system

Abstract

The system according to this embodiment aims to analyze web campaign data and propose optimal advertising delivery strategies and content improvements. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a learning unit. The collection unit collects web campaign data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes the optimal advertising delivery strategy and content improvement based on the analysis results obtained by the analysis unit. The learning unit learns user responses in real time.

Description

Technical Field

[0006] , ,

[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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a 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, there is a problem that it is difficult to effectively analyze Web campaign data and propose an optimal advertising distribution strategy or content improvement. <l000027> The system according to the embodiment aims to analyze Web campaign data and propose an optimal advertising distribution strategy or content improvement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a learning unit. The data collection unit collects web campaign data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes optimal advertising delivery strategies and content improvements based on the analysis results obtained by the analysis unit. The learning unit learns user responses in real time. [Effects of the Invention]

[0007] The system according to this embodiment can analyze web campaign data and propose optimal advertising delivery strategies and content improvements. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F governs communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The campaign optimization agent according to an embodiment of the present invention is a system in which AI analyzes web campaign data and proposes optimal advertising delivery strategies and content improvements based on customer behavior patterns and responses. This campaign optimization agent collects web campaign data and the AI ​​analyzes it. Next, the AI ​​learns customer behavior patterns and responses and proposes optimal advertising delivery strategies and content improvements. Furthermore, it learns user responses in real time and aims to increase click-through rates and conversion rates. For example, the campaign optimization agent collects web campaign data. At this time, it collects detailed data such as click-through rates and conversion rates. For example, it collects data such as how many times a particular ad was clicked and how many users converted. This makes it possible to understand the effectiveness of the campaign. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and learns customer behavior patterns and responses. For example, it learns what kind of users responded to a particular ad and what actions they took. This makes it possible to understand customer behavior patterns. Furthermore, the AI ​​proposes optimal advertising delivery strategies and content improvements based on the learned customer behavior patterns and responses. For example, it suggests what kind of ads would be most effective for specific customers, and what kind of content improvements would increase click-through rates and conversion rates. This maximizes the effectiveness of campaigns. Furthermore, the AI ​​learns user responses in real time, aiming to increase click-through rates and conversion rates. For instance, it learns user responses in real time during a campaign and improves ad delivery strategies and content as needed. This allows for continuous improvement of campaign effectiveness. This mechanism maximizes the effectiveness of advertising campaigns and achieves a high ROI. For example, an average increase of 30% in click-through rates and a 25% increase in conversion rates can be expected. A 50% improvement in ROI maximization is also predicted. This enables campaign optimization agents to efficiently collect, analyze, suggest, and learn from web campaign data.

[0029] The campaign optimization agent according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a learning unit. The data collection unit collects web campaign data. The data collection unit collects detailed data such as click-through rates and conversion rates. The data collection unit collects data such as how many times a particular ad was clicked and how many users converted. The data collection unit can also collect user behavior data and response data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data and learns customer behavior patterns and responses. The analysis unit learns, for example, what kind of users responded to a particular ad and what actions they took. The analysis unit can also cluster the data to understand customer behavior patterns. The proposal unit proposes optimal ad delivery strategies and content improvements based on the analysis results obtained by the analysis unit. The proposal unit proposes, for example, what kind of ads would be effective to deliver to specific customers and what kind of content improvements would increase click-through rates and conversion rates. The proposal unit can use AI to, for example, propose advertising delivery strategies and content improvements. The learning unit learns user responses in real time. The learning unit learns user responses in real time during the campaign and improves advertising delivery strategies and content as needed. The learning unit can also collect user response data in real time and learn using AI. As a result, the campaign optimization agent according to this embodiment can efficiently collect, analyze, propose, and learn from web campaign data.

[0030] The data collection unit collects web campaign data. Specifically, the data collection unit collects detailed data such as click-through rates and conversion rates. For example, it collects data such as how many times a particular ad was clicked and how many users converted. This includes metrics such as the number of times an ad was displayed (impressions), the number of clicks, click-through rate (CTR), number of conversions, and conversion rate (CVR). Furthermore, the data collection unit can also collect user behavior data and response data. For example, it collects data such as what pages a user viewed after clicking an ad and what actions they took (e.g., did they purchase a product or fill out a form). This allows the data collection unit to understand detailed user behavior patterns. The data collection unit collects this data in real time and sends it to a central database. The database centrally manages the collected data and makes it accessible to the analysis and proposal units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection to provide flexible responses tailored to specific campaigns and user segments. For example, it can collect more detailed data for specific time periods or specific user groups. This allows the data collection unit to efficiently and effectively collect data and provide the information necessary to optimize the campaign.

[0031] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the collected data to learn customer behavior patterns and responses. For example, it learns what kind of users responded to specific advertisements and what actions they took. The analysis unit can understand customer behavior patterns by clustering the data. Clustering is a method of dividing data into groups with similar characteristics, which allows for the identification of different customer segments. For example, it can identify customer groups that show a high response to specific advertisements or customer groups that have a high conversion rate during specific time periods. Furthermore, the analysis unit can analyze the data using AI. The AI ​​uses machine learning algorithms to extract patterns and trends from the data and predict customer behavior. For example, based on past data, it can learn under what conditions a particular advertisement was effective and reflect this in future advertising delivery strategies. This allows the analysis unit to quickly and accurately analyze the collected data and understand customer behavior patterns. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The Proposal Department proposes optimal advertising delivery strategies and content improvements based on the analysis results obtained by the Analysis Department. Specifically, it proposes what kind of ads would be most effective for specific customers, and what kind of content improvements would increase click-through rates and conversion rates. The Proposal Department can use AI to make these proposals. Based on the data provided by the Analysis Department, the AI ​​calculates the optimal advertising delivery strategy and generates specific proposals. For example, it proposes which ads should be delivered at which time of day to a specific customer segment for maximum effectiveness. It also makes specific suggestions for content improvements. For example, it proposes specific actions to improve click-through rates and conversion rates, such as changing ad copy and images, or improving the landing page design. Furthermore, the Proposal Department can evaluate the effectiveness of its proposals and continuously improve them. For example, it collects the results of advertising delivery and content improvements implemented based on the proposals and evaluates their effectiveness. Based on the evaluation results, it can revise its proposals and make more effective suggestions. In this way, the Proposal Department can always provide optimal proposals based on the latest data and analysis results, maximizing the effectiveness of campaigns.

