system

Abstract

The system according to this embodiment aims to identify potential users from a vast amount of data and propose the optimal advertising channel strategy. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an identification unit, and a generation unit. The collection unit collects online activity and purchasing behavior data. The analysis unit performs structured analysis on the data collected by the collection unit. The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. The generation unit generates an optimal advertising channel strategy for the potential users identified by the identification 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 prior art, although the discovery of the target market greatly affects the success or failure of an advertising campaign, there is a problem that it is difficult to find an optimal channel strategy for effective advertising investment.

[0005] The system according to the embodiment aims to identify potential users from a vast amount of data and propose an optimal advertising channel strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a generation unit. The collection unit collects online activity and purchasing behavior data. The analysis unit performs structured analysis on the data collected by the collection unit. The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. The generation unit generates an optimal advertising channel strategy for the potential users identified by the identification unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify potential users from a vast amount of data and propose the optimal advertising channel strategy. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The target market discovery system according to an embodiment of the present invention is a system that uses AI to discover target markets and proposes an optimal advertising channel strategy. This target market discovery system collects a vast amount of market data, such as online activity and purchasing behavior data, and the AI ​​structures and analyzes the collected data to identify potential users with high similarity. Furthermore, the AI ​​generates an optimal advertising channel strategy for the identified potential users. This mechanism optimizes advertising investment and maximizes cost-effectiveness. In addition, the time spent on market research is significantly reduced and targeting accuracy is improved. For example, the target market discovery system collects a vast amount of market data, such as online activity and purchasing behavior data. In this process, information is collected from various data sources, such as users' website browsing history, purchase history, and social media activity. For example, this includes purchase history on e-commerce sites and content posted on social media. This makes it possible to understand user behavior patterns. Next, the target market discovery system uses AI to structure and analyze the collected data. The AI ​​analyzes the collected data and identifies user behavior patterns and interests. For example, it can identify users who have a high level of interest in a particular product and formulate an appropriate advertising strategy for those users. This allows for the precise identification of target markets. Furthermore, the target market discovery system uses AI to generate optimal advertising channel strategies for identified potential users. The AI ​​selects the most suitable advertising channels and formulates advertising strategies based on user behavior patterns and interests. For example, if it determines that social media advertising is effective for a particular user, it formulates a strategy to deliver social media ads to that user. This optimizes advertising investment and maximizes cost-effectiveness. For example, the reach of implementing companies increases by 200%, and advertising effectiveness increases by 50%. In addition, the time spent on market research is reduced by 75%. This makes target market discovery more efficient and improves the success rate of advertising campaigns. In this way, the target market discovery system optimizes advertising investment and maximizes cost-effectiveness.

[0029] The target market discovery system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a generation unit. The collection unit collects online activity and purchasing behavior data. The collection unit collects data such as browsing history of e-commerce sites, social networking services (SNS), and websites. For example, the collection unit can collect purchase history on e-commerce sites. The collection unit can also collect content posted on SNS. Furthermore, the collection unit can also collect website browsing history. For example, the collection unit collects the history of web pages visited by a user to understand the user's interests. The analysis unit performs structured analysis on the data collected by the collection unit. The analysis unit analyzes the data using machine learning or deep learning, for example. The analysis unit can analyze the data using a neural network, for example. The analysis unit can also analyze the data using a support vector machine. Furthermore, the analysis unit can also analyze the data using a decision tree. For example, the analysis unit uses a neural network to analyze user behavior patterns and identify interests. The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. The identification unit identifies potential users using, for example, clustering or classification algorithms. The identification unit can identify potential users using, for example, K-means clustering. It can also identify potential users using decision trees. Furthermore, it can identify potential users using random forests. For example, the identification unit groups users using K-means clustering to identify potential users with high similarity. The generation unit generates the optimal advertising channel strategy for the potential users identified by the identification unit. The generation unit selects advertising channels such as social media advertising, search engine advertising, and display advertising, and generates an advertising strategy. For example, the generation unit can select social media advertising and generate an advertising strategy. It can also select search engine advertising and generate an advertising strategy. Furthermore, it can select display advertising and generate an advertising strategy. For example, the generation unit selects social media advertising and generates a strategy to deliver social media ads to specific users.This enables the target market discovery system according to the embodiment to discover target markets and propose optimal advertising channel strategies.

[0030] The data collection unit collects online activity and purchasing behavior data. For example, it collects data from e-commerce sites, social media, and website browsing history. Specifically, from e-commerce sites, it can collect detailed data such as the types of products purchased by users, purchase frequency, and purchase amount. This allows for an understanding of users' purchasing trends and preferences. From social media, it collects data such as the content of users' posts, likes and shares, and accounts they follow. This allows for an understanding of users' interests and social connections. Furthermore, from website browsing history, it collects data such as the pages users visited, the time spent on each page, and the links they clicked. This allows for an understanding of what kind of information users are interested in. The data collection unit collects this data in real time and stores it in a central database. Technologies such as cookies, tracking pixels, and API integration can be used for data collection. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and gain a detailed understanding of user behavior and interests.

[0031] The analysis unit performs structured analysis on the data collected by the collection unit. For example, the analysis unit uses machine learning and deep learning to analyze the data. Specifically, it uses neural networks to analyze the data and identify user behavior patterns and interests. A neural network consists of multiple perceptrons, processing input data layer by layer to obtain an output. This allows for highly accurate analysis of complex patterns and relationships. Furthermore, data can also be analyzed using support vector machines. Support vector machines map data into a high-dimensional space and classify it by finding the optimal classification boundary. This allows for highly accurate classification of user interests. Additionally, data can be analyzed using decision trees. Decision trees divide data based on conditions, forming a tree structure. This makes it easier to visually understand user behavior patterns. By combining these technologies, the analysis unit can analyze the collected data from multiple perspectives and gain a detailed understanding of user behavior and interests.

