented by the control unit 46A of the robot 414 and presents an optimal posting schedule. 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The data acquisition unit acquires trend data, An analysis unit analyzes the data acquired by the acquisition unit and identifies keywords and topics, A generation unit that generates hashtags based on keywords and topics identified by the analysis unit, The analytics department analyzes the online activity time and interests of followers, The system includes a presentation unit that presents an optimal posting schedule based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The acquisition unit is, Use APIs to retrieve trending data and relevant posts from social media platforms in real time. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, By utilizing natural language processing technology, the acquired text data is classified, and keywords and topics are identified. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is By analyzing a company's product information and past posting data, it dynamically generates hashtags that match the product's characteristics and themes that contribute to its popularity. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze your followers' online activity time and interests. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned display unit is, Based on the analysis results obtained by the aforementioned analysis unit, the optimal posting schedule will be presented. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is By utilizing a machine learning model that learns patterns from successful past posts, we predict the optimal content for the next post. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, It estimates user sentiment and adjusts the timing of trend data acquisition based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, Analyze the history of past trend data acquisition and select the optimal acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring trend data, filter it based on specific industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, It estimates user sentiment and determines the priority of trend data to acquire based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring trend data, the system prioritizes acquiring highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When acquiring trend data, we analyze users' social media activity and obtain relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the representation of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, 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 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, 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 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was acquired. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, 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 20) The generating unit is It estimates user sentiment and adjusts the way hashtags are expressed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating hashtags, adjust the level of detail based on the importance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating hashtags, different generation algorithms are applied depending on the product category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is It estimates the user's sentiment and adjusts the length of the generated hashtags based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating hashtags, the priority of generation is determined based on the product submission date. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating hashtags, the generation order is adjusted based on the relevance of the product. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is We estimate user sentiment and adjust the criteria for follower behavior analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is When analyzing, consider the relationships between followers to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is When performing the analysis, follower attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is It estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is When performing the analysis, the geographical distribution of followers will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is During analysis, refer to relevant literature from followers to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned display unit is, It estimates the user's sentiment and adjusts how the posting schedule is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned display unit is, When presenting content, the system will refer to the user's past posting history to select the most suitable display method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned display unit is, It estimates the user's sentiment and adjusts the posting schedule operation procedure based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned display unit is, When presenting the information, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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. The data acquisition unit acquires trend data, An analysis unit analyzes the data acquired by the acquisition unit and identifies keywords and topics, A generation unit that generates hashtags based on keywords and topics identified by the analysis unit, The analytics department analyzes the online activity time and interests of followers, The system includes a presentation unit that presents an optimal posting schedule based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. The acquisition unit is, Use APIs to retrieve trending data and relevant posts from social media platforms in real time. The system according to feature 1.

3. The aforementioned analysis unit, By utilizing natural language processing technology, the acquired text data is classified, and keywords and topics are identified. The system according to feature 1.

4. The generating unit is By analyzing a company's product information and past posting data, it dynamically generates hashtags that match the product's characteristics and themes that contribute to its popularity. The system according to feature 1.

5. The aforementioned analysis unit is Analyze your followers' online activity time and interests. The system according to feature 1.

6. The aforementioned display unit is, Based on the analysis results obtained by the aforementioned analysis unit, the optimal posting schedule will be presented. The system according to feature 1.

7. The generating unit is By utilizing a machine learning model that learns patterns from successful past posts, we predict the optimal content for the next post. The system according to feature 1.

8. The acquisition unit is, It estimates user sentiment and adjusts the timing of trend data acquisition based on the estimated user sentiment. The system according to feature 1.

9. The acquisition unit is, Analyze the history of past trend data acquisition and select the optimal acquisition method. The system according to feature 1.

10. The acquisition unit is, When acquiring trend data, filter it based on specific industries or regions. The system according to feature 1.

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