system

Abstract

The system according to this embodiment aims to maximize the effectiveness of advertising campaigns by integrating data from multiple advertising platforms. [Solution] The system according to the embodiment comprises a collection unit, an integration unit, an analysis unit, an analysis unit, and a identification unit. The collection unit collects data from each advertising platform. The integration unit integrates the data collected by the collection unit. The analysis unit analyzes the data integrated by the integration unit to maximize the effectiveness of advertising campaigns. The analysis unit analyzes the performance of creatives based on the data obtained by the analysis unit. The identification unit identifies markets for acquiring new users based on the data obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult to integrate data from multiple advertising platforms and maximize the effect of an advertising campaign.

[0005] The system according to the embodiment aims to integrate data from multiple advertising platforms and maximize the effect of an advertising campaign.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an integration unit, an analysis unit, an analysis unit, and a identification unit. The collection unit collects data from each advertising platform. The integration unit integrates the data collected by the collection unit. The analysis unit analyzes the data integrated by the integration unit to maximize the effectiveness of advertising campaigns. The analysis unit analyzes the performance of creatives based on the data obtained by the analysis unit. The identification unit identifies markets for acquiring new users based on the data obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate data from multiple advertising platforms and maximize the effectiveness of advertising campaigns. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An advertising campaign optimization system according to an embodiment of the present invention is a system that integrates data from multiple advertising platforms and supports the optimization of advertising campaigns, creative improvement, and acquisition of new users through integration with internal data using AI automation. The advertising campaign optimization system collects and integrates data from each advertising platform. Next, the AI ​​analyzes the integrated data and optimizes the advertising campaign. Furthermore, the AI ​​analyzes the performance of the creative and makes improvement suggestions. Finally, based on the data integrated with internal data, the AI ​​identifies the target market for acquiring new users and proposes the optimal strategy. For example, the advertising campaign optimization system collects and integrates data from each advertising platform. In this process, data is obtained from the advertising platforms and centrally managed. For example, advertising performance data and user behavior data from each platform are collected. This enables centralized management of advertising data. Next, the advertising campaign optimization system uses AI to analyze the integrated data and optimize the advertising campaign. Based on the collected data, the AI ​​proposes the optimal bidding strategy. For example, it uses an automated bidding tool to provide a bidding strategy that maximizes advertising conversions and ROI. This is expected to reduce advertising costs and improve advertising effectiveness. Furthermore, the advertising campaign optimization system uses AI to analyze creative performance and suggest improvements. The AI ​​analyzes creative performance data and automatically selects the optimal creative. For example, it uses generative AI tools like CXAI to automatically generate advertising creatives and select the best one through A / B testing. This maximizes the effectiveness of the creative. Finally, the advertising campaign optimization system uses AI to identify target markets for new user acquisition and propose optimal strategies based on integrated data with internal data. The AI ​​merges internal data and advertising data to identify untapped markets. For example, a SaaS company can increase new user acquisition by integrating internal data and advertising data, allowing AI to identify untapped markets. This enables the automatic generation of advertising messages tailored to the target market, resulting in an effective approach.This system allows marketing professionals and advertising teams to centralize advertising data, optimize creatives, and streamline new user acquisition. For example, retailers can integrate advertising data from multiple platforms, and AI can adjust bids in real time, reducing advertising costs and increasing sales. Fashion brands can also use AI to analyze market trends and dynamically adjust advertising creatives, improving click-through rates and increasing sales during the season. In short, advertising campaign optimization systems can integrate data from multiple advertising platforms and use AI automation to support advertising campaign optimization, creative improvement, and new user acquisition.

[0029] The advertising campaign optimization system according to this embodiment comprises a collection unit, an integration unit, an analysis unit, an analysis unit, and a identification unit. The collection unit collects data from each advertising platform. The collection unit, for example, obtains data from advertising platforms. The collection unit can collect, for example, advertising performance data and user behavior data. The collection unit can also automate data collection using AI. The integration unit integrates the data collected by the collection unit. The integration unit, for example, centrally manages the collected data. The integration unit can perform data format conversion and eliminate duplicate data. The integration unit can also automate data integration using AI. The analysis unit analyzes the data integrated by the integration unit and optimizes advertising campaigns. The analysis unit, for example, proposes the optimal bidding strategy based on the collected data. The analysis unit can perform data analysis using AI. The analysis unit, for example, uses an automated bidding tool to provide a bidding strategy to maximize advertising conversions and ROI. The analysis unit analyzes the performance of creatives based on the data obtained by the analysis unit. The analysis department, for example, analyzes creative performance data and automatically selects the optimal creative. The analysis department can use AI to analyze creative performance. The analysis department can use generative AI tools such as CXAI to automatically generate advertising creatives and select the optimal creative through A / B testing. The identification department identifies target markets for acquiring new users based on the data obtained by the analysis department. The identification department can merge internal data and advertising data to identify untapped markets. The identification department can use AI to identify target markets. The identification department can, for example, enable a SaaS company to increase the number of new users acquired by integrating internal data and advertising data, and having AI identify untapped markets. As a result, the advertising campaign optimization system according to this embodiment can integrate data from multiple advertising platforms and support the optimization of advertising campaigns, creative improvement, and new user acquisition through AI automation.