[0032] The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. The identification unit identifies potential users using, for example, clustering or classification algorithms. Specifically, it groups users using K-means clustering to identify potential users with high similarity. K-means clustering divides the data into K clusters and calculates the center point of each cluster. This allows users with similar characteristics to be classified into the same cluster. Potential users can also be identified using decision trees. Decision trees divide the data based on conditions and form a tree structure. This makes it easier to visually understand user characteristics and identify potential users with high similarity. Furthermore, potential users can also be identified using random forests. Random forests are a type of ensemble learning that combines multiple decision trees to make predictions, enabling highly accurate classification. By utilizing these technologies, the identification unit can efficiently identify potential users with high similarity from the analyzed data and uncover target markets.

[0033] The generation unit generates the optimal advertising channel strategy for potential users identified by the identification unit. The generation unit selects advertising channels such as social media advertising, search engine advertising, and display advertising, and generates an advertising strategy. Specifically, it selects social media advertising and generates a strategy to deliver social media ads to specific users. Social media advertising can deliver targeted ads based on users' interests and behavioral patterns. It can also select search engine advertising and generate a strategy to display ads based on specific keywords. Search engine advertising effectively reaches target users by displaying relevant ads when users search for specific keywords. Furthermore, it can select display advertising and generate a strategy to display ads on specific websites or apps. Display advertising can attract user attention by displaying visually appealing ads. By combining these advertising channels, the generation unit generates the optimal advertising strategy, effectively reaching identified potential users. This enables the discovery of target markets and the proposal of optimal advertising channel strategies, maximizing marketing effectiveness.

[0034] The data collection unit can collect data such as browsing history from e-commerce sites, social media, and websites. For example, the data collection unit can collect purchase history from e-commerce sites. For example, the data collection unit can collect the history of products purchased by users on e-commerce sites. The data collection unit can also collect content posted on social media. For example, the data collection unit can collect content posted by users on social media to understand users' interests. Furthermore, the data collection unit can also collect website browsing history. For example, the data collection unit can collect the history of web pages visited by users to understand users' behavior patterns. In this way, user behavior patterns can be understood by collecting information from diverse data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input purchase history from e-commerce sites into AI, and the AI ​​can collect the data.

[0035] The analysis unit can analyze data using machine learning or deep learning. For example, the analysis unit can analyze data using a neural network. For example, the analysis unit can analyze user behavior patterns using a neural network to identify interests. The analysis unit can also analyze data using a support vector machine. For example, the analysis unit can analyze user behavior patterns using a support vector machine to identify interests. Furthermore, the analysis unit can analyze data using a decision tree. For example, the analysis unit can analyze user behavior patterns using a decision tree to identify interests. This improves data accuracy by employing advanced analysis techniques. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input collected data into an AI, which can then analyze the data.

[0036] The identification unit can identify potential users with high similarity using clustering and classification algorithms. For example, the identification unit can identify potential users using K-means clustering. For example, the identification unit can group users using K-means clustering to identify potential users with high similarity. The identification unit can also identify potential users using decision trees. For example, the identification unit can classify users using decision trees to identify potential users with high similarity. Furthermore, the identification unit can also identify potential users using random forests. For example, the identification unit can classify users using random forests to identify potential users with high similarity. This improves the accuracy of potential user identification. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the analyzed data into AI, which can then identify potential users.

[0037] The generation unit can select advertising channels such as social media advertising, search engine advertising, and display advertising, and generate advertising strategies. For example, the generation unit can select social media advertising and generate an advertising strategy. For example, the generation unit can generate a strategy to deliver social media advertising to specific users. The generation unit can also select search engine advertising and generate an advertising strategy. For example, the generation unit can generate a strategy to deliver search engine advertising to specific users. Furthermore, the generation unit can also select display advertising and generate an advertising strategy. For example, the generation unit can generate a strategy to deliver display advertising to specific users. This maximizes the effectiveness of advertising investment by generating the optimal advertising channel strategy. 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 data on identified potential users into AI, and the AI ​​can generate the optimal advertising channel strategy.

[0038] The generation unit can generate the timing and frequency of ad delivery, as well as the content of the creative. For example, the generation unit can generate the timing of ad delivery. For example, the generation unit can generate the optimal timing for a specific user. The generation unit can also generate the frequency of ad delivery. For example, the generation unit can generate the optimal frequency for a specific user. Furthermore, the generation unit can also generate the content of the ad creative. For example, the generation unit can generate the optimal content for a specific user. By optimizing the timing and frequency of ad delivery and the content of the creative, the effectiveness of the ads is improved. 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 data of identified potential users into AI, and the AI ​​can generate the timing and frequency of ad delivery and the content of the creative.

