system

Abstract

The system according to this embodiment aims to provide precise estimates for web advertising. [Solution] The system according to the embodiment comprises a data collection unit, a prediction unit, an analysis unit, and a suggestion unit. The data collection unit collects data. The prediction unit makes predictions based on the data collected by the data collection unit. The analysis unit analyzes the prediction results obtained by the prediction unit in real time. The suggestion unit suggests search keywords based on the analysis results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

[0007] The system according to this embodiment can accurately estimate the cost of web advertising. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 Web advertising simulation system according to an embodiment of the present invention is a system that uses AI to analyze and predict real data, providing simulations that are more precise than conventional estimates. This Web advertising simulation system provides predictions that take into account the buyer's behavior history and market trends, real-time analysis of results, and advice on search keywords. For example, the Web advertising simulation system first performs predictions using real data analysis and AI. In this process, it collects past advertising data and market data, and the AI ​​analyzes this data. For example, it collects data such as click-through rates and conversion rates of past advertising campaigns, and the AI ​​predicts future advertising effectiveness based on this data. Next, the Web advertising simulation system makes predictions that take into account the buyer's behavior history and market trends. Specifically, it collects data such as what actions buyers have taken and what products they are interested in, and the AI ​​analyzes this data. For example, it predicts future purchasing behavior based on data such as what products buyers have purchased in the past and what advertisements they have responded to. Furthermore, the Web advertising simulation system performs real-time analysis of results. After an advertisement is published, the AI ​​analyzes the effectiveness of the advertisement in real time and proposes improvement plans as needed. For example, the system monitors ad click-through rates and conversion rates in real time, and if performance is low, the AI ​​proposes improvement plans. Finally, the web advertising simulation system provides advice on search keywords. The AI ​​suggests optimal search keywords based on market trends and customer behavior history. For example, it suggests search keywords related to a product to customers who have a high interest in that product. By adding AI predictions to conventional prediction methods, more precise simulations become possible, and real-time analysis and detailed improvements can be easily made. In addition, because search keywords can be set taking into account customer and market trends, it is expected that advertising effectiveness will be maximized. In this way, the web advertising simulation system can maximize advertising effectiveness.

[0029] The Web advertising simulation system according to this embodiment comprises a data collection unit, a prediction unit, an analysis unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects past advertising data and market data. The data collection unit can collect data such as click-through rates and conversion rates of past advertising campaigns. For example, the data collection unit collects data such as the number of ad impressions, clicks, and conversions. The data collection unit can also collect market data. Market data includes, for example, sales data and customer data. For example, the data collection unit collects data such as sales trends and competitor analysis. The prediction unit makes predictions based on the data collected by the data collection unit. For example, the prediction unit predicts future advertising effectiveness based on the collected data. For example, the prediction unit makes predictions using statistical models or machine learning algorithms. For example, the prediction unit predicts future click-through rates and conversion rates based on past advertising data. For example, the prediction unit predicts future advertising effectiveness based on data from past advertising campaigns. The analysis unit analyzes the prediction results obtained by the prediction unit in real time. The analysis department monitors, for example, the click-through rate and conversion rate of advertisements in real time. The analysis department proposes improvement plans if, for example, the effectiveness of the advertisements is low. The analysis department monitors, for example, the click-through rate and conversion rate of advertisements in real time, and if the effectiveness is low, proposes improvement plans such as changing the ad copy or reviewing the targeting. The proposal department proposes search keywords based on the analysis results obtained by the analysis department. The proposal department proposes optimal search keywords based, for example, the buyer's behavior history and market trends. The proposal department proposes optimal search keywords based, for example, the keywords the buyer has searched for in the past and their purchase history. The proposal department proposes highly relevant search keywords based, for example, the buyer's behavior history and market trends. As a result, the web advertising simulation system according to the embodiment is capable of data collection, prediction, real-time analysis, and search keyword proposal.

[0030] The data collection unit collects data. For example, the data collection unit collects historical advertising data and market data. Specifically, it can collect data such as click-through rates and conversion rates for past advertising campaigns. For example, the data collection unit collects data such as ad impressions, clicks, and conversions. The data collection unit can also collect market data. Market data includes, for example, sales data and customer data. For example, the data collection unit collects data such as sales trends and competitor analysis. The data collection unit centrally manages this data and stores it in a database. The database is built on the cloud and is updated in real time. The data collection unit can connect with external data sources via APIs and automatically obtain the latest data. For example, it can use APIs from advertising platforms and market research companies to obtain the latest advertising and market data. In addition, the data collection unit cleans and normalizes the data to ensure data quality. Data cleaning involves imputing missing values ​​and removing outliers, and normalization unifies the scale of the data. This ensures that the collected data is stored in a format suitable for analysis and forecasting. Furthermore, the data collection unit can adjust the frequency of data collection. For example, the collection frequency can be set according to the characteristics of the data, such as collecting advertising data daily and market data weekly. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0031] The prediction unit makes predictions based on data collected by the data collection unit. For example, the prediction unit predicts future advertising effectiveness based on the collected data. Specifically, it uses statistical models and machine learning algorithms to make predictions. For example, the prediction unit predicts future click-through rates and conversion rates based on past advertising data. For example, the prediction unit predicts future advertising effectiveness based on data from past advertising campaigns. The prediction unit uses this data to predict metrics such as ad impressions, clicks, and conversions. Machine learning algorithms used include regression analysis, decision trees, random forests, and neural networks. These algorithms can learn from past data and predict future advertising effectiveness with high accuracy. For example, regression analysis can be used to model the relationship between ad impressions and click-through rates and predict future click-through rates. Neural networks can also be used to model the impact of multiple factors on advertising effectiveness, enabling more complex predictions. The prediction unit updates prediction results in real time, providing predictions based on the latest data. For example, when a new advertising campaign is launched, the data is immediately incorporated and the prediction model is updated. This allows the prediction unit to always provide highly accurate predictions based on the latest information, helping to maximize advertising effectiveness.