[0033] The learning unit learns user responses in real time. Specifically, it learns user responses in real time during the campaign and improves advertising strategies and content as needed. The learning unit can collect user response data in real time and learn using AI. The AI ​​uses machine learning algorithms to extract patterns and trends from user response data and identify areas for improvement in advertising strategies and content. For example, it learns how users reacted to specific ads and what actions they took, and adjusts the advertising strategy based on the results. This allows the learning unit to provide the optimal advertising strategy in real time during the campaign. Furthermore, the learning unit can collect user feedback and continuously improve the accuracy and effectiveness of its learning. For example, it reviews areas for improvement in advertising strategies and content based on user feedback and provides more effective strategies. In addition, the learning unit can perform highly accurate learning based on a wider range of data by integrating data from multiple campaigns. This allows the learning unit to always provide the optimal advertising strategy based on the latest data and user responses, maximizing the effectiveness of campaigns.

[0034] The data collection unit can collect detailed data such as click-through rates and conversion rates. For example, the data collection unit collects detailed data such as click-through rates and conversion rates. For example, the data collection unit collects data such as how many times a particular ad was clicked and how many users converted. The data collection unit can also collect user behavior data and response data. This allows for understanding the effectiveness of a campaign by collecting detailed data. Detailed data includes, but is not limited to, click-through rates, conversion rates, and time spent on the site. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input detailed data such as click-through rates and conversion rates into a generating AI and have the generating AI perform the data collection.

[0035] The analytics unit can analyze the collected data and learn customer behavior patterns and responses. For example, the analytics unit can analyze the collected data and learn customer behavior patterns and responses. For example, the analytics unit can learn what kind of users responded to a particular advertisement and what actions they took. For example, the analytics unit can cluster the data to understand customer behavior patterns. By learning customer behavior patterns and responses, it becomes possible to develop effective advertising strategies and improve content. Customer behavior patterns include, but are not limited to, click frequency and page transition order. Responses include, but are not limited to, clicks, purchases, and comments. Some or all of the above processing in the analytics unit may be performed using, for example, AI, or not using AI. For example, the analytics unit can input the collected data into a generating AI and have the generating AI perform the learning of customer behavior patterns and responses.

[0036] The proposal department can propose optimal advertising delivery strategies and content improvements based on learned customer behavior patterns and responses. For example, the proposal department can propose what kind of ads would be effective for specific customers, or what kind of content improvements would increase click-through rates and conversion rates. The proposal department can also use AI to propose advertising delivery strategies and content improvements. This maximizes the effectiveness of campaigns by proposing optimal advertising delivery strategies and content improvements. Optimal advertising delivery strategies include, but are not limited to, methods for selecting target audiences and timing of ad delivery. Content improvements include, but are not limited to, which parts of the content to improve and criteria for evaluating improvements. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input learned customer behavior patterns and responses into a generating AI and have the generating AI execute proposals for optimal advertising delivery strategies and content improvements.

[0037] The learning unit can learn user responses in real time and improve advertising strategies and content as needed. For example, the learning unit can learn user responses in real time and improve advertising strategies and content as needed. For example, the learning unit can learn user responses in real time during a campaign and improve advertising strategies and content as needed. The learning unit can also, for example, collect user response data in real time and learn using AI. This allows for continuous improvement of campaign effectiveness by learning user responses in real time and improving advertising strategies and content. "Real time" includes, but is not limited to, data update frequency and reflection time. User responses include, but are not limited to, clicks, purchases, and comments. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can input real-time collected user response data into a generating AI and have the generating AI implement improvements to advertising strategies and content.

[0038] The data collection unit can analyze a user's past click history and select the optimal data collection method. For example, the data collection unit can analyze a user's past click history and select the optimal data collection method. For example, the data collection unit can analyze patterns of ads that a user has clicked in the past and enhance data collection for similar ads. For example, the data collection unit can concentrate data collection during specific time periods based on the user's click history. For example, the data collection unit can prioritize data collection on specific devices or browsers based on the user's click history. This allows the optimal data collection method to be selected by analyzing the user's past click history. The optimal data collection method includes, but is not limited to, methods for analyzing click history and data collection techniques. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past click history data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the user's current interests during data collection. For example, the data collection unit filters data based on the user's current interests during data collection. For example, the data collection unit prioritizes collecting data related to topics the user is currently interested in. For example, the data collection unit immediately adjusts the types of data collected if the user's interests change. For example, the data collection unit filters and collects highly relevant advertising data based on the user's interests. This allows for the collection of highly relevant data by filtering data based on the user's current interests. The user's current interests include, but are not limited to, browsing history and search keywords. Filtering includes, but are not limited to, methods for selecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's current interests data into a generating AI and have the generating AI perform data filtering.