[0039] The data collection unit can analyze a user's past online activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites the user has frequently visited in the past. For example, the data collection unit can collect data from social networking services (SNS) platforms where the user has spent a lot of time in the past. The data collection unit can also collect relevant data based on the user's past purchase history. This enables efficient data collection by selecting the optimal data collection method based on the user's past online activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past online activity history into AI, which can then select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can filter data based on keywords the user has recently searched for. The data collection unit can also collect data based on posts from influencers the user follows. This allows for the collection of highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into the AI, which can then filter the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of event information in the area where the user is currently located. For example, if the user is traveling, the data collection unit can prioritize the collection of tourist information for the travel destination. The data collection unit can also prioritize the collection of local news in the area where the user lives. In this way, by collecting data while considering the user's geographical location information, it is possible to prioritize the collection of data relevant to the region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on the content of posts from accounts that the user follows. For example, the data collection unit can collect data based on the activities of groups and communities that the user participates in. The data collection unit can also collect data related to content that the user shares. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on specific behavioral patterns during data analysis. For example, the analysis unit can perform detailed analysis on actions that users frequently perform. For example, the analysis unit can perform detailed analysis on actions that users perform during specific time periods. Furthermore, the analysis unit can also perform detailed analysis on actions that users perform using specific devices. This allows for efficient data analysis by adjusting the level of detail of the analysis based on specific behavioral patterns. 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 user behavioral pattern data into the AI, which can then adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase behavior data. For example, the analysis unit can apply a sentiment analysis algorithm to social media data. Furthermore, the analysis unit can apply a browsing pattern analysis algorithm to website browsing history data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. 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 the AI ​​can apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. The analysis unit can also prioritize the analysis of data during a specific event period to evaluate the impact of the event. This allows for the provision of real-time insights by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, which can then determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. For example, the analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can also group highly relevant data and analyze them all at once. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0047] The identification unit can improve the accuracy of identifying potential users by considering user relationships. For example, the identification unit can identify potential users by analyzing a user's relationships within social networks. For example, the identification unit can identify potential users by considering the relationships between a user's followers and the accounts they follow. The identification unit can also identify potential users based on a user's activities within online communities. This improves the accuracy of identifying potential users by considering user relationships. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user relationship data into AI, which can then improve the accuracy of identification.

[0048] The identification unit can identify potential users by considering the user's attribute information. For example, the identification unit can identify potential users based on attribute information such as the user's age, gender, and occupation. For example, the identification unit can identify potential users by considering the user's interests and hobbies. Furthermore, the identification unit can identify potential users based on the user's purchase history and browsing history. This improves the accuracy of potential user identification by considering the user's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's attribute information into AI, and the AI ​​can identify potential users.

[0049] The identification unit can identify potential users by considering the geographical distribution of users. For example, the identification unit can identify potential users based on the user's place of residence. For example, the identification unit can identify potential users by considering the user's travel destinations or business trip destinations. Furthermore, the identification unit can identify potential users based on the user's geographical movement patterns. In this way, by considering the geographical distribution of users, potential users related to a region can be identified. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user geographical distribution data into AI, and the AI ​​can identify potential users.

[0050] The identification unit can improve the accuracy of identification by referring to relevant literature when identifying potential users. For example, the identification unit can refer to relevant research papers and adjust the criteria for identifying potential users. For example, the identification unit can refer to industry reports and improve the accuracy of identifying potential users. The identification unit can also refer to market research data and improve the accuracy of identifying potential users. Thus, by referring to relevant literature, the accuracy of identifying potential users is improved. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input relevant literature data into AI, which can then improve the accuracy of identification.

[0051] The generation unit can analyze a user's past ad responses to select the optimal strategy when generating an advertising channel strategy. For example, the generation unit can select the optimal strategy based on data of ads the user has clicked on in the past. For example, the generation unit can select the optimal strategy based on ad data of products the user has purchased in the past. The generation unit can also select the optimal strategy based on data of ads the user has shared in the past. In this way, the optimal advertising strategy can be selected by analyzing the user's past ad responses. 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 user's past ad response data into AI, and the AI ​​can select the optimal advertising strategy.

[0052] The generation unit can customize advertising channel strategies based on the user's current areas of interest when generating them. For example, the generation unit can generate advertising strategies related to topics the user is currently interested in. For example, the generation unit can customize advertising strategies based on keywords the user has recently searched for. The generation unit can also generate advertising strategies based on posts from influencers the user follows. This allows for more effective ad delivery by customizing advertising strategies based on the user's current areas of interest. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's current areas of interest data into AI, which can then customize the advertising strategy.

[0053] The generation unit can select the optimal advertising channel strategy by considering the user's geographical location when generating the strategy. For example, the generation unit can prioritize advertising channels in the user's current location. For example, if the user is traveling, the generation unit can prioritize advertising channels in their travel destination. The generation unit can also prioritize local advertising channels in the user's residential area. This allows the generation unit to select the most relevant advertising strategy for the user's location by considering their geographical location. 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 user's geographical location information into AI, which can then select the optimal advertising strategy.

[0054] The generation unit can analyze a user's social media activity and propose an advertising channel strategy when generating it. For example, the generation unit can propose an advertising strategy based on the content of posts from accounts the user follows. For example, the generation unit can propose an advertising strategy based on the activities of groups and communities the user participates in. The generation unit can also propose an advertising strategy related to content the user has shared. This allows for the proposal of more effective advertising strategies by analyzing the user's social media activity. 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 user social media activity data into AI, which can then propose an advertising strategy.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze a user's past online activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites the user has frequently visited in the past. For example, the data collection unit can collect data from social networking services (SNS) platforms where the user has spent a lot of time in the past. The data collection unit can also collect relevant data based on the user's past purchase history. This enables efficient data collection by selecting the optimal data collection method based on the user's past online activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past online activity history into AI, which can then select the optimal data collection method.