[0032] The analytics department analyzes the prediction results obtained by the forecasting department in real time. For example, the analytics department monitors ad click-through rates and conversion rates in real time. Specifically, if the effectiveness of an ad is low, it proposes improvement plans. The analytics department monitors ad click-through rates and conversion rates in real time, and if the effectiveness is low, it proposes improvement plans such as changing the ad copy or reviewing the targeting. Based on this data, the analytics department evaluates the performance of the ad and proposes the optimal improvement measures. For example, if the click-through rate is low, it proposes changing the ad copy or image, and if the conversion rate is low, it proposes reviewing the targeting or improving the landing page. The analytics department can automatically generate these proposals and notify advertisers. For example, it can use AI to evaluate the performance of an ad and automatically generate the optimal improvement measures. This allows advertisers to improve their ads quickly and effectively. Furthermore, the analytics department can also develop long-term advertising strategies by utilizing historical data and statistical information. For example, based on data from past advertising campaigns, it can predict the effectiveness of advertising in specific seasons or events and develop future advertising strategies. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analysis department to not only grasp the situation in real time, but also to formulate long-term advertising strategies and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0033] The Proposal Department suggests search keywords based on the analysis results obtained by the Analysis Department. For example, the Proposal Department suggests optimal search keywords based on the buyer's behavior history and market trends. Specifically, it suggests optimal search keywords based on keywords the buyer has searched for in the past and their purchase history. For example, the Proposal Department suggests highly relevant search keywords based on the buyer's behavior history and market trends. The Proposal Department uses this data to suggest optimal search keywords to advertisers. For example, it suggests keywords related to specific products or services based on past purchase history to optimize ad targeting. It also suggests keywords that match current trends and demand based on market trends to maximize the effectiveness of advertising. The Proposal Department uses AI to analyze this data and automatically generate optimal search keywords. For example, it uses natural language processing technology to analyze the buyer's search history and extract highly relevant keywords. It also uses machine learning algorithms to predict market trends and suggest keywords that will be in high demand in the future. The Proposal Department notifies advertisers of these suggestions and supports them in maximizing the effectiveness of their advertising campaigns. Furthermore, the Proposal Department can monitor the effectiveness of the suggested keywords and modify the suggestions as needed. For example, the system monitors the click-through rate and conversion rate of suggested keywords, and if they are ineffective, it suggests new keywords. This allows the suggestion team to always provide the most suitable search keywords based on the latest information, maximizing the effectiveness of advertising campaigns.

[0034] The data collection unit can collect historical advertising data and market data. For example, the data collection unit can collect historical advertising data. For example, the data collection unit can collect data such as click-through rates and impressions from past advertising campaigns. For example, the data collection unit can collect data such as conversion rates and sales data from past advertising campaigns. The data collection unit can also collect market data. For example, the data collection unit can collect market data such as sales data and customer data. For example, the data collection unit can collect market data such as sales trends and competitor analysis. By collecting historical advertising data and market data, the accuracy of predictions is improved. 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 historical advertising data and market data into AI and have the AI ​​perform the data collection.

[0035] The prediction unit can predict future advertising effectiveness based on collected data. For example, the prediction unit predicts future advertising effectiveness based on collected data. The prediction unit makes predictions using, for example, statistical models or machine learning algorithms. For example, the prediction unit predicts future click-through rates and conversion rates based on past advertising data. For example, the prediction unit predicts future advertising effectiveness based on data from past advertising campaigns. This improves the accuracy of advertising strategies by predicting future advertising effectiveness based on collected data. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input collected data into AI and have the AI ​​perform predictions of future advertising effectiveness.

[0036] The analytics department can monitor ad click-through rates and conversion rates in real time and propose improvement plans if the performance is low. For example, the analytics department monitors ad click-through rates and conversion rates in real time. For example, the analytics department proposes improvement plans if the performance of the ads is low. For example, the analytics department monitors ad click-through rates and conversion rates in real time and proposes improvement plans such as changing the ad copy or reviewing the targeting if the performance is low. This maximizes the effectiveness of advertising by monitoring ad click-through rates and conversion rates in real time and proposing improvement plans if the performance is low. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input ad click-through rate and conversion rate data into AI and have the AI ​​perform real-time monitoring and propose improvement plans.

[0037] The suggestion department can propose optimal search keywords based on the buyer's behavior history and market trends. For example, the suggestion department can propose optimal search keywords based on the buyer's behavior history and market trends. For example, the suggestion department can propose optimal search keywords based on keywords the buyer has searched for in the past and their purchase history. For example, the suggestion department can propose highly relevant search keywords based on the buyer's behavior history and market trends. This maximizes advertising effectiveness by proposing optimal search keywords based on the buyer's behavior history and market trends. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input data on the buyer's behavior history and market trends into AI and have the AI ​​propose optimal search keywords.

[0038] The data collection unit can select and collect only data that meets specific conditions from past advertising data. For example, the data collection unit can collect only data with a click-through rate above a certain level from past advertising data. For example, the data collection unit can collect only data with a conversion rate above a certain level from past advertising data. For example, the data collection unit can collect only data within a specific period from past advertising data. By selecting and collecting only data that meets specific conditions, the accuracy of predictions is improved. 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 past advertising data into AI and have the AI ​​perform the selection and collection of data that meets specific conditions.

[0039] The data collection unit can filter market data by considering specific events or seasonal factors. For example, the data collection unit may prioritize collecting market data during a specific event (e.g., Black Friday). For example, the data collection unit may collect relevant market data by considering seasonal factors (e.g., the Christmas season). For example, the data collection unit may filter and collect market data during a specific promotion period. This allows for the collection of more relevant data by filtering the data by considering specific events or seasonal factors. 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 market data into AI and have the AI ​​perform data filtering that considers specific events or seasonal factors.

[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 can prioritize the collection of region-specific market data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can prioritize the collection of relevant data by considering the user's travel history. This enables more accurate data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the priority collection of highly relevant data.

[0041] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect data considering the number of users' social media followers and engagement rates. For example, the data collection unit can analyze users' social media trends and collect relevant data. This allows for more accurate data collection by analyzing users' social media activity and collecting relevant data. 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 data on users' social media activity into AI and have the AI ​​perform the collection of relevant data.

[0042] The prediction unit can analyze the success factors of past advertising campaigns during prediction and optimize the prediction algorithm based on that analysis. For example, the prediction unit can analyze the click-through rate and conversion rate of past advertising campaigns and optimize the prediction algorithm. For example, the prediction unit can extract the factors of successful advertising campaigns and adjust the prediction algorithm based on them. For example, the prediction unit can analyze the failure factors of past advertising campaigns and build a prediction algorithm to avoid them. This improves the accuracy of predictions by analyzing the success factors of past advertising campaigns and optimizing the prediction algorithm based on them. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from past advertising campaigns into AI and have the AI ​​perform the analysis of success factors and the optimization of the prediction algorithm.

[0043] The forecasting unit can apply different forecasting models to specific market segments during forecasting. For example, the forecasting unit can apply a specific forecasting model to an advertising campaign targeting young people. For example, the forecasting unit can apply a different forecasting model to an advertising campaign targeting older adults. For example, the forecasting unit can apply a customized forecasting model to a specific region or country. This allows for more accurate forecasts by applying different forecasting models to specific market segments. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input data for a specific market segment into an AI and have the AI ​​apply different forecasting models.