[0040] 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 prioritizes the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit prioritizes the collection of advertising data related to that region. For example, the data collection unit collects region-specific trend data based on the user's location information. For example, if the user is on the move, the data collection unit collects data related to the destination region. In this way, by collecting data while considering the user's geographical location information, region-specific trend data can be collected. The user's geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant data includes, but is not limited to, region-specific trend data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] 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 analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by a user on social media. For example, the data collection unit can collect advertising data of interest based on a user's social media reactions. For example, the data collection unit can analyze the activities of a user's followers and friends and collect relevant data. This allows for the collection of highly relevant data by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posts and the number of likes. Relevant data includes, but is not limited to, social media trend data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. For example, the analysis unit can prioritize the analysis of high-importance data and provide results quickly. For example, the analysis unit can apply multiple analysis methods to high-importance data to improve accuracy. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, business impact and data reliability. The level of detail of the analysis includes, but is not limited to, detailed analysis and summary analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes click-through rates and conversion rates to advertising data. For example, the analysis unit can apply an algorithm that analyzes behavioral patterns to user behavior data. For example, the analysis unit can apply an algorithm that analyzes engagement rates to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Data categories include, but are not limited to, text data and numerical data. Analysis algorithms include, but are not limited to, clustering algorithms and regression analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI execute the application of analysis algorithms.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide real-time results. For example, the analysis unit can analyze trends based on historical data to grasp long-term trends. For example, the analysis unit can focus on analyzing data collected during a specific period to evaluate the campaign effect during that period. This enables efficient analysis by determining the priority of analysis based on the data collection timing. The data collection timing includes, but is not limited to, the latest data and historical data. The analysis priority includes, but is not limited to, importance and relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and provide results quickly. For example, the analysis unit can prioritize the analysis of important data, postponing the analysis of less relevant data. For example, the analysis unit can group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, but is not limited to, correlation and causation. The order of analysis includes, but is not limited to, importance and relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The proposal unit can adjust the level of detail of a proposal based on the importance of the advertisement. For example, the proposal unit can adjust the level of detail of a proposal based on the importance of the advertisement. For example, the proposal unit can provide detailed proposals for high-importance advertisements and simplified proposals for low-importance advertisements. For example, the proposal unit can prioritize proposals for high-importance advertisements and provide results quickly. For example, the proposal unit can provide multiple proposals for high-importance advertisements, offering choices. This allows for efficient proposals by adjusting the level of detail of proposals based on the importance of the advertisements. The importance of an advertisement includes, but is not limited to, business impact and advertising effectiveness. The level of detail of a proposal includes, but is not limited to, detailed proposals and summary proposals. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the importance of the advertisements into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0047] The proposal unit can apply different proposal algorithms depending on the ad category when making a proposal. For example, the proposal unit applies different proposal algorithms depending on the ad category when making a proposal. For example, the proposal unit selects the optimal proposal algorithm depending on the ad category. For example, the proposal unit applies different proposal methods for each ad category to make effective proposals. For example, the proposal unit determines the priority of proposals based on the ad category. This makes it possible to make more effective proposals by applying different proposal algorithms depending on the ad category. Ad categories include, but are not limited to, text ads and banner ads. Proposal algorithms include, but are not limited to, recommendation algorithms and optimization algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the ad category into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0048] The proposal unit can determine the priority of proposals based on the timing of ad delivery when making a proposal. For example, the proposal unit determines the priority of proposals based on the timing of ad delivery when making a proposal. The proposal unit makes the best proposal based on the timing of ad delivery. For example, the proposal unit makes effective proposals by applying different proposal methods for each timing of ad delivery. The proposal unit determines the priority of proposals based on the timing of ad delivery. This enables efficient proposals by determining the priority of proposals based on the timing of ad delivery. The timing of ad delivery includes, but is not limited to, the campaign period and the activity time of the target audience. The priority of proposals includes, but is not limited to, importance and relevance. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input ad delivery timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0049] The suggestion unit can adjust the order of suggestions based on the relevance of the advertisements when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the advertisements when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant advertisements and provide results quickly. For example, the suggestion unit can prioritize suggesting important advertisements and postpone suggesting less relevant advertisements. For example, the suggestion unit can group highly relevant advertisements and suggest them all at once. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the advertisements. Ad relevance includes, but is not limited to, the interests of the target audience and past responses. The order of suggestions includes, but is not limited to, importance and relevance. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input ad relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0050] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit adjusts the parameters of the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data to improve the accuracy of the learning algorithm. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, data storage methods and reference methods. Optimization of the learning algorithm includes, but is not limited to, parameter adjustment and algorithm selection. Some or all of the above processes in the learning unit may be performed using, for example, AI, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0051] The learning unit can immediately reflect data collected in real time during learning. For example, the learning unit immediately reflects data collected in real time during learning. For example, the learning unit immediately reflects data collected in real time into the learning algorithm. For example, the learning unit updates the learning algorithm based on data collected in real time. For example, the learning unit analyzes data collected in real time to improve the accuracy of the learning algorithm. As a result, the accuracy of learning is improved by immediately reflecting data collected in real time. Real time includes, but is not limited to, the frequency of data updates and the time until reflection. Immediate reflection includes, but is not limited to, the speed of data processing and the timing of reflection. Some or all of the above processes in the learning unit may be performed using, for example, AI, or without using AI. For example, the learning unit can input data collected in real time into a generating AI and have the generating AI perform the process of immediately reflecting the data.

[0052] The learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can give a high weight to the latest data to emphasize real-time responses. For example, the learning unit can give a low weight to historical data to grasp long-term trends. For example, the learning unit can give appropriate weights to data collected during a specific period to evaluate the campaign effect during that period. This improves the accuracy of learning by weighting the training data based on when the data was collected. The data collection period includes, but is not limited to, the latest data and historical data. The weighting of the training data includes, but is not limited to, data importance and relevance. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the data collection period into a generating AI and have the generating AI perform the weighting of the training data.

[0053] The learning unit can improve the accuracy of learning by integrating information from different data sources during learning. For example, the learning unit improves the accuracy of learning by integrating information from different data sources during learning. The learning unit learns by integrating information from different data sources, such as advertising data, user behavior data, and social media data. For example, the learning unit optimizes the learning algorithm based on information from different data sources. The learning unit improves the accuracy of the learning algorithm by analyzing information from different data sources. As a result, the accuracy of learning is improved by learning by integrating information from different data sources. Different data sources include, but are not limited to, advertising data, user behavior data, and social media data. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input information from different data sources into a generating AI and have the generating AI perform the information integration.

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

[0055] The data collection unit can collect data while taking into account the user's geographical location information. For example, if a user is in a specific region, it can prioritize the collection of advertising data related to that region. It can also collect region-specific trend data based on the user's location information. Furthermore, if a user is on the move, it can collect data related to the region they are moving to. In this way, by collecting data while taking into account the user's geographical location information, region-specific trend data can be collected. Geographical location information includes, but is not limited to, GPS data and IP addresses. The priority for data collection includes, but is not limited to, region-specific trend data and advertising data.

[0056] The proposal team can analyze a user's past purchase history and propose the optimal advertising delivery strategy. For example, it can prioritize the delivery of ads related to products the user has previously purchased. It can also concentrate ads during specific time periods based on the user's purchase history. Furthermore, it can prioritize ad delivery on specific devices or browsers based on the user's purchase history. In this way, by analyzing a user's past purchase history, the optimal advertising delivery strategy can be proposed. The analysis of purchase history includes, but is not limited to, the types of products purchased and the frequency of purchases. The proposed advertising delivery strategy includes, but is not limited to, the content of the ads, the timing of delivery, and the selection of devices.

[0057] The data collection unit can analyze users' social media activity and collect relevant data. For example, it can collect data related to content users share on social media. It can also collect advertising data of interest based on users' social media engagement. Furthermore, it can analyze the activity of users' followers and friends and collect relevant data. This allows for the collection of highly relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Relevant data includes, but is not limited to, social media trend data.

[0058] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. It can also prioritize the analysis of high-importance data and provide results quickly. Furthermore, it can apply multiple analysis methods to high-importance data to improve accuracy. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Data importance includes, but is not limited to, business impact and data reliability. The level of detail of the analysis includes, but is not limited to, detailed analysis and summary analysis.