[0057] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can filter data based on keywords the user has recently searched for. The data collection unit can also collect data based on posts from influencers the user follows. This allows for the collection of highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into the AI, which can then filter the data.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of event information in the area where the user is currently located. For example, if the user is traveling, the data collection unit can prioritize the collection of tourist information for the travel destination. The data collection unit can also prioritize the collection of local news in the area where the user lives. In this way, by collecting data while considering the user's geographical location information, it is possible to prioritize the collection of data relevant to the region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0059] The analysis unit can adjust the level of detail of the analysis based on specific behavioral patterns during data analysis. For example, the analysis unit can perform detailed analysis on actions that users frequently perform. For example, the analysis unit can perform detailed analysis on actions that users perform during specific time periods. Furthermore, the analysis unit can also perform detailed analysis on actions that users perform using specific devices. This allows for efficient data analysis by adjusting the level of detail of the analysis based on specific behavioral patterns. 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 user behavioral pattern data into the AI, which can then adjust the level of detail of the analysis.

[0060] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase behavior data. For example, the analysis unit can apply a sentiment analysis algorithm to social media data. Furthermore, the analysis unit can apply a browsing pattern analysis algorithm to website browsing history data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. 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 the AI ​​can apply an appropriate analysis algorithm.

[0061] The analysis unit can determine the priority of analysis based on the data collection timing during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. The analysis unit can also prioritize the analysis of data during a specific event period to evaluate the impact of the event. This allows for the provision of real-time insights by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, which can then determine the priority of analysis.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects online activity and purchasing behavior data. For example, it collects data such as browsing history from e-commerce sites, social media, and websites. The data collection unit collects purchase history on e-commerce sites, social media posts, and website browsing history to understand users' interests. Step 2: The analysis unit performs structured analysis on the data collected by the collection unit. For example, it analyzes the data using machine learning, deep learning, neural networks, support vector machines, decision trees, etc., to analyze user behavior patterns and identify interests. Step 3: The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. For example, potential users are identified using clustering and classification algorithms, K-means clustering, decision trees, random forests, etc. Step 4: The generation unit generates the optimal advertising channel strategy for the potential users identified by the identification unit. For example, it selects advertising channels such as social media advertising, search engine advertising, and display advertising, and generates the advertising strategy.

[0064] (Example of form 2) The target market discovery system according to an embodiment of the present invention is a system that uses AI to discover target markets and proposes an optimal advertising channel strategy. This target market discovery system collects a vast amount of market data, such as online activity and purchasing behavior data, and the AI ​​structures and analyzes the collected data to identify potential users with high similarity. Furthermore, the AI ​​generates an optimal advertising channel strategy for the identified potential users. This mechanism optimizes advertising investment and maximizes cost-effectiveness. In addition, the time spent on market research is significantly reduced and targeting accuracy is improved. For example, the target market discovery system collects a vast amount of market data, such as online activity and purchasing behavior data. In this process, information is collected from various data sources, such as users' website browsing history, purchase history, and social media activity. For example, this includes purchase history on e-commerce sites and content posted on social media. This makes it possible to understand user behavior patterns. Next, the target market discovery system uses AI to structure and analyze the collected data. The AI ​​analyzes the collected data and identifies user behavior patterns and interests. For example, it can identify users who have a high level of interest in a particular product and formulate an appropriate advertising strategy for those users. This allows for the precise identification of target markets. Furthermore, the target market discovery system uses AI to generate optimal advertising channel strategies for identified potential users. The AI ​​selects the most suitable advertising channels and formulates advertising strategies based on user behavior patterns and interests. For example, if it determines that social media advertising is effective for a particular user, it formulates a strategy to deliver social media ads to that user. This optimizes advertising investment and maximizes cost-effectiveness. For example, the reach of implementing companies increases by 200%, and advertising effectiveness increases by 50%. In addition, the time spent on market research is reduced by 75%. This makes target market discovery more efficient and improves the success rate of advertising campaigns. In this way, the target market discovery system optimizes advertising investment and maximizes cost-effectiveness.

[0065] The target market discovery system according to this embodiment comprises a collection unit, an analysis unit, an identification unit, and a generation unit. The collection unit collects online activity and purchasing behavior data. The collection unit collects data such as browsing history of e-commerce sites, social networking services (SNS), and websites. For example, the collection unit can collect purchase history on e-commerce sites. The collection unit can also collect content posted on SNS. Furthermore, the collection unit can also collect website browsing history. For example, the collection unit collects the history of web pages visited by a user to understand the user's interests. The analysis unit performs structured analysis on the data collected by the collection unit. The analysis unit analyzes the data using machine learning or deep learning, for example. The analysis unit can analyze the data using a neural network, for example. The analysis unit can also analyze the data using a support vector machine. Furthermore, the analysis unit can also analyze the data using a decision tree. For example, the analysis unit uses a neural network to analyze user behavior patterns and identify interests. The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. The identification unit identifies potential users using, for example, clustering or classification algorithms. The identification unit can identify potential users using, for example, K-means clustering. It can also identify potential users using decision trees. Furthermore, it can identify potential users using random forests. For example, the identification unit groups users using K-means clustering to identify potential users with high similarity. The generation unit generates the optimal advertising channel strategy for the potential users identified by the identification unit. The generation unit selects advertising channels such as social media advertising, search engine advertising, and display advertising, and generates an advertising strategy. For example, the generation unit can select social media advertising and generate an advertising strategy. It can also select search engine advertising and generate an advertising strategy. Furthermore, it can select display advertising and generate an advertising strategy. For example, the generation unit selects social media advertising and generates a strategy to deliver social media ads to specific users.This enables the target market discovery system according to the embodiment to discover target markets and propose optimal advertising channel strategies.