[0044] The prediction unit can determine the priority of predictions based on the timing of ad placements. For example, the prediction unit prioritizes predictions when the ad placement date is approaching. For example, the prediction unit performs detailed predictions when the ad placement date is far off. The prediction unit adjusts the timing of predictions based on the ad placement date. This enables efficient predictions by determining the priority of predictions based on the ad placement date. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on the ad placement date into AI and have AI determine the priority of predictions.

[0045] The prediction unit can adjust the order of predictions based on the relevance of the advertisements during the prediction process. For example, the prediction unit prioritizes predictions for advertisements with high relevance. For example, the prediction unit postpones predictions for advertisements with low relevance. The prediction unit adjusts the order of predictions based on the relevance of the advertisements. This allows for efficient predictions by adjusting the order of predictions based on the relevance of the advertisements. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input advertisement relevance data into the AI ​​and have the AI ​​perform the adjustment of the prediction order.

[0046] The analysis department can perform a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements during the analysis process. For example, the analysis department can perform a detailed analysis of the factors influencing the click-through rate of advertisements and identify areas for improvement. For example, the analysis department can perform a detailed analysis of the factors influencing the conversion rate of advertisements and identify areas for improvement. For example, the analysis department can analyze the correlation between the click-through rate and conversion rate of advertisements and propose effective improvement measures. In this way, by performing a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements, effective improvement measures can be proposed. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input data on the click-through rate and conversion rate of advertisements into AI and have the AI ​​perform a detailed analysis of the factors influencing these rates.

[0047] The analysis department can evaluate the effectiveness of a particular advertising campaign by comparing it to other campaigns during analysis. For example, the analysis department can evaluate the click-through rate of a particular advertising campaign by comparing it to other campaigns. For example, the analysis department can evaluate the conversion rate of a particular advertising campaign by comparing it to other campaigns. For example, the analysis department can evaluate the ROI of a particular advertising campaign by comparing it to other campaigns. This allows for the development of effective advertising strategies by evaluating the effectiveness of a particular advertising campaign by comparing it to other campaigns. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data from a particular advertising campaign into AI and have the AI ​​perform a comparative evaluation with other campaigns.

[0048] The analysis department can perform analyses while considering the geographical distribution of advertisements. For example, the analysis department can analyze the geographical distribution of click-through rates and conversion rates of advertisements. For example, the analysis department can compare the effectiveness of advertisements by region and propose the optimal advertising strategy. For example, the analysis department can optimize the targeting of advertisements based on geographical distribution. In this way, by performing analyses while considering the geographical distribution of advertisements, the effectiveness of advertisements by region can be optimized. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data on the geographical distribution of advertisements into AI and have the AI ​​perform an analysis that takes geographical distribution into account.

[0049] The analysis department can improve the accuracy of its analysis by referring to relevant advertising literature during the analysis process. For example, the analysis department can refer to relevant advertising literature and incorporate the latest knowledge into its analysis. For example, the analysis department can introduce effective analytical methods based on relevant advertising literature. For example, the analysis department can improve the reliability of its analysis results by referring to relevant advertising literature. In this way, by improving the accuracy of the analysis by referring to relevant advertising literature, it is possible to provide more reliable analytical results. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data from relevant advertising literature into AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0050] The suggestion unit can analyze the buyer's past behavior history in detail and propose the most suitable search keywords when making a suggestion. For example, the suggestion unit can propose the most suitable search keywords based on keywords the buyer has searched for in the past. For example, the suggestion unit can analyze the buyer's past purchase history and propose relevant search keywords. For example, the suggestion unit can propose effective search keywords based on the buyer's past ad click history. This maximizes advertising effectiveness by analyzing the buyer's past behavior history in detail and proposing the most suitable search keywords. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the buyer's past behavior history into AI and have the AI ​​propose the most suitable search keywords.

[0051] The proposal department can determine the priority of search keywords when making proposals, taking into account specific market trends. For example, the proposal department can analyze current market trends and prioritize suggesting relevant search keywords. For example, the proposal department can prioritize suggesting search keywords related to specific seasons or events. For example, the proposal department can determine the priority of effective search keywords based on market trends. This allows for the development of more effective advertising strategies by prioritizing search keywords while considering specific market trends. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input market trend data into AI and have the AI ​​perform the task of determining the priority of search keywords.

[0052] The suggestion unit can propose optimal search keywords while considering the buyer's geographical location information. For example, the suggestion unit can propose region-specific search keywords based on the buyer's current location. For example, the suggestion unit can propose relevant search keywords by referring to the buyer's past location information. For example, the suggestion unit can propose relevant search keywords by considering the buyer's travel history. This allows for the development of more effective advertising strategies by proposing optimal search keywords while considering the buyer's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the buyer's geographical location data into AI and have the AI ​​propose optimal search keywords.

[0053] The suggestion unit can analyze the buyer's social media activity and suggest relevant search keywords when making a suggestion. For example, the suggestion unit can analyze the content of the buyer's social media posts and suggest relevant search keywords. For example, the suggestion unit can suggest search keywords considering the buyer's number of followers and engagement rate on social media. For example, the suggestion unit can analyze the buyer's social media trends and suggest relevant search keywords. This allows for the development of more effective advertising strategies by analyzing the buyer's social media activity and suggesting relevant search keywords. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input data on the buyer's social media activity into AI and have the AI ​​suggest relevant search keywords.

[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 select and collect only data that meets specific conditions from past advertising data. For example, the data collection unit can collect only data with a click-through rate above a certain level from past advertising data. For example, the data collection unit can collect only data with a conversion rate above a certain level from past advertising data. For example, the data collection unit can collect only data within a specific period from past advertising data. By selecting and collecting only data that meets specific conditions, the accuracy of predictions is improved. 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 past advertising data into AI and have the AI ​​perform the selection and collection of data that meets specific conditions.

[0056] The data collection unit can filter market data by considering specific events or seasonal factors. For example, the data collection unit may prioritize collecting market data during a specific event (e.g., Black Friday). For example, the data collection unit may collect relevant market data by considering seasonal factors (e.g., the Christmas season). For example, the data collection unit may filter and collect market data during a specific promotion period. This allows for the collection of more relevant data by filtering the data by considering specific events or seasonal factors. 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 market data into AI and have the AI ​​perform data filtering that considers specific events or seasonal factors.

[0057] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-specific market data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can prioritize the collection of relevant data by considering the user's travel history. This enables more accurate data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the priority collection of highly relevant data.