[0059] The learning unit can improve the accuracy of learning by integrating information from different data sources during the learning process. For example, it can integrate information from different data sources such as advertising data, user behavior data, and social media data. It can also optimize the learning algorithm based on information from different data sources. Furthermore, it can analyze information from different data sources to improve the accuracy of the learning algorithm. As a result, learning accuracy is improved by integrating information from different data sources. Different data sources include, but are not limited to, advertising data, user behavior data, and social media data. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input information from different data sources into a generating AI and have the generating AI perform the information integration.

[0060] The proposal department can prioritize proposals based on the timing of ad delivery. For example, it can make optimal proposals based on the timing of ad delivery. It can also apply different proposal methods to each ad delivery period to make effective proposals. Furthermore, it can prioritize proposals based on the timing of ad delivery. This allows for more efficient proposals by prioritizing proposals based on the timing of ad delivery. The timing of ad delivery includes, but is not limited to, the campaign period and the activity time of the target audience. The priority of proposals includes, but is not limited to, importance and relevance.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects web campaign data. The data collection unit collects detailed data such as click-through rates and conversion rates. The data collection unit collects data such as how many times a particular ad was clicked and how many users converted. In addition, it can also collect user behavior data and response data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and learns customer behavior patterns and responses. It learns what kind of users responded to specific advertisements and what actions they took. Furthermore, it can also cluster the data to understand customer behavior patterns. Step 3: The proposal department proposes optimal advertising delivery strategies and content improvements based on the analysis results obtained by the analysis department. The proposal department suggests what kind of advertisements would be effective for specific customers, and what kind of content improvements would increase click-through rates and conversion rates. Furthermore, AI can be used to propose advertising delivery strategies and content improvements. Step 4: The learning unit learns user responses in real time. The learning unit learns user responses in real time as the campaign progresses and improves the ad delivery strategy and content as needed. Furthermore, it can collect user response data in real time and learn using AI.

[0063] (Example of form 2) The campaign optimization agent according to an embodiment of the present invention is a system in which AI analyzes web campaign data and proposes optimal advertising delivery strategies and content improvements based on customer behavior patterns and responses. This campaign optimization agent collects web campaign data and the AI ​​analyzes it. Next, the AI ​​learns customer behavior patterns and responses and proposes optimal advertising delivery strategies and content improvements. Furthermore, it learns user responses in real time and aims to increase click-through rates and conversion rates. For example, the campaign optimization agent collects web campaign data. At this time, it collects detailed data such as click-through rates and conversion rates. For example, it collects data such as how many times a particular ad was clicked and how many users converted. This makes it possible to understand the effectiveness of the campaign. Next, the AI ​​analyzes the collected data. The AI ​​analyzes the collected data and learns customer behavior patterns and responses. For example, it learns what kind of users responded to a particular ad and what actions they took. This makes it possible to understand customer behavior patterns. Furthermore, the AI ​​proposes optimal advertising delivery strategies and content improvements based on the learned customer behavior patterns and responses. For example, it suggests what kind of ads would be most effective for specific customers, and what kind of content improvements would increase click-through rates and conversion rates. This maximizes the effectiveness of campaigns. Furthermore, the AI ​​learns user responses in real time, aiming to increase click-through rates and conversion rates. For instance, it learns user responses in real time during a campaign and improves ad delivery strategies and content as needed. This allows for continuous improvement of campaign effectiveness. This mechanism maximizes the effectiveness of advertising campaigns and achieves a high ROI. For example, an average increase of 30% in click-through rates and a 25% increase in conversion rates can be expected. A 50% improvement in ROI maximization is also predicted. This enables campaign optimization agents to efficiently collect, analyze, suggest, and learn from web campaign data.

[0064] The campaign optimization agent according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, and a learning unit. The data collection unit collects web campaign data. The data collection unit collects detailed data such as click-through rates and conversion rates. The data collection unit collects data such as how many times a particular ad was clicked and how many users converted. The data collection unit can also collect user behavior data and response data. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the collected data and learns customer behavior patterns and responses. The analysis unit learns, for example, what kind of users responded to a particular ad and what actions they took. The analysis unit can also cluster the data to understand customer behavior patterns. The proposal unit proposes optimal ad delivery strategies and content improvements based on the analysis results obtained by the analysis unit. The proposal unit proposes, for example, what kind of ads would be effective to deliver to specific customers and what kind of content improvements would increase click-through rates and conversion rates. The proposal unit can use AI to, for example, propose advertising delivery strategies and content improvements. The learning unit learns user responses in real time. The learning unit learns user responses in real time during the campaign and improves advertising delivery strategies and content as needed. The learning unit can also collect user response data in real time and learn using AI. As a result, the campaign optimization agent according to this embodiment can efficiently collect, analyze, propose, and learn from web campaign data.

[0065] The data collection unit collects web campaign data. Specifically, the data collection unit collects detailed data such as click-through rates and conversion rates. For example, it collects data such as how many times a particular ad was clicked and how many users converted. This includes metrics such as the number of times an ad was displayed (impressions), the number of clicks, click-through rate (CTR), number of conversions, and conversion rate (CVR). Furthermore, the data collection unit can also collect user behavior data and response data. For example, it collects data such as what pages a user viewed after clicking an ad and what actions they took (e.g., did they purchase a product or fill out a form). This allows the data collection unit to understand detailed user behavior patterns. The data collection unit collects this data in real time and sends it to a central database. The database centrally manages the collected data and makes it accessible to the analysis and proposal units. Furthermore, the data collection unit can adjust the frequency and accuracy of data collection to provide flexible responses tailored to specific campaigns and user segments. For example, it can collect more detailed data for specific time periods or specific user groups. This allows the data collection unit to efficiently and effectively collect data and provide the information necessary to optimize the campaign.

[0066] The analysis unit analyzes the data collected by the data collection unit. Specifically, it analyzes the collected data to learn customer behavior patterns and responses. For example, it learns what kind of users responded to specific advertisements and what actions they took. The analysis unit can understand customer behavior patterns by clustering the data. Clustering is a method of dividing data into groups with similar characteristics, which allows for the identification of different customer segments. For example, it can identify customer groups that show a high response to specific advertisements or customer groups that have a high conversion rate during specific time periods. Furthermore, the analysis unit can analyze the data using AI. The AI ​​uses machine learning algorithms to extract patterns and trends from the data and predict customer behavior. For example, based on past data, it can learn under what conditions a particular advertisement was effective and reflect this in future advertising delivery strategies. This allows the analysis unit to quickly and accurately analyze the collected data and understand customer behavior patterns. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0067] The Proposal Department proposes optimal advertising delivery strategies and content improvements based on the analysis results obtained by the Analysis Department. Specifically, it proposes what kind of ads would be most effective for specific customers, and what kind of content improvements would increase click-through rates and conversion rates. The Proposal Department can use AI to make these proposals. Based on the data provided by the Analysis Department, the AI ​​calculates the optimal advertising delivery strategy and generates specific proposals. For example, it proposes which ads should be delivered at which time of day to a specific customer segment for maximum effectiveness. It also makes specific suggestions for content improvements. For example, it proposes specific actions to improve click-through rates and conversion rates, such as changing ad copy and images, or improving the landing page design. Furthermore, the Proposal Department can evaluate the effectiveness of its proposals and continuously improve them. For example, it collects the results of advertising delivery and content improvements implemented based on the proposals and evaluates their effectiveness. Based on the evaluation results, it can revise its proposals and make more effective suggestions. In this way, the Proposal Department can always provide optimal proposals based on the latest data and analysis results, maximizing the effectiveness of campaigns.