[0066] The data collection unit collects online activity and purchasing behavior data. For example, it collects data from e-commerce sites, social media, and website browsing history. Specifically, from e-commerce sites, it can collect detailed data such as the types of products purchased by users, purchase frequency, and purchase amount. This allows for an understanding of users' purchasing trends and preferences. From social media, it collects data such as the content of users' posts, likes and shares, and accounts they follow. This allows for an understanding of users' interests and social connections. Furthermore, from website browsing history, it collects data such as the pages users visited, the time spent on each page, and the links they clicked. This allows for an understanding of what kind of information users are interested in. The data collection unit collects this data in real time and stores it in a central database. Technologies such as cookies, tracking pixels, and API integration can be used for data collection. This allows the data collection unit to efficiently collect a wide range of data from diverse data sources and gain a detailed understanding of user behavior and interests.

[0067] The analysis unit performs structured analysis on the data collected by the collection unit. For example, the analysis unit uses machine learning and deep learning to analyze the data. Specifically, it uses neural networks to analyze the data and identify user behavior patterns and interests. A neural network consists of multiple perceptrons, processing input data layer by layer to obtain an output. This allows for highly accurate analysis of complex patterns and relationships. Furthermore, data can also be analyzed using support vector machines. Support vector machines map data into a high-dimensional space and classify it by finding the optimal classification boundary. This allows for highly accurate classification of user interests. Additionally, data can be analyzed using decision trees. Decision trees divide data based on conditions, forming a tree structure. This makes it easier to visually understand user behavior patterns. By combining these technologies, the analysis unit can analyze the collected data from multiple perspectives and gain a detailed understanding of user behavior and interests.

[0068] The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. The identification unit identifies potential users using, for example, clustering or classification algorithms. Specifically, it groups users using K-means clustering to identify potential users with high similarity. K-means clustering divides the data into K clusters and calculates the center point of each cluster. This allows users with similar characteristics to be classified into the same cluster. Potential users can also be identified using decision trees. Decision trees divide the data based on conditions and form a tree structure. This makes it easier to visually understand user characteristics and identify potential users with high similarity. Furthermore, potential users can also be identified using random forests. Random forests are a type of ensemble learning that combines multiple decision trees to make predictions, enabling highly accurate classification. By utilizing these technologies, the identification unit can efficiently identify potential users with high similarity from the analyzed data and uncover target markets.

[0069] The generation unit generates the optimal advertising channel strategy for potential users identified by the identification unit. The generation unit selects advertising channels such as social media advertising, search engine advertising, and display advertising, and generates an advertising strategy. Specifically, it selects social media advertising and generates a strategy to deliver social media ads to specific users. Social media advertising can deliver targeted ads based on users' interests and behavioral patterns. It can also select search engine advertising and generate a strategy to display ads based on specific keywords. Search engine advertising effectively reaches target users by displaying relevant ads when users search for specific keywords. Furthermore, it can select display advertising and generate a strategy to display ads on specific websites or apps. Display advertising can attract user attention by displaying visually appealing ads. By combining these advertising channels, the generation unit generates the optimal advertising strategy, effectively reaching identified potential users. This enables the discovery of target markets and the proposal of optimal advertising channel strategies, maximizing marketing effectiveness.

[0070] The data collection unit can collect data such as browsing history from e-commerce sites, social media, and websites. For example, the data collection unit can collect purchase history from e-commerce sites. For example, the data collection unit can collect the history of products purchased by users on e-commerce sites. The data collection unit can also collect content posted on social media. For example, the data collection unit can collect content posted by users on social media to understand users' interests. Furthermore, the data collection unit can also collect website browsing history. For example, the data collection unit can collect the history of web pages visited by users to understand users' behavior patterns. In this way, user behavior patterns can be understood by collecting information from diverse data sources. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input purchase history from e-commerce sites into AI, and the AI ​​can collect the data.

[0071] The analysis unit can analyze data using machine learning or deep learning. For example, the analysis unit can analyze data using a neural network. For example, the analysis unit can analyze user behavior patterns using a neural network to identify interests. The analysis unit can also analyze data using a support vector machine. For example, the analysis unit can analyze user behavior patterns using a support vector machine to identify interests. Furthermore, the analysis unit can analyze data using a decision tree. For example, the analysis unit can analyze user behavior patterns using a decision tree to identify interests. This improves data accuracy by employing advanced analysis techniques. Some or all of the above-described processes in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input collected data into an AI, which can then analyze the data.

[0072] The identification unit can identify potential users with high similarity using clustering and classification algorithms. For example, the identification unit can identify potential users using K-means clustering. For example, the identification unit can group users using K-means clustering to identify potential users with high similarity. The identification unit can also identify potential users using decision trees. For example, the identification unit can classify users using decision trees to identify potential users with high similarity. Furthermore, the identification unit can also identify potential users using random forests. For example, the identification unit can classify users using random forests to identify potential users with high similarity. This improves the accuracy of potential user identification. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the analyzed data into AI, which can then identify potential users.

[0073] The generation unit can select advertising channels such as social media advertising, search engine advertising, and display advertising, and generate advertising strategies. For example, the generation unit can select social media advertising and generate an advertising strategy. For example, the generation unit can generate a strategy to deliver social media advertising to specific users. The generation unit can also select search engine advertising and generate an advertising strategy. For example, the generation unit can generate a strategy to deliver search engine advertising to specific users. Furthermore, the generation unit can also select display advertising and generate an advertising strategy. For example, the generation unit can generate a strategy to deliver display advertising to specific users. This maximizes the effectiveness of advertising investment by generating the optimal advertising channel strategy. 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 data on identified potential users into AI, and the AI ​​can generate the optimal advertising channel strategy.