[0058] The prediction unit can analyze the success factors of past advertising campaigns during prediction and optimize the prediction algorithm based on that analysis. For example, the prediction unit can analyze the click-through rate and conversion rate of past advertising campaigns and optimize the prediction algorithm. For example, the prediction unit can extract the factors of successful advertising campaigns and adjust the prediction algorithm based on them. For example, the prediction unit can analyze the failure factors of past advertising campaigns and build a prediction algorithm to avoid them. This improves the accuracy of predictions by analyzing the success factors of past advertising campaigns and optimizing the prediction algorithm based on them. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from past advertising campaigns into AI and have the AI ​​perform the analysis of success factors and the optimization of the prediction algorithm.

[0059] The forecasting unit can apply different forecasting models to specific market segments during forecasting. For example, the forecasting unit can apply a specific forecasting model to an advertising campaign targeting young people. For example, the forecasting unit can apply a different forecasting model to an advertising campaign targeting older adults. For example, the forecasting unit can apply a customized forecasting model to a specific region or country. This allows for more accurate forecasts by applying different forecasting models to specific market segments. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input data for a specific market segment into an AI and have the AI ​​apply different forecasting models.

[0060] The analysis department can perform a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements during the analysis process. For example, the analysis department can perform a detailed analysis of the factors influencing the click-through rate of advertisements and identify areas for improvement. For example, the analysis department can perform a detailed analysis of the factors influencing the conversion rate of advertisements and identify areas for improvement. For example, the analysis department can analyze the correlation between the click-through rate and conversion rate of advertisements and propose effective improvement measures. In this way, by performing a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements, effective improvement measures can be proposed. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input data on the click-through rate and conversion rate of advertisements into AI and have the AI ​​perform a detailed analysis of the factors influencing these rates.

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

[0062] Step 1: The data collection unit collects data. The data collection unit collects data such as historical advertising data and market data. Specifically, it collects data such as click-through rates and conversion rates of past advertising campaigns, ad impressions and clicks, conversions, sales data and customer data, sales trends and competitor analysis. Step 2: The prediction unit makes predictions based on the data collected by the collection unit. For example, the prediction unit predicts future advertising effectiveness based on the collected data. Specifically, it uses statistical models and machine learning algorithms to predict future click-through rates and conversion rates based on past advertising data. Step 3: The analysis department analyzes the prediction results obtained by the prediction department in real time. For example, the analysis department monitors the click-through rate and conversion rate of ads in real time, and if the performance is low, it proposes improvement plans such as changing the ad copy or reviewing the targeting. Step 4: The Proposal Department suggests search keywords based on the analysis results obtained by the Analysis Department. For example, the Proposal Department suggests optimal search keywords based on the buyer's behavior history and market trends. Specifically, it suggests keywords that the buyer has searched for in the past, their purchase history, and highly relevant search keywords.

[0063] (Example of form 2) The Web advertising simulation system according to an embodiment of the present invention is a system that uses AI to analyze and predict real data, providing simulations that are more precise than conventional estimates. This Web advertising simulation system provides predictions that take into account the buyer's behavior history and market trends, real-time analysis of results, and advice on search keywords. For example, the Web advertising simulation system first performs predictions using real data analysis and AI. In this process, it collects past advertising data and market data, and the AI ​​analyzes this data. For example, it collects data such as click-through rates and conversion rates of past advertising campaigns, and the AI ​​predicts future advertising effectiveness based on this data. Next, the Web advertising simulation system makes predictions that take into account the buyer's behavior history and market trends. Specifically, it collects data such as what actions buyers have taken and what products they are interested in, and the AI ​​analyzes this data. For example, it predicts future purchasing behavior based on data such as what products buyers have purchased in the past and what advertisements they have responded to. Furthermore, the Web advertising simulation system performs real-time analysis of results. After an advertisement is published, the AI ​​analyzes the effectiveness of the advertisement in real time and proposes improvement plans as needed. For example, the system monitors ad click-through rates and conversion rates in real time, and if performance is low, the AI ​​proposes improvement plans. Finally, the web advertising simulation system provides advice on search keywords. The AI ​​suggests optimal search keywords based on market trends and customer behavior history. For example, it suggests search keywords related to a product to customers who have a high interest in that product. By adding AI predictions to conventional prediction methods, more precise simulations become possible, and real-time analysis and detailed improvements can be easily made. In addition, because search keywords can be set taking into account customer and market trends, it is expected that advertising effectiveness will be maximized. In this way, the web advertising simulation system can maximize advertising effectiveness.

[0064] The Web advertising simulation system according to this embodiment comprises a data collection unit, a prediction unit, an analysis unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects past advertising data and market data. The data collection unit can collect data such as click-through rates and conversion rates of past advertising campaigns. For example, the data collection unit collects data such as the number of ad impressions, clicks, and conversions. The data collection unit can also collect market data. Market data includes, for example, sales data and customer data. For example, the data collection unit collects data such as sales trends and competitor analysis. The prediction unit makes predictions based on the data collected by the data collection unit. For example, the prediction unit predicts future advertising effectiveness based on the collected data. For example, the prediction unit makes predictions using statistical models or machine learning algorithms. For example, the prediction unit predicts future click-through rates and conversion rates based on past advertising data. For example, the prediction unit predicts future advertising effectiveness based on data from past advertising campaigns. The analysis unit analyzes the prediction results obtained by the prediction unit in real time. The analysis department monitors, for example, the click-through rate and conversion rate of advertisements in real time. The analysis department proposes improvement plans if, for example, the effectiveness of the advertisements is low. The analysis department monitors, for example, the click-through rate and conversion rate of advertisements in real time, and if the effectiveness is low, proposes improvement plans such as changing the ad copy or reviewing the targeting. The proposal department proposes search keywords based on the analysis results obtained by the analysis department. The proposal department proposes optimal search keywords based, for example, the buyer's behavior history and market trends. The proposal department proposes optimal search keywords based, for example, the keywords the buyer has searched for in the past and their purchase history. The proposal department proposes highly relevant search keywords based, for example, the buyer's behavior history and market trends. As a result, the web advertising simulation system according to the embodiment is capable of data collection, prediction, real-time analysis, and search keyword proposal.

[0065] The data collection unit collects data. For example, the data collection unit collects historical advertising data and market data. Specifically, it can collect data such as click-through rates and conversion rates for past advertising campaigns. For example, the data collection unit collects data such as ad impressions, clicks, and conversions. The data collection unit can also collect market data. Market data includes, for example, sales data and customer data. For example, the data collection unit collects data such as sales trends and competitor analysis. The data collection unit centrally manages this data and stores it in a database. The database is built on the cloud and is updated in real time. The data collection unit can connect with external data sources via APIs and automatically obtain the latest data. For example, it can use APIs from advertising platforms and market research companies to obtain the latest advertising and market data. In addition, the data collection unit cleans and normalizes the data to ensure data quality. Data cleaning involves imputing missing values ​​and removing outliers, and normalization unifies the scale of the data. This ensures that the collected data is stored in a format suitable for analysis and forecasting. Furthermore, the data collection unit can adjust the frequency of data collection. For example, the collection frequency can be set according to the characteristics of the data, such as collecting advertising data daily and market data weekly. This allows the data collection unit to collect data efficiently and effectively, improving the overall performance of the system.