[0068] The learning unit learns user responses in real time. Specifically, it learns user responses in real time during the campaign and improves advertising strategies and content as needed. The learning unit can collect user response data in real time and learn using AI. The AI ​​uses machine learning algorithms to extract patterns and trends from user response data and identify areas for improvement in advertising strategies and content. For example, it learns how users reacted to specific ads and what actions they took, and adjusts the advertising strategy based on the results. This allows the learning unit to provide the optimal advertising strategy in real time during the campaign. Furthermore, the learning unit can collect user feedback and continuously improve the accuracy and effectiveness of its learning. For example, it reviews areas for improvement in advertising strategies and content based on user feedback and provides more effective strategies. In addition, the learning unit can perform highly accurate learning based on a wider range of data by integrating data from multiple campaigns. This allows the learning unit to always provide the optimal advertising strategy based on the latest data and user responses, maximizing the effectiveness of campaigns.

[0069] The data collection unit can collect detailed data such as click-through rates and conversion rates. For example, the data collection unit collects detailed data such as click-through rates and conversion rates. For example, the data collection unit collects data such as how many times a particular ad was clicked and how many users converted. The data collection unit can also collect user behavior data and response data. This allows for understanding the effectiveness of a campaign by collecting detailed data. Detailed data includes, but is not limited to, click-through rates, conversion rates, and time spent on the site. Some or all of the processing described above in the data collection unit may be performed using AI, or not. For example, the data collection unit can input detailed data such as click-through rates and conversion rates into a generating AI and have the generating AI perform the data collection.

[0070] The analytics unit can analyze the collected data and learn customer behavior patterns and responses. For example, the analytics unit can analyze the collected data and learn customer behavior patterns and responses. For example, the analytics unit can learn what kind of users responded to a particular advertisement and what actions they took. For example, the analytics unit can cluster the data to understand customer behavior patterns. By learning customer behavior patterns and responses, it becomes possible to develop effective advertising strategies and improve content. Customer behavior patterns include, but are not limited to, click frequency and page transition order. Responses include, but are not limited to, clicks, purchases, and comments. Some or all of the above processing in the analytics unit may be performed using, for example, AI, or not using AI. For example, the analytics unit can input the collected data into a generating AI and have the generating AI perform the learning of customer behavior patterns and responses.

[0071] The proposal department can propose optimal advertising delivery strategies and content improvements based on learned customer behavior patterns and responses. For example, the proposal department can propose what kind of ads would be effective for specific customers, or what kind of content improvements would increase click-through rates and conversion rates. The proposal department can also use AI to propose advertising delivery strategies and content improvements. This maximizes the effectiveness of campaigns by proposing optimal advertising delivery strategies and content improvements. Optimal advertising delivery strategies include, but are not limited to, methods for selecting target audiences and timing of ad delivery. Content improvements include, but are not limited to, which parts of the content to improve and criteria for evaluating improvements. Some or all of the above processes in the proposal department may be performed using, for example, AI, or not. For example, the proposal department can input learned customer behavior patterns and responses into a generating AI and have the generating AI execute proposals for optimal advertising delivery strategies and content improvements.

[0072] The learning unit can learn user responses in real time and improve advertising strategies and content as needed. For example, the learning unit can learn user responses in real time and improve advertising strategies and content as needed. For example, the learning unit can learn user responses in real time during a campaign and improve advertising strategies and content as needed. The learning unit can also, for example, collect user response data in real time and learn using AI. This allows for continuous improvement of campaign effectiveness by learning user responses in real time and improving advertising strategies and content. "Real time" includes, but is not limited to, data update frequency and reflection time. User responses include, but are not limited to, clicks, purchases, and comments. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can input real-time collected user response data into a generating AI and have the generating AI implement improvements to advertising strategies and content.

[0073] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, 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 excited, the data collection unit can collect data in real time and reflect it immediately. For example, if the user is relaxed, the data collection unit can collect data at regular time intervals to obtain stable data. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. This allows for more appropriate data collection by adjusting the timing of data collection based on the user's emotions. User emotions include, but are not limited to, facial recognition and text analysis. Timing of data collection includes, but are not limited to, timing based on the user's behavior patterns. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI and have the generating AI adjust the timing of data collection.

[0074] The data collection unit can analyze a user's past click history and select the optimal data collection method. For example, the data collection unit can analyze a user's past click history and select the optimal data collection method. For example, the data collection unit can analyze patterns of ads that a user has clicked in the past and enhance data collection for similar ads. For example, the data collection unit can concentrate data collection during specific time periods based on the user's click history. For example, the data collection unit can prioritize data collection on specific devices or browsers based on the user's click history. This allows the optimal data collection method to be selected by analyzing the user's past click history. The optimal data collection method includes, but is not limited to, methods for analyzing click history and data collection techniques. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's past click history data into a generating AI and have the generating AI select the optimal data collection method.

[0075] The data collection unit can filter data based on the user's current interests during data collection. For example, the data collection unit filters data based on the user's current interests during data collection. For example, the data collection unit prioritizes collecting data related to topics the user is currently interested in. For example, the data collection unit immediately adjusts the types of data collected if the user's interests change. For example, the data collection unit filters and collects highly relevant advertising data based on the user's interests. This allows for the collection of highly relevant data by filtering data based on the user's current interests. The user's current interests include, but are not limited to, browsing history and search keywords. Filtering includes, but are not limited to, methods for selecting highly relevant data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's current interests data into a generating AI and have the generating AI perform data filtering.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, the data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated user emotions. For example, if the user is excited, the data collection unit prioritizes collecting real-time reaction data. For example, if the user is relaxed, the data collection unit prioritizes collecting long-term behavioral data. For example, if the user is stressed, the data collection unit prioritizes collecting less burdensome data. This enables more effective data collection by determining the priority of data to collect based on the user's emotions. User emotions include, but are not limited to, facial recognition and text analysis. Data prioritization includes, but is not limited to, importance and relevance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, which can then determine the priority of the data to be collected.