[0074] The generation unit can generate the timing and frequency of ad delivery, as well as the content of the creative. For example, the generation unit can generate the timing of ad delivery. For example, the generation unit can generate the optimal timing for a specific user. The generation unit can also generate the frequency of ad delivery. For example, the generation unit can generate the optimal frequency for a specific user. Furthermore, the generation unit can also generate the content of the ad creative. For example, the generation unit can generate the optimal content for a specific user. By optimizing the timing and frequency of ad delivery and the content of the creative, the effectiveness of the ads is improved. 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 data of identified potential users into AI, and the AI ​​can generate the timing and frequency of ad delivery and the content of the creative.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also collect data in real time if the user is excited to grasp their immediate reaction. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into an AI, which can then adjust the timing of data collection.

[0076] The data collection unit can analyze a user's past online activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites the user has frequently visited in the past. For example, the data collection unit can collect data from social networking services (SNS) platforms where the user has spent a lot of time in the past. The data collection unit can also collect relevant data based on the user's past purchase history. This enables efficient data collection by selecting the optimal data collection method based on the user's past online activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past online activity history into AI, which can then select the optimal data collection method.

[0077] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can filter data based on keywords the user has recently searched for. The data collection unit can also collect data based on posts from influencers the user follows. This allows for the collection of highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into the AI, which can then filter the data.

[0078] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to relaxing content. If the user is excited, the data collection unit may prioritize collecting entertainment-related data. Similarly, if the user is tired, the data collection unit may prioritize collecting data related to refreshing content. By prioritizing the data to collect according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of event information in the area where the user is currently located. For example, if the user is traveling, the data collection unit can prioritize the collection of tourist information for the travel destination. The data collection unit can also prioritize the collection of local news in the area where the user lives. In this way, by collecting data while considering the user's geographical location information, it is possible to prioritize the collection of data relevant to the region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on the content of posts from accounts that the user follows. For example, the data collection unit can collect data based on the activities of groups and communities that the user participates in. The data collection unit can also collect data related to content that the user shares. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, which can then collect relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a concise analysis and provide the main points. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the data analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the data analysis method.

[0082] The analysis unit can adjust the level of detail of the analysis based on specific behavioral patterns during data analysis. For example, the analysis unit can perform detailed analysis on actions that users frequently perform. For example, the analysis unit can perform detailed analysis on actions that users perform during specific time periods. Furthermore, the analysis unit can also perform detailed analysis on actions that users perform using specific devices. This allows for efficient data analysis by adjusting the level of detail of the analysis based on specific behavioral patterns. 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 user behavioral pattern data into the AI, which can then adjust the level of detail of the analysis.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase behavior data. For example, the analysis unit can apply a sentiment analysis algorithm to social media data. Furthermore, the analysis unit can apply a browsing pattern analysis algorithm to website browsing history data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. 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 the AI ​​can apply an appropriate analysis algorithm.

[0084] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, highly visible displays are possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the display method of the analysis results.

[0085] The analysis unit can determine the priority of analysis based on the data collection timing during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. The analysis unit can also prioritize the analysis of data during a specific event period to evaluate the impact of the event. This allows for the provision of real-time insights by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, which can then determine the priority of analysis.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during data analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. For example, the analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can also group highly relevant data and analyze them all at once. This allows for efficient data analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into the AI, which can then adjust the order of analysis.

[0087] The identification unit can estimate the user's emotions and adjust the criteria for identifying potential users based on the estimated emotions. For example, if the user is relaxed, the identification unit can identify potential users using broad criteria. If the user is in a hurry, the identification unit can identify potential users using strict criteria. Furthermore, if the user is excited, the identification unit can identify potential users by emphasizing emotional responses. This allows for the identification of more appropriate potential users by adjusting the criteria for identifying potential users according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into an AI, which can then adjust the criteria for identifying potential users.

[0088] The identification unit can improve the accuracy of identifying potential users by considering user relationships. For example, the identification unit can identify potential users by analyzing a user's relationships within social networks. For example, the identification unit can identify potential users by considering the relationships between a user's followers and the accounts they follow. The identification unit can also identify potential users based on a user's activities within online communities. This improves the accuracy of identifying potential users by considering user relationships. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user relationship data into AI, which can then improve the accuracy of identification.

[0089] The identification unit can identify potential users by considering the user's attribute information. For example, the identification unit can identify potential users based on attribute information such as the user's age, gender, and occupation. For example, the identification unit can identify potential users by considering the user's interests and hobbies. Furthermore, the identification unit can identify potential users based on the user's purchase history and browsing history. This improves the accuracy of potential user identification by considering the user's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the user's attribute information into AI, and the AI ​​can identify potential users.

[0090] The identification unit can estimate the user's emotions and adjust the display order of identified potential users based on the estimated user emotions. For example, if the user is relaxed, the identification unit can provide a display order that includes detailed information. For example, if the user is in a hurry, the identification unit can provide a display order that gets straight to the point. Furthermore, if the user is excited, the identification unit can also provide a visually appealing display order. This allows for more appropriate information to be provided by adjusting the display order of potential users according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI, or not using AI. For example, the identification unit can input user emotion data into AI, and the AI ​​can adjust the display order of identified potential users.