[0066] The prediction unit makes predictions based on data collected by the data collection unit. For example, the prediction unit predicts future advertising effectiveness based on the collected data. Specifically, it uses statistical models and machine learning algorithms to make predictions. For example, the prediction unit predicts future click-through rates and conversion rates based on past advertising data. For example, the prediction unit predicts future advertising effectiveness based on data from past advertising campaigns. The prediction unit uses this data to predict metrics such as ad impressions, clicks, and conversions. Machine learning algorithms used include regression analysis, decision trees, random forests, and neural networks. These algorithms can learn from past data and predict future advertising effectiveness with high accuracy. For example, regression analysis can be used to model the relationship between ad impressions and click-through rates and predict future click-through rates. Neural networks can also be used to model the impact of multiple factors on advertising effectiveness, enabling more complex predictions. The prediction unit updates prediction results in real time, providing predictions based on the latest data. For example, when a new advertising campaign is launched, the data is immediately incorporated and the prediction model is updated. This allows the prediction unit to always provide highly accurate predictions based on the latest information, helping to maximize advertising effectiveness.

[0067] The analytics department analyzes the prediction results obtained by the forecasting department in real time. For example, the analytics department monitors ad click-through rates and conversion rates in real time. Specifically, if the effectiveness of an ad is low, it proposes improvement plans. The analytics department monitors ad click-through rates and conversion rates in real time, and if the effectiveness is low, it proposes improvement plans such as changing the ad copy or reviewing the targeting. Based on this data, the analytics department evaluates the performance of the ad and proposes the optimal improvement measures. For example, if the click-through rate is low, it proposes changing the ad copy or image, and if the conversion rate is low, it proposes reviewing the targeting or improving the landing page. The analytics department can automatically generate these proposals and notify advertisers. For example, it can use AI to evaluate the performance of an ad and automatically generate the optimal improvement measures. This allows advertisers to improve their ads quickly and effectively. Furthermore, the analytics department can also develop long-term advertising strategies by utilizing historical data and statistical information. For example, based on data from past advertising campaigns, it can predict the effectiveness of advertising in specific seasons or events and develop future advertising strategies. Furthermore, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the analysis department to not only grasp the situation in real time, but also to formulate long-term advertising strategies and detect anomalies, thereby improving the reliability and effectiveness of the entire system.

[0068] The Proposal Department suggests search keywords based on the analysis results obtained by the Analysis Department. For example, the Proposal Department suggests optimal search keywords based on the buyer's behavior history and market trends. Specifically, it suggests optimal search keywords based on keywords the buyer has searched for in the past and their purchase history. For example, the Proposal Department suggests highly relevant search keywords based on the buyer's behavior history and market trends. The Proposal Department uses this data to suggest optimal search keywords to advertisers. For example, it suggests keywords related to specific products or services based on past purchase history to optimize ad targeting. It also suggests keywords that match current trends and demand based on market trends to maximize the effectiveness of advertising. The Proposal Department uses AI to analyze this data and automatically generate optimal search keywords. For example, it uses natural language processing technology to analyze the buyer's search history and extract highly relevant keywords. It also uses machine learning algorithms to predict market trends and suggest keywords that will be in high demand in the future. The Proposal Department notifies advertisers of these suggestions and supports them in maximizing the effectiveness of their advertising campaigns. Furthermore, the Proposal Department can monitor the effectiveness of the suggested keywords and modify the suggestions as needed. For example, the system monitors the click-through rate and conversion rate of suggested keywords, and if they are ineffective, it suggests new keywords. This allows the suggestion team to always provide the most suitable search keywords based on the latest information, maximizing the effectiveness of advertising campaigns.

[0069] The data collection unit can collect historical advertising data and market data. For example, the data collection unit can collect historical advertising data. For example, the data collection unit can collect data such as click-through rates and impressions from past advertising campaigns. For example, the data collection unit can collect data such as conversion rates and sales data from past advertising campaigns. The data collection unit can also collect market data. For example, the data collection unit can collect market data such as sales data and customer data. For example, the data collection unit can collect market data such as sales trends and competitor analysis. By collecting historical advertising data and market data, the accuracy of predictions is improved. 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 historical advertising data and market data into AI and have the AI ​​perform the data collection.

[0070] The prediction unit can predict future advertising effectiveness based on collected data. For example, the prediction unit predicts future advertising effectiveness based on collected data. The prediction unit makes predictions using, for example, statistical models or machine learning algorithms. For example, the prediction unit predicts future click-through rates and conversion rates based on past advertising data. For example, the prediction unit predicts future advertising effectiveness based on data from past advertising campaigns. This improves the accuracy of advertising strategies by predicting future advertising effectiveness based on collected data. Some or all of the above processing in the prediction unit may be performed using, for example, AI, or not using AI. For example, the prediction unit can input collected data into AI and have the AI ​​perform predictions of future advertising effectiveness.

[0071] The analytics department can monitor ad click-through rates and conversion rates in real time and propose improvement plans if the performance is low. For example, the analytics department monitors ad click-through rates and conversion rates in real time. For example, the analytics department proposes improvement plans if the performance of the ads is low. For example, the analytics department monitors ad click-through rates and conversion rates in real time and proposes improvement plans such as changing the ad copy or reviewing the targeting if the performance is low. This maximizes the effectiveness of advertising by monitoring ad click-through rates and conversion rates in real time and proposing improvement plans if the performance is low. Some or all of the above processes in the analytics department may be performed using AI, for example, or not. For example, the analytics department can input ad click-through rate and conversion rate data into AI and have the AI ​​perform real-time monitoring and propose improvement plans.

[0072] The suggestion department can propose optimal search keywords based on the buyer's behavior history and market trends. For example, the suggestion department can propose optimal search keywords based on the buyer's behavior history and market trends. For example, the suggestion department can propose optimal search keywords based on keywords the buyer has searched for in the past and their purchase history. For example, the suggestion department can propose highly relevant search keywords based on the buyer's behavior history and market trends. This maximizes advertising effectiveness by proposing optimal search keywords based on the buyer's behavior history and market trends. Some or all of the above processing in the suggestion department may be performed using AI, for example, or without AI. For example, the suggestion department can input data on the buyer's behavior history and market trends into AI and have the AI ​​propose optimal search keywords.