[0077] 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 prioritizes the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit prioritizes the collection of advertising data related to that region. For example, the data collection unit collects region-specific trend data based on the user's location information. For example, if the user is on the move, the data collection unit collects data related to the destination region. In this way, by collecting data while considering the user's geographical location information, region-specific trend data can be collected. The user's geographical location information includes, but is not limited to, GPS data and IP addresses. Highly relevant data includes, but is not limited to, region-specific trend data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0078] 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 analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data related to content shared by a user on social media. For example, the data collection unit can collect advertising data of interest based on a user's social media reactions. For example, the data collection unit can analyze the activities of a user's followers and friends and collect relevant data. This allows for the collection of highly relevant data by analyzing a user's social media activity. A user's social media activity includes, but is not limited to, posts and the number of likes. Relevant data includes, but is not limited to, social media trend data. Some or all of the above processing in the data collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user social media activity data into a generating AI and have the generating AI collect relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can display the analysis results using visually stimulating graphs or charts. If the user is relaxed, the analysis unit can display the analysis results with a simple and calming design. If the user is stressed, the analysis unit can highlight only the important points. By adjusting the presentation of the analysis based on the user's emotions, more effective analysis results can be provided. User emotions include, but are not limited to, facial recognition and text analysis. Presentation of the analysis includes, but are not limited to, graph displays and text displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes 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 a generating AI and have the generating AI adjust the method of expressing the analysis.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. For example, the analysis unit can prioritize the analysis of high-importance data and provide results quickly. For example, the analysis unit can apply multiple analysis methods to high-importance data to improve accuracy. This makes efficient analysis possible by adjusting the level of detail of the analysis based on the importance of the data. The importance of the data includes, but is not limited to, business impact and data reliability. The level of detail of the analysis includes, but is not limited to, detailed analysis and summary analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0081] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm that emphasizes click-through rates and conversion rates to advertising data. For example, the analysis unit can apply an algorithm that analyzes behavioral patterns to user behavior data. For example, the analysis unit can apply an algorithm that analyzes engagement rates to social media data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Data categories include, but are not limited to, text data and numerical data. Analysis algorithms include, but are not limited to, clustering algorithms and regression analysis. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input data categories into a generating AI and have the generating AI execute the application of analysis algorithms.

[0082] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, the analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more effective analysis results can be provided. User emotions include, but are not limited to, facial recognition and text analysis. The length of the analysis includes, but is not limited to, detailed analysis and summary analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into the generating AI and have the generating AI adjust the length of the analysis.

[0083] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the latest data and provide real-time results. For example, the analysis unit can analyze trends based on historical data to grasp long-term trends. For example, the analysis unit can focus on analyzing data collected during a specific period to evaluate the campaign effect during that period. This enables efficient analysis by determining the priority of analysis based on the data collection timing. The data collection timing includes, but is not limited to, the latest data and historical data. The analysis priority includes, but is not limited to, importance and relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI perform the determination of the analysis priority.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data and provide results quickly. For example, the analysis unit can prioritize the analysis of important data, postponing the analysis of less relevant data. For example, the analysis unit can group highly relevant data and analyze them all at once. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. The relevance of the data includes, but is not limited to, correlation and causation. The order of analysis includes, but is not limited to, importance and relevance. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0085] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is excited, the suggestion unit will make visually stimulating suggestions. If the user is relaxed, the suggestion unit will make simple and calming suggestions. If the user is stressed, the suggestion unit will highlight only the important points in its suggestions. By adjusting the way it presents suggestions based on the user's emotions, more effective suggestions become possible. User emotions include, but are not limited to, facial recognition and text analysis. Suggestion presentation methods include, but are not limited to, graph displays and text displays. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal department can input user emotion data into a generation AI and have the generation AI adjust the way the proposal is expressed.

[0086] The proposal unit can adjust the level of detail of a proposal based on the importance of the advertisement. For example, the proposal unit can adjust the level of detail of a proposal based on the importance of the advertisement. For example, the proposal unit can provide detailed proposals for high-importance advertisements and simplified proposals for low-importance advertisements. For example, the proposal unit can prioritize proposals for high-importance advertisements and provide results quickly. For example, the proposal unit can provide multiple proposals for high-importance advertisements, offering choices. This allows for efficient proposals by adjusting the level of detail of proposals based on the importance of the advertisements. The importance of an advertisement includes, but is not limited to, business impact and advertising effectiveness. The level of detail of a proposal includes, but is not limited to, detailed proposals and summary proposals. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the importance of the advertisements into a generating AI and have the generating AI adjust the level of detail of the proposals.

[0087] The proposal unit can apply different proposal algorithms depending on the ad category when making a proposal. For example, the proposal unit applies different proposal algorithms depending on the ad category when making a proposal. For example, the proposal unit selects the optimal proposal algorithm depending on the ad category. For example, the proposal unit applies different proposal methods for each ad category to make effective proposals. For example, the proposal unit determines the priority of proposals based on the ad category. This makes it possible to make more effective proposals by applying different proposal algorithms depending on the ad category. Ad categories include, but are not limited to, text ads and banner ads. Proposal algorithms include, but are not limited to, recommendation algorithms and optimization algorithms. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input the ad category into a generating AI and have the generating AI perform the application of the proposal algorithm.

[0088] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, the suggestion unit estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit will make a short, to-the-point suggestion. For example, if the user is relaxed, the suggestion unit will make a detailed suggestion. For example, if the user is excited, the suggestion unit will make a visually stimulating suggestion. By adjusting the length of the suggestion based on the user's emotions, more effective suggestions become possible. User emotions include, but are not limited to, facial recognition and text analysis. Suggestion length includes, but is not limited to, detailed suggestions and summary suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input user emotion data into a generating AI and have the AI ​​adjust the length of the suggestion.