[0091] The identification unit can identify potential users by considering the geographical distribution of users. For example, the identification unit can identify potential users based on the user's place of residence. For example, the identification unit can identify potential users by considering the user's travel destinations or business trip destinations. Furthermore, the identification unit can identify potential users based on the user's geographical movement patterns. In this way, by considering the geographical distribution of users, potential users related to a region can be identified. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input user geographical distribution data into AI, and the AI ​​can identify potential users.

[0092] The identification unit can improve the accuracy of identification by referring to relevant literature when identifying potential users. For example, the identification unit can refer to relevant research papers and adjust the criteria for identifying potential users. For example, the identification unit can refer to industry reports and improve the accuracy of identifying potential users. The identification unit can also refer to market research data and improve the accuracy of identifying potential users. Thus, by referring to relevant literature, the accuracy of identifying potential users is improved. Some or all of the above processing in the identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input relevant literature data into AI, which can then improve the accuracy of identification.

[0093] The generation unit can estimate the user's emotions and adjust the method of generating advertising channel strategies based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate a detailed advertising channel strategy. If the user is in a hurry, the generation unit can generate a concise advertising channel strategy. Furthermore, if the user is excited, the generation unit can generate a visually appealing advertising channel strategy. This allows for the provision of more effective advertising strategies by adjusting the method of generating advertising channel strategies according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into an AI, which can then adjust the method of generating advertising channel strategies.

[0094] The generation unit can analyze a user's past ad responses to select the optimal strategy when generating an advertising channel strategy. For example, the generation unit can select the optimal strategy based on data of ads the user has clicked on in the past. For example, the generation unit can select the optimal strategy based on ad data of products the user has purchased in the past. The generation unit can also select the optimal strategy based on data of ads the user has shared in the past. In this way, the optimal advertising strategy can be selected by analyzing the user's past ad responses. 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 user's past ad response data into AI, and the AI ​​can select the optimal advertising strategy.

[0095] The generation unit can customize advertising channel strategies based on the user's current areas of interest when generating them. For example, the generation unit can generate advertising strategies related to topics the user is currently interested in. For example, the generation unit can customize advertising strategies based on keywords the user has recently searched for. The generation unit can also generate advertising strategies based on posts from influencers the user follows. This allows for more effective ad delivery by customizing advertising strategies based on the user's current areas of interest. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's current areas of interest data into AI, which can then customize the advertising strategy.

[0096] The generation unit can estimate the user's emotions and prioritize advertising channel strategies based on those emotions. For example, if the user is relaxed, the generation unit might prioritize detailed advertising channel strategies. If the user is in a hurry, for example, the generation unit might prioritize concise advertising channel strategies. Furthermore, if the user is excited, the generation unit might prioritize visually appealing advertising channel strategies. This allows for more effective ad delivery by prioritizing advertising channel strategies according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI could be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into an AI, which can then determine the priority of advertising channel strategies.

[0097] The generation unit can select the optimal advertising channel strategy by considering the user's geographical location when generating the strategy. For example, the generation unit can prioritize advertising channels in the user's current location. For example, if the user is traveling, the generation unit can prioritize advertising channels in their travel destination. The generation unit can also prioritize local advertising channels in the user's residential area. This allows the generation unit to select the most relevant advertising strategy for the user's location by considering their geographical location. 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 user's geographical location information into AI, which can then select the optimal advertising strategy.

[0098] The generation unit can analyze a user's social media activity and propose an advertising channel strategy when generating it. For example, the generation unit can propose an advertising strategy based on the content of posts from accounts the user follows. For example, the generation unit can propose an advertising strategy based on the activities of groups and communities the user participates in. The generation unit can also propose an advertising strategy related to content the user has shared. This allows for the proposal of more effective advertising strategies by analyzing the user's social media activity. 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 user social media activity data into AI, which can then propose an advertising strategy.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. The data collection unit can also collect data in real time if the user is excited to grasp their immediate reaction. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's emotion data into an AI, which can then adjust the timing of data collection.

[0101] The data collection unit can analyze a user's past online activity history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from websites the user has frequently visited in the past. For example, the data collection unit can collect data from social networking services (SNS) platforms where the user has spent a lot of time in the past. The data collection unit can also collect relevant data based on the user's past purchase history. This enables efficient data collection by selecting the optimal data collection method based on the user's past online activity history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past online activity history into AI, which can then select the optimal data collection method.

[0102] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can filter data based on keywords the user has recently searched for. The data collection unit can also collect data based on posts from influencers the user follows. This allows for the collection of highly relevant data by filtering data based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into the AI, which can then filter the data.

[0103] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may prioritize collecting data related to relaxing content. If the user is excited, the data collection unit may prioritize collecting entertainment-related data. Similarly, if the user is tired, the data collection unit may prioritize collecting data related to refreshing content. By prioritizing the data to collect according to the user's emotions, more appropriate data can be collected. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI, which can then determine the priority of data to collect.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of event information in the area where the user is currently located. For example, if the user is traveling, the data collection unit can prioritize the collection of tourist information for the travel destination. The data collection unit can also prioritize the collection of local news in the area where the user lives. In this way, by collecting data while considering the user's geographical location information, it is possible to prioritize the collection of data relevant to the region. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, which can then prioritize the collection of highly relevant data.

[0105] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a concise analysis and provide the main points. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. In this way, by adjusting the data analysis method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI, and the AI ​​can adjust the data analysis method.

[0106] The analysis unit can adjust the level of detail of the analysis based on specific behavioral patterns during data analysis. For example, the analysis unit can perform detailed analysis on actions that users frequently perform. For example, the analysis unit can perform detailed analysis on actions that users perform during specific time periods. Furthermore, the analysis unit can also perform detailed analysis on actions that users perform using specific devices. This allows for efficient data analysis by adjusting the level of detail of the analysis based on specific behavioral patterns. 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 user behavioral pattern data into the AI, which can then adjust the level of detail of the analysis.