[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, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. By adjusting the timing of data collection based on the user's emotions, the burden on the user is reduced, and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0074] The data collection unit can select and collect only data that meets specific conditions from past advertising data. For example, the data collection unit can collect only data with a click-through rate above a certain level from past advertising data. For example, the data collection unit can collect only data with a conversion rate above a certain level from past advertising data. For example, the data collection unit can collect only data within a specific period from past advertising data. By selecting and collecting only data that meets specific conditions, the accuracy of predictions is improved. 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 past advertising data into AI and have the AI ​​perform the selection and collection of data that meets specific conditions.

[0075] The data collection unit can filter market data by considering specific events or seasonal factors. For example, the data collection unit may prioritize collecting market data during a specific event (e.g., Black Friday). For example, the data collection unit may collect relevant market data by considering seasonal factors (e.g., the Christmas season). For example, the data collection unit may filter and collect market data during a specific promotion period. This allows for the collection of more relevant data by filtering the data by considering specific events or seasonal factors. 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 market data into AI and have the AI ​​perform data filtering that considers specific events or seasonal factors.

[0076] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[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 can prioritize the collection of region-specific market data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can prioritize the collection of relevant data by considering the user's travel history. This enables more accurate data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the priority collection of highly relevant data.

[0078] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of users' social media posts and collect relevant market data. For example, the data collection unit can collect data considering the number of users' social media followers and engagement rates. For example, the data collection unit can analyze users' social media trends and collect relevant data. This allows for more accurate data collection by analyzing users' social media activity and collecting relevant data. 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 data on users' social media activity into AI and have the AI ​​perform the collection of relevant data.

[0079] The prediction unit can estimate the user's emotions and adjust the way the prediction is presented based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides detailed prediction results. For example, if the user is in a hurry, the prediction unit provides concise prediction results. For example, if the user is stressed, the prediction unit provides visually easy-to-understand prediction results. In this way, by adjusting the way the prediction is presented based on the user's emotions, prediction results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into AI and have the AI ​​adjust the way the prediction is presented.

[0080] The prediction unit can analyze the success factors of past advertising campaigns during prediction and optimize the prediction algorithm based on that analysis. For example, the prediction unit can analyze the click-through rate and conversion rate of past advertising campaigns and optimize the prediction algorithm. For example, the prediction unit can extract the factors of successful advertising campaigns and adjust the prediction algorithm based on them. For example, the prediction unit can analyze the failure factors of past advertising campaigns and build a prediction algorithm to avoid them. This improves the accuracy of predictions by analyzing the success factors of past advertising campaigns and optimizing the prediction algorithm based on them. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from past advertising campaigns into AI and have the AI ​​perform the analysis of success factors and the optimization of the prediction algorithm.

[0081] The forecasting unit can apply different forecasting models to specific market segments during forecasting. For example, the forecasting unit can apply a specific forecasting model to an advertising campaign targeting young people. For example, the forecasting unit can apply a different forecasting model to an advertising campaign targeting older adults. For example, the forecasting unit can apply a customized forecasting model to a specific region or country. This allows for more accurate forecasts by applying different forecasting models to specific market segments. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input data for a specific market segment into an AI and have the AI ​​apply different forecasting models.

[0082] The prediction unit can estimate the user's emotions and adjust the level of detail of the prediction based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides a detailed prediction result. For example, if the user is in a hurry, the prediction unit provides a concise prediction result. For example, if the user is stressed, the prediction unit provides a visually easy-to-understand prediction result. In this way, by adjusting the level of detail of the prediction based on the user's emotions, prediction results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into AI and have the AI ​​perform the adjustment of the level of detail of the prediction.

[0083] The prediction unit can determine the priority of predictions based on the timing of ad placements. For example, the prediction unit prioritizes predictions when the ad placement date is approaching. For example, the prediction unit performs detailed predictions when the ad placement date is far off. The prediction unit adjusts the timing of predictions based on the ad placement date. This enables efficient predictions by determining the priority of predictions based on the ad placement date. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data on the ad placement date into AI and have AI determine the priority of predictions.

[0084] The prediction unit can adjust the order of predictions based on the relevance of the advertisements during the prediction process. For example, the prediction unit prioritizes predictions for advertisements with high relevance. For example, the prediction unit postpones predictions for advertisements with low relevance. The prediction unit adjusts the order of predictions based on the relevance of the advertisements. This allows for efficient predictions by adjusting the order of predictions based on the relevance of the advertisements. Some or all of the above processing in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input advertisement relevance data into the AI ​​and have the AI ​​perform the adjustment of the prediction order.

[0085] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. For example, if the user is stressed, the analysis unit provides visually easy-to-understand analysis results. In this way, by adjusting the analysis criteria based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the analysis criteria.

[0086] The analysis department can perform a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements during the analysis process. For example, the analysis department can perform a detailed analysis of the factors influencing the click-through rate of advertisements and identify areas for improvement. For example, the analysis department can perform a detailed analysis of the factors influencing the conversion rate of advertisements and identify areas for improvement. For example, the analysis department can analyze the correlation between the click-through rate and conversion rate of advertisements and propose effective improvement measures. In this way, by performing a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements, effective improvement measures can be proposed. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input data on the click-through rate and conversion rate of advertisements into AI and have the AI ​​perform a detailed analysis of the factors influencing these rates.

[0087] The analysis department can evaluate the effectiveness of a particular advertising campaign by comparing it to other campaigns during analysis. For example, the analysis department can evaluate the click-through rate of a particular advertising campaign by comparing it to other campaigns. For example, the analysis department can evaluate the conversion rate of a particular advertising campaign by comparing it to other campaigns. For example, the analysis department can evaluate the ROI of a particular advertising campaign by comparing it to other campaigns. This allows for the development of effective advertising strategies by evaluating the effectiveness of a particular advertising campaign by comparing it to other campaigns. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data from a particular advertising campaign into AI and have the AI ​​perform a comparative evaluation with other campaigns.

[0088] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. For example, if the user is stressed, the analysis unit provides visually easy-to-understand analysis results. In this way, by adjusting how the analysis results are displayed based on the user's emotions, the analysis results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust how the analysis results are displayed.

[0089] The analysis department can perform analyses while considering the geographical distribution of advertisements. For example, the analysis department can analyze the geographical distribution of click-through rates and conversion rates of advertisements. For example, the analysis department can compare the effectiveness of advertisements by region and propose the optimal advertising strategy. For example, the analysis department can optimize the targeting of advertisements based on geographical distribution. In this way, by performing analyses while considering the geographical distribution of advertisements, the effectiveness of advertisements by region can be optimized. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data on the geographical distribution of advertisements into AI and have the AI ​​perform an analysis that takes geographical distribution into account.