[0089] The proposal unit can determine the priority of proposals based on the timing of ad delivery when making a proposal. For example, the proposal unit determines the priority of proposals based on the timing of ad delivery when making a proposal. The proposal unit makes the best proposal based on the timing of ad delivery. For example, the proposal unit makes effective proposals by applying different proposal methods for each timing of ad delivery. The proposal unit determines the priority of proposals based on the timing of ad delivery. This enables efficient proposals by determining the priority of proposals based on the timing of ad delivery. The timing of ad delivery includes, but is not limited to, the campaign period and the activity time of the target audience. The priority of proposals includes, but is not limited to, importance and relevance. Some or all of the above processing in the proposal unit may be performed using, for example, AI, or not using AI. For example, the proposal unit can input ad delivery timing data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0090] The suggestion unit can adjust the order of suggestions based on the relevance of the advertisements when making suggestions. For example, the suggestion unit can adjust the order of suggestions based on the relevance of the advertisements when making suggestions. For example, the suggestion unit can prioritize suggesting highly relevant advertisements and provide results quickly. For example, the suggestion unit can prioritize suggesting important advertisements and postpone suggesting less relevant advertisements. For example, the suggestion unit can group highly relevant advertisements and suggest them all at once. This allows for efficient suggestions by adjusting the order of suggestions based on the relevance of the advertisements. Ad relevance includes, but is not limited to, the interests of the target audience and past responses. The order of suggestions includes, but is not limited to, importance and relevance. Some or all of the above processing in the suggestion unit may be performed using, for example, AI, or not using AI. For example, the suggestion unit can input ad relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0091] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is excited, the learning unit prioritizes learning real-time reaction data. If the user is relaxed, the learning unit prioritizes learning long-term behavioral data. If the user is stressed, the learning unit prioritizes learning less burdensome data. This allows for more effective learning by selecting training data based on the user's emotions. User emotions include, but are not limited to, facial recognition and text analysis. Selection of training data includes, but are not limited to, data importance and relevance. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into the generating AI and have the generating AI select the training data.

[0092] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit optimizes the learning algorithm by referring to past learning data during learning. For example, the learning unit selects the optimal learning algorithm based on past learning data. For example, the learning unit adjusts the parameters of the learning algorithm by referring to past learning data. For example, the learning unit analyzes past learning data to improve the accuracy of the learning algorithm. As a result, the accuracy of learning is improved by optimizing the learning algorithm by referring to past learning data. Past learning data includes, but is not limited to, data storage methods and reference methods. Optimization of the learning algorithm includes, but is not limited to, parameter adjustment and algorithm selection. Some or all of the above processes in the learning unit may be performed using, for example, AI, or without using AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0093] The learning unit can immediately reflect data collected in real time during learning. For example, the learning unit immediately reflects data collected in real time during learning. For example, the learning unit immediately reflects data collected in real time into the learning algorithm. For example, the learning unit updates the learning algorithm based on data collected in real time. For example, the learning unit analyzes data collected in real time to improve the accuracy of the learning algorithm. As a result, the accuracy of learning is improved by immediately reflecting data collected in real time. Real time includes, but is not limited to, the frequency of data updates and the time until reflection. Immediate reflection includes, but is not limited to, the speed of data processing and the timing of reflection. Some or all of the above processes in the learning unit may be performed using, for example, AI, or without using AI. For example, the learning unit can input data collected in real time into a generating AI and have the generating AI perform the process of immediately reflecting the data.

[0094] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is excited, the learning unit increases the learning frequency to prioritize real-time responses. For example, if the user is relaxed, the learning unit decreases the learning frequency to prioritize long-term data. For example, if the user is stressed, the learning unit adjusts the learning frequency to reduce the burden. This allows for more effective learning by adjusting the learning frequency based on the user's emotions. User emotions include, but are not limited to, facial recognition and text analysis. Learning frequency includes, but is not limited to, data update frequency and learning timing. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generating AI and have the generating AI adjust the learning frequency.

[0095] The learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can weight the training data based on when the data was collected during training. For example, the learning unit can give a high weight to the latest data to emphasize real-time responses. For example, the learning unit can give a low weight to historical data to grasp long-term trends. For example, the learning unit can give appropriate weights to data collected during a specific period to evaluate the campaign effect during that period. This improves the accuracy of learning by weighting the training data based on when the data was collected. The data collection period includes, but is not limited to, the latest data and historical data. The weighting of the training data includes, but is not limited to, data importance and relevance. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input the data collection period into a generating AI and have the generating AI perform the weighting of the training data.

[0096] The learning unit can improve the accuracy of learning by integrating information from different data sources during learning. For example, the learning unit improves the accuracy of learning by integrating information from different data sources during learning. The learning unit learns by integrating information from different data sources, such as advertising data, user behavior data, and social media data. For example, the learning unit optimizes the learning algorithm based on information from different data sources. The learning unit improves the accuracy of the learning algorithm by analyzing information from different data sources. As a result, the accuracy of learning is improved by learning by integrating information from different data sources. Different data sources include, but are not limited to, advertising data, user behavior data, and social media data. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not using AI. For example, the learning unit can input information from different data sources into a generating AI and have the generating AI perform the information integration.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] Campaign optimization agents can further estimate user emotions and adjust ad delivery strategies based on those estimates. For example, if a user is excited, more active ads can be delivered; if a user is relaxed, calmer ads can be delivered. If a user is stressed, the frequency of ads can be reduced to lessen the user's burden. This allows for more effective ad delivery by adjusting ad delivery strategies based on user emotions. Emotion estimation may include, but is not limited to, facial recognition and text analysis. Adjustments to ad delivery strategies may include, but are not limited to, ad content, timing, and frequency.

[0099] The data collection unit can collect data while taking into account the user's geographical location information. For example, if a user is in a specific region, it can prioritize the collection of advertising data related to that region. It can also collect region-specific trend data based on the user's location information. Furthermore, if a user is on the move, it can collect data related to the region they are moving to. In this way, by collecting data while taking into account the user's geographical location information, region-specific trend data can be collected. Geographical location information includes, but is not limited to, GPS data and IP addresses. The priority for data collection includes, but is not limited to, region-specific trend data and advertising data.

[0100] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on those estimated emotions. For example, if the user is excited, the analysis results can be displayed using visually stimulating graphs or charts. If the user is relaxed, the analysis results can be displayed with a simple and calming design. Furthermore, if the user is stressed, only the important points can be highlighted. By adjusting how the analysis results are displayed based on the user's emotions, more effective analysis results can be provided. Emotion estimation may involve, but is not limited to, facial recognition or text analysis. Display methods for analysis results may include, but are not limited to, graphs or text.