[0107] The analysis unit can apply different analysis algorithms depending on the data category during data analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase behavior data. For example, the analysis unit can apply a sentiment analysis algorithm to social media data. Furthermore, the analysis unit can apply a browsing pattern analysis algorithm to website browsing history data. By applying different analysis algorithms depending on the data category, the accuracy of the analysis is improved. 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 the AI ​​can apply an appropriate analysis algorithm.

[0108] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is tense, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, highly visible displays are possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the AI, and the AI ​​can adjust the display method of the analysis results.

[0109] The analysis unit can determine the priority of analysis based on the data collection timing during data analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. For example, the analysis unit can analyze historical data to grasp long-term trends. The analysis unit can also prioritize the analysis of data during a specific event period to evaluate the impact of the event. This allows for the provision of real-time insights by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into the AI, which can then determine the priority of analysis.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects online activity and purchasing behavior data. For example, it collects data such as browsing history from e-commerce sites, social media, and websites. The data collection unit collects purchase history on e-commerce sites, social media posts, and website browsing history to understand users' interests. Step 2: The analysis unit performs structured analysis on the data collected by the collection unit. For example, it analyzes the data using machine learning, deep learning, neural networks, support vector machines, decision trees, etc., to analyze user behavior patterns and identify interests. Step 3: The identification unit identifies potential users with high similarity based on the data analyzed by the analysis unit. For example, potential users are identified using clustering and classification algorithms, K-means clustering, decision trees, random forests, etc. Step 4: The generation unit generates the optimal advertising channel strategy for the potential users identified by the identification unit. For example, it selects advertising channels such as social media advertising, search engine advertising, and display advertising, and generates the advertising strategy.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the computer 36 of the smart device 14 and collects online activity and purchasing behavior data. The analysis unit is implemented by the identification processing unit 290 of the data processing device 12 and performs structured analysis on the collected data. The identification unit is implemented by the identification processing unit 290 of the data processing device 12 and identifies potential users based on the analyzed data. The generation unit is implemented by the control unit 46A of the smart device 14 and generates an optimal advertising channel strategy for the identified potential users. 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.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the smart glasses 214 and collects online activity and purchasing behavior data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs structured analysis on the collected data. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies potential users based on the analyzed data. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates an optimal advertising channel strategy for the identified potential users. 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.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the headset terminal 314 and collects online activity and purchasing behavior data. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and performs structured analysis on the collected data. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12 and identifies potential users based on the analyzed data. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates an optimal advertising channel strategy for the identified potential users. 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.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, identification unit, and generation unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the computer 36 of the robot 414 and collects online activity and purchasing behavior data. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs structured analysis on the collected data. The identification unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and identifies potential users based on the analyzed data. The generation unit is implemented, for example, by the control unit 46A of the robot 414 and generates an optimal advertising channel strategy for the identified potential users. 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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."

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] (Note 1) The data collection department collects online activity and purchasing behavior data, An analysis unit performs structured analysis on the data collected by the aforementioned collection unit, An identification unit identifies potential users with high similarity based on the data analyzed by the aforementioned analysis unit, The system includes a generation unit that generates an optimal advertising channel strategy for potential users identified by the identification unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as browsing history from e-commerce sites, social media, and other websites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze data using machine learning and deep learning. The system described in Appendix 1, characterized by the features described herein. (Note 4) The specified part is, Identify potential users with high similarity using clustering and classification algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Select advertising channels such as social media ads, search engine ads, and display ads, and generate an advertising strategy. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate ad delivery timing, frequency, and creative content. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past online activity history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During data analysis, adjust the level of detail based on specific behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, When analyzing data, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, When analyzing data, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During data analysis, adjust the order of analysis based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The specified part is, We estimate user sentiment and adjust the criteria for identifying potential users based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The specified part is, When identifying potential users, consider user relationships to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The specified part is, When identifying potential users, the identification process takes into account user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The specified part is, It estimates the user's sentiment and adjusts the display order of potential users identified based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The specified part is, When identifying potential users, the geographical distribution of users should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The specified part is, When identifying potential users, refer to relevant literature to improve accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is We estimate user sentiment and adjust how advertising channel strategies are generated based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating an advertising channel strategy, the system analyzes users' past ad responses to select the optimal strategy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating advertising channel strategies, customize the strategies based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is We estimate user sentiment and prioritize advertising channel strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating advertising channel strategies, the optimal strategy is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating advertising channel strategies, we analyze users' social media activity and propose strategies accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 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 collection department collects online activity and purchasing behavior data, An analysis unit performs structured analysis on the data collected by the aforementioned collection unit, An identification unit identifies potential users with high similarity based on the data analyzed by the aforementioned analysis unit, The system includes a generation unit that generates an optimal advertising channel strategy for potential users identified by the identification unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as browsing history from e-commerce sites, social media, and other websites. The system according to feature 1.

3. The aforementioned analysis unit, Analyze data using machine learning and deep learning. The system according to feature 1.

4. The specified part is, Identify potential users with high similarity using clustering and classification algorithms. The system according to feature 1.

5. The generating unit is Select advertising channels such as social media ads, search engine ads, and display ads, and generate an advertising strategy. The system according to feature 1.

6. The generating unit is Generate ad delivery timing, frequency, and creative content. The system according to feature 1.

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

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

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