[0090] The analysis department can improve the accuracy of its analysis by referring to relevant advertising literature during the analysis process. For example, the analysis department can refer to relevant advertising literature and incorporate the latest knowledge into its analysis. For example, the analysis department can introduce effective analytical methods based on relevant advertising literature. For example, the analysis department can improve the reliability of its analysis results by referring to relevant advertising literature. In this way, by improving the accuracy of the analysis by referring to relevant advertising literature, it is possible to provide more reliable analytical results. Some or all of the above processes in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input data from relevant advertising literature into AI and have the AI ​​perform the task of improving the accuracy of the analysis.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. If the user is stressed, the suggestion unit will provide visually easy-to-understand suggestions. By adjusting the way suggestions are presented based on the user's emotions, the suggestion unit can provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into an AI and have the AI ​​adjust the way suggestions are presented.

[0092] The suggestion unit can analyze the buyer's past behavior history in detail and propose the most suitable search keywords when making a suggestion. For example, the suggestion unit can propose the most suitable search keywords based on keywords the buyer has searched for in the past. For example, the suggestion unit can analyze the buyer's past purchase history and propose relevant search keywords. For example, the suggestion unit can propose effective search keywords based on the buyer's past ad click history. This maximizes advertising effectiveness by analyzing the buyer's past behavior history in detail and proposing the most suitable search keywords. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input data on the buyer's past behavior history into AI and have the AI ​​propose the most suitable search keywords.

[0093] The proposal department can determine the priority of search keywords when making proposals, taking into account specific market trends. For example, the proposal department can analyze current market trends and prioritize suggesting relevant search keywords. For example, the proposal department can prioritize suggesting search keywords related to specific seasons or events. For example, the proposal department can determine the priority of effective search keywords based on market trends. This allows for the development of more effective advertising strategies by prioritizing search keywords while considering specific market trends. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input market trend data into AI and have the AI ​​perform the task of determining the priority of search keywords.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit will provide detailed suggestions. For example, if the user is in a hurry, the suggestion unit will provide concise suggestions. For example, if the user is stressed, the suggestion unit will provide visually easy-to-understand suggestions. By adjusting the length of suggestions based on the user's emotions, suggestions that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into AI and have the AI ​​adjust the length of the suggestions.

[0095] The suggestion unit can propose optimal search keywords while considering the buyer's geographical location information. For example, the suggestion unit can propose region-specific search keywords based on the buyer's current location. For example, the suggestion unit can propose relevant search keywords by referring to the buyer's past location information. For example, the suggestion unit can propose relevant search keywords by considering the buyer's travel history. This allows for the development of more effective advertising strategies by proposing optimal search keywords while considering the buyer's geographical location information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the buyer's geographical location data into AI and have the AI ​​propose optimal search keywords.

[0096] The suggestion unit can analyze the buyer's social media activity and suggest relevant search keywords when making a suggestion. For example, the suggestion unit can analyze the content of the buyer's social media posts and suggest relevant search keywords. For example, the suggestion unit can suggest search keywords considering the buyer's number of followers and engagement rate on social media. For example, the suggestion unit can analyze the buyer's social media trends and suggest relevant search keywords. This allows for the development of more effective advertising strategies by analyzing the buyer's social media activity and suggesting relevant search keywords. Some or all of the above processes in the suggestion unit may be performed using AI, for example, or not. For example, the suggestion unit can input data on the buyer's social media activity into AI and have the AI ​​suggest relevant search keywords.

[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] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. By adjusting the timing of data collection based on the user's emotions, the burden on the user is reduced, and efficient data collection becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into AI and have the AI ​​adjust the timing of data collection.

[0099] The data collection unit can select and collect only data that meets specific conditions from past advertising data. For example, the data collection unit can collect only data with a click-through rate above a certain level from past advertising data. For example, the data collection unit can collect only data with a conversion rate above a certain level from past advertising data. For example, the data collection unit can collect only data within a specific period from past advertising data. By selecting and collecting only data that meets specific conditions, the accuracy of predictions is improved. 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 past advertising data into AI and have the AI ​​perform the selection and collection of data that meets specific conditions.

[0100] The data collection unit can filter market data by considering specific events or seasonal factors. For example, the data collection unit may prioritize collecting market data during a specific event (e.g., Black Friday). For example, the data collection unit may collect relevant market data by considering seasonal factors (e.g., the Christmas season). For example, the data collection unit may filter and collect market data during a specific promotion period. This allows for the collection of more relevant data by filtering the data by considering specific events or seasonal factors. 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 market data into AI and have the AI ​​perform data filtering that considers specific events or seasonal factors.

[0101] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting only important data. If the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This enables efficient data collection by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[0102] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of region-specific market data based on the user's current location. For example, the data collection unit can collect highly relevant data by referring to the user's past location information. For example, the data collection unit can prioritize the collection of relevant data by considering the user's travel history. This enables more accurate data collection by prioritizing the collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the priority collection of highly relevant data.

[0103] The prediction unit can estimate the user's emotions and adjust the way the prediction is presented based on the estimated emotions. For example, if the user is relaxed, the prediction unit provides detailed prediction results. For example, if the user is in a hurry, the prediction unit provides concise prediction results. For example, if the user is stressed, the prediction unit provides visually easy-to-understand prediction results. In this way, by adjusting the way the prediction is presented based on the user's emotions, prediction results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI, for example, or not using AI. For example, the prediction unit can input user emotion data into AI and have the AI ​​adjust the way the prediction is presented.

[0104] The prediction unit can analyze the success factors of past advertising campaigns during prediction and optimize the prediction algorithm based on that analysis. For example, the prediction unit can analyze the click-through rate and conversion rate of past advertising campaigns and optimize the prediction algorithm. For example, the prediction unit can extract the factors of successful advertising campaigns and adjust the prediction algorithm based on them. For example, the prediction unit can analyze the failure factors of past advertising campaigns and build a prediction algorithm to avoid them. This improves the accuracy of predictions by analyzing the success factors of past advertising campaigns and optimizing the prediction algorithm based on them. Some or all of the above processes in the prediction unit may be performed using AI, for example, or without AI. For example, the prediction unit can input data from past advertising campaigns into AI and have the AI ​​perform the analysis of success factors and the optimization of the prediction algorithm.