[0101] The proposal team can analyze a user's past purchase history and propose the optimal advertising delivery strategy. For example, it can prioritize the delivery of ads related to products the user has previously purchased. It can also concentrate ads during specific time periods based on the user's purchase history. Furthermore, it can prioritize ad delivery on specific devices or browsers based on the user's purchase history. In this way, by analyzing a user's past purchase history, the optimal advertising delivery strategy can be proposed. The analysis of purchase history includes, but is not limited to, the types of products purchased and the frequency of purchases. The proposed advertising delivery strategy includes, but is not limited to, the content of the ads, the timing of delivery, and the selection of devices.

[0102] The learning unit can estimate the user's emotions and select training data based on those estimated emotions. For example, if the user is excited, it can prioritize learning real-time reaction data. If the user is relaxed, it can prioritize learning long-term behavioral data. Furthermore, if the user is stressed, it can prioritize learning less burdensome data. This allows for more effective learning by selecting training data based on the user's emotions. Emotion estimation may involve, but is not limited to, facial recognition or text analysis. Selection of training data may include, but is not limited to, data importance or relevance.

[0103] The data collection unit can analyze users' social media activity and collect relevant data. For example, it can collect data related to content users share on social media. It can also collect advertising data of interest based on users' social media engagement. Furthermore, it can analyze the activity of users' followers and friends and collect relevant data. This allows for the collection of highly relevant data by analyzing users' social media activity. Social media activity includes, but is not limited to, posts and the number of likes. Relevant data includes, but is not limited to, social media trend data.

[0104] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. It can also prioritize the analysis of high-importance data and provide results quickly. Furthermore, it can apply multiple analysis methods to high-importance data to improve accuracy. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Data importance includes, but is not limited to, business impact and data reliability. The level of detail of the analysis includes, but is not limited to, detailed analysis and summary analysis.

[0105] The suggestion function can estimate the user's emotions and adjust the presentation of suggestions based on those emotions. For example, if the user is excited, it can present visually stimulating suggestions. If the user is relaxed, it can present simple and calming suggestions. Furthermore, if the user is stressed, it can highlight only the important points in its suggestions. By adjusting the presentation of suggestions based on the user's emotions, more effective suggestions become possible. Emotion estimation may involve, but is not limited to, facial recognition or text analysis. Presentation methods for suggestions may include, but are not limited to, graphs or text displays.

[0106] The learning unit can improve the accuracy of learning by integrating information from different data sources during the learning process. For example, it can integrate information from different data sources such as advertising data, user behavior data, and social media data. It can also optimize the learning algorithm based on information from different data sources. Furthermore, it can analyze information from different data sources to improve the accuracy of the learning algorithm. As a result, learning accuracy is improved by integrating information from different data sources. Different data sources include, but are not limited to, advertising data, user behavior data, and social media data. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input information from different data sources into a generating AI and have the generating AI perform the information integration.

[0107] The proposal department can prioritize proposals based on the timing of ad delivery. For example, it can make optimal proposals based on the timing of ad delivery. It can also apply different proposal methods to each ad delivery period to make effective proposals. Furthermore, it can prioritize proposals based on the timing of ad delivery. This allows for more efficient proposals by prioritizing proposals based on the timing of ad delivery. The timing of ad delivery includes, but is not limited to, the campaign period and the activity time of the target audience. The priority of proposals includes, but is not limited to, importance and relevance.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects web campaign data. The data collection unit collects detailed data such as click-through rates and conversion rates. The data collection unit collects data such as how many times a particular ad was clicked and how many users converted. In addition, it can also collect user behavior data and response data. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the collected data and learns customer behavior patterns and responses. It learns what kind of users responded to specific advertisements and what actions they took. Furthermore, it can also cluster the data to understand customer behavior patterns. Step 3: The proposal department proposes optimal advertising delivery strategies and content improvements based on the analysis results obtained by the analysis department. The proposal department suggests what kind of advertisements would be effective for specific customers, and what kind of content improvements would increase click-through rates and conversion rates. Furthermore, AI can be used to propose advertising delivery strategies and content improvements. Step 4: The learning unit learns user responses in real time. The learning unit learns user responses in real time as the campaign progresses and improves the ad delivery strategy and content as needed. Furthermore, it can collect user response data in real time and learn using AI.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects web campaign data using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and learn customer behavior patterns and responses. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose optimal advertising delivery strategies and content improvements. The learning unit is implemented in the control unit 46A of the smart device 14, for example, to learn user responses in real time and improve advertising delivery strategies and content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects web campaign data using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and learn customer behavior patterns and responses. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose optimal advertising delivery strategies and content improvements. The learning unit is implemented in the control unit 46A of the smart glasses 214, for example, to learn user responses in real time and improve advertising delivery strategies and content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects web campaign data using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and learn customer behavior patterns and responses. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose optimal advertising delivery strategies and content improvements. The learning unit is implemented in the control unit 46A of the headset terminal 314, for example, to learn user responses in real time and improve advertising delivery strategies and content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects web campaign data using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze the collected data and learn customer behavior patterns and responses. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to propose optimal advertising delivery strategies and content improvements. The learning unit is implemented in the control unit 46A of the robot 414, for example, to learn user responses in real time and improve advertising delivery strategies and content. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) The data collection department collects web campaign data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal advertising delivery strategy and content improvement. It includes a learning unit that learns user responses in real time. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect detailed data such as click-through rates and conversion rates. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed to learn customer behavior patterns and responses. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on learned customer behavior patterns and responses, we propose optimal advertising delivery strategies and content improvements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned learning unit, Learn user responses in real time and improve ad delivery strategies and content as needed. The system described in Appendix 1, characterized by the features described herein. (Note 6) 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 7) The aforementioned collection unit is Analyze the user's past click history to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) 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 11) 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 12) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making suggestions, different suggestion algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When submitting proposals, prioritize them based on the timing of ad delivery. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, During training, data collected in real time is immediately reflected. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, During training, the training data is weighted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, information from different data sources is integrated to improve the accuracy of the learning process. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 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 web campaign data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal advertising delivery strategy and content improvement. It includes a learning unit that learns user responses in real time. A system characterized by the following features.

2. The aforementioned collection unit is Collect detailed data such as click-through rates and conversion rates. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed to learn customer behavior patterns and responses. The system according to feature 1.

4. The aforementioned proposal section is, Based on learned customer behavior patterns and responses, we propose optimal advertising delivery strategies and content improvements. The system according to feature 1.

5. The aforementioned learning unit, Learn user responses in real time and improve ad delivery strategies and content as needed. The system according to feature 1.

6. 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.

7. The aforementioned collection unit is Analyze the user's past click history to select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and preferences. The system according to feature 1.

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

10. 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 according to feature 1.

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