[0105] The forecasting unit can apply different forecasting models to specific market segments during forecasting. For example, the forecasting unit can apply a specific forecasting model to an advertising campaign targeting young people. For example, the forecasting unit can apply a different forecasting model to an advertising campaign targeting older adults. For example, the forecasting unit can apply a customized forecasting model to a specific region or country. This allows for more accurate forecasts by applying different forecasting models to specific market segments. Some or all of the above processing in the forecasting unit may be performed using AI, for example, or without AI. For example, the forecasting unit can input data for a specific market segment into an AI and have the AI ​​apply different forecasting models.

[0106] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. For example, if the user is stressed, the analysis unit provides visually easy-to-understand analysis results. In this way, by adjusting the analysis criteria based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the analysis criteria.

[0107] The analysis department can perform a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements during the analysis process. For example, the analysis department can perform a detailed analysis of the factors influencing the click-through rate of advertisements and identify areas for improvement. For example, the analysis department can perform a detailed analysis of the factors influencing the conversion rate of advertisements and identify areas for improvement. For example, the analysis department can analyze the correlation between the click-through rate and conversion rate of advertisements and propose effective improvement measures. In this way, by performing a detailed analysis of the factors influencing the click-through rate and conversion rate of advertisements, effective improvement measures can be proposed. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input data on the click-through rate and conversion rate of advertisements into AI and have the AI ​​perform a detailed analysis of the factors influencing these rates.

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

[0109] Step 1: The data collection unit collects data. The data collection unit collects data such as historical advertising data and market data. Specifically, it collects data such as click-through rates and conversion rates of past advertising campaigns, ad impressions and clicks, conversions, sales data and customer data, sales trends and competitor analysis. Step 2: The prediction unit makes predictions based on the data collected by the collection unit. For example, the prediction unit predicts future advertising effectiveness based on the collected data. Specifically, it uses statistical models and machine learning algorithms to predict future click-through rates and conversion rates based on past advertising data. Step 3: The analysis department analyzes the prediction results obtained by the prediction department in real time. For example, the analysis department monitors the click-through rate and conversion rate of ads in real time, and if the performance is low, it proposes improvement plans such as changing the ad copy or reviewing the targeting. Step 4: The Proposal Department suggests search keywords based on the analysis results obtained by the Analysis Department. For example, the Proposal Department suggests optimal search keywords based on the buyer's behavior history and market trends. Specifically, it suggests keywords that the buyer has searched for in the past, their purchase history, and highly relevant search keywords.

[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, prediction unit, analysis unit, and proposal 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 data using the camera 42 and communication I / F 44 of the smart device 14 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future advertising effectiveness based on the collected data. The analysis unit is implemented in the control unit 46A of the smart device 14 and monitors the click-through rate and conversion rate of advertisements in real time. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal search keywords based on the buyer's behavior history and market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[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, prediction unit, analysis unit, and suggestion 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 data using the camera 42 and communication I / F 44 of the smart glasses 214 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future advertising effectiveness based on the collected data. The analysis unit is implemented in the control unit 46A of the smart glasses 214 and monitors the click-through rate and conversion rate of advertisements in real time. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests optimal search keywords based on the buyer's behavior history and market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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, prediction unit, analysis unit, and suggestion 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 data using the camera 42 and communication I / F 44 of the headset terminal 314 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future advertising effectiveness based on the collected data. The analysis unit is implemented in the control unit 46A of the headset terminal 314 and monitors the click-through rate and conversion rate of advertisements in real time. The suggestion unit is implemented in the specific processing unit 290 of the data processing unit 12 and suggests optimal search keywords based on the buyer's behavior history and market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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, prediction unit, analysis unit, and proposal 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 data using the camera 42 and communication I / F 44 of the robot 414 and analyzes the collected data using the specific processing unit 290 of the data processing unit 12. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future advertising effectiveness based on the collected data. The analysis unit is implemented in the control unit 46A of the robot 414 and monitors the click-through rate and conversion rate of advertisements in real time. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes optimal search keywords based on the buyer's behavior history and market trends. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[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) A data collection unit that collects data, A prediction unit that makes predictions based on the data collected by the collection unit, An analysis unit that analyzes the prediction results obtained by the prediction unit in real time, A proposal unit that proposes search keywords based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect past advertising data and market data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The prediction unit, Predicting future advertising effectiveness based on collected data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is We monitor ad click-through rates and conversion rates in real time and propose improvement plans if performance is low. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, We suggest optimal search keywords based on buyer behavior history and market trends. 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 Select and collect only the data that meets specific criteria from past advertising data. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting market data, filter the data to take into account specific events or seasonal factors. 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 prediction unit, It estimates the user's emotions and adjusts how predictions are expressed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The prediction unit, During the prediction process, the success factors of past advertising campaigns are analyzed, and the prediction algorithm is optimized based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 14) The prediction unit, When making forecasts, different forecasting models are applied to specific market segments. The system described in Appendix 1, characterized by the features described herein. (Note 15) The prediction unit, It estimates the user's emotions and adjusts the level of detail in the prediction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The prediction unit, When making predictions, we prioritize predictions based on when the ads will be placed. The system described in Appendix 1, characterized by the features described herein. (Note 17) The prediction unit, During prediction, the order of predictions is adjusted based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During the analysis, we will thoroughly examine the factors that cause fluctuations in ad click-through rates and conversion rates. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is During analysis, the effectiveness of a specific advertising campaign is evaluated by comparing it to other campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is When conducting the analysis, the geographical distribution of advertisements should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is During analysis, we refer to relevant literature on advertising to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned proposal section is, When making a proposal, we conduct a detailed analysis of the buyer's past behavior history to suggest the most suitable search keywords. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, prioritize search keywords by considering specific market trends. The system described in Appendix 1, characterized by the features described herein. (Note 27) 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 28) The aforementioned proposal section is, When making a proposal, we suggest the most suitable search keywords, taking into account the buyer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When making a proposal, we analyze the buyer's social media activity and suggest relevant search keywords. 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. A data collection unit that collects data, A prediction unit that makes predictions based on the data collected by the collection unit, An analysis unit that analyzes the prediction results obtained by the prediction unit in real time, A proposal unit that proposes search keywords based on the analysis results obtained by the aforementioned analysis unit, Equipped with A system characterized by the following features.

2. The aforementioned collection unit is Collect past advertising data and market data. The system according to feature 1.

3. The prediction unit, Predicting future advertising effectiveness based on collected data. The system according to feature 1.

4. The aforementioned analysis unit is We monitor ad click-through rates and conversion rates in real time and propose improvement plans if performance is low. The system according to feature 1.

5. The aforementioned proposal section is, We suggest optimal search keywords based on buyer behavior history and market trends. 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 Select and collect only the data that meets specific criteria from past advertising data. The system according to feature 1.

8. The aforementioned collection unit is When collecting market data, filter the data to take into account specific events or seasonal factors. 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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