[0030] The data collection unit collects data from various advertising platforms. For example, it retrieves data from advertising platforms. Specifically, it uses the advertising platform's API to collect advertising performance data such as ad impressions, clicks, conversions, click-through rates (CTR), and conversion rates (CVR). It can also collect user behavior data, such as the site behavior, purchase history, time spent on the site, and page views of users who clicked on ads. The data collection unit can also automate data collection using AI. For example, the AI ​​can periodically call the APIs of each platform to automatically retrieve the latest data and store it in the database. This eliminates manual data collection work and makes it possible to obtain the latest data in real time. Furthermore, the data collection unit can perform data integrity checks and detect outliers to ensure data quality. For example, if the collected data contains missing values ​​or outliers, the AI ​​will detect them and perform appropriate imputation or filtering. This allows the data collection unit to provide high-quality data and improve the accuracy of subsequent analysis and integration processes.

[0031] The Integration Unit integrates the data collected by the Collection Unit. For example, the Integration Unit centrally manages the collected data. Specifically, it converts data collected from different advertising platforms into a common format and integrates it into a single database. This allows for consistent handling of data from different platforms. The Integration Unit can perform data format conversion and duplicate data elimination. For example, if the same user clicks on ads on multiple platforms, the data is eliminated as duplicates and integrated into a single record. The Integration Unit can also automate data integration using AI. AI automatically performs data format conversion and detects and eliminates duplicate data, maintaining data integrity. Furthermore, AI can optimize the data integration process and reduce processing time. In addition, the Integration Unit can implement appropriate access control and encryption to ensure data security and privacy. For example, it can restrict access to the database to authenticated users only and encrypt data during transmission and reception. This allows the Integration Unit to integrate data securely and efficiently, improving the overall reliability of the system.

[0032] The analytics department analyzes data integrated by the integration department to optimize advertising campaigns. For example, the analytics department proposes optimal bidding strategies based on the collected data. Specifically, it uses AI to analyze data and provide bidding strategies to maximize advertising conversions and ROI. For instance, the AI ​​analyzes historical advertising performance data to calculate the optimal bid amount for specific times of day or days of the week. The AI ​​also analyzes user behavior patterns and proposes strategies to display the most suitable ads to specific user segments. The analytics department uses automated bidding tools to provide bidding strategies that maximize advertising conversions and ROI. This allows advertisers to eliminate manual bid adjustments and benefit from automated optimization by AI. Furthermore, the analytics department can analyze data in real time and continuously monitor the performance of advertising campaigns. For example, if an ad's click-through rate or conversion rate falls below the target, the AI ​​immediately detects the anomaly and proposes appropriate countermeasures. This allows the analytics department to maximize advertising campaign performance and improve advertisers' ROI.

[0033] The analytics department analyzes the performance of creatives based on the data obtained by the analysis department. For example, the analytics department analyzes creative performance data and automatically selects the optimal creative. Specifically, it uses AI to analyze creative performance and identify which creative is most effective. For example, the AI ​​compares the effectiveness of different creatives based on ad click-through rates and conversion rates and selects the most effective creative. The analytics department uses generative AI tools such as CXAI to automatically generate ad creatives and selects the optimal creative through A / B testing. This allows advertisers to eliminate manual creative creation work and enjoy automated creative generation and optimization by AI. Furthermore, the analytics department can continuously monitor creative performance and update creatives as needed. For example, if ad performance declines, the AI ​​automatically generates new creatives and selects the optimal creative through A / B testing. This allows the analytics department to maximize the performance of ad campaigns and improve advertisers' ROI.

[0034] The Specialist Department identifies target markets for acquiring new users based on data obtained by the Analysis Department. For example, the Specialist Department merges internal data and advertising data to identify untapped markets. Specifically, it uses AI to identify target markets and propose the most effective advertising strategies. For instance, AI analyzes historical advertising performance data and user behavior data to identify untapped markets in specific regions or demographics. AI also integrates internal data and advertising data to predict potential demand for specific products or services. The Specialist Department enables SaaS companies to increase new user acquisition by integrating internal and advertising data and using AI to identify untapped markets. This allows advertisers to implement effective targeting strategies and maximize new user acquisition. Furthermore, the Specialist Department can analyze the characteristics and needs of target markets in detail and propose optimal advertising messages and creatives. For example, it selects the most effective advertising messages and creatives for specific target markets to maximize the performance of advertising campaigns. This allows the Specialist Department to effectively identify target markets for acquiring new users and improve advertisers' ROI.

[0035] The data collection unit can acquire data from multiple advertising platforms. For example, the data collection unit can acquire data from advertising platforms. The data collection unit can collect advertising performance data and user behavior data. The data collection unit can also automate data collection using AI. This makes it possible to acquire data from multiple advertising platforms and manage it centrally. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when acquiring data from advertising platforms, the data collection unit can automate the data collection process using generative AI.

[0036] The integration unit can centrally manage the collected data. For example, the integration unit can centrally manage the collected data. The integration unit can perform data format conversion and eliminate duplicate data. The integration unit can also automate data integration using AI. This makes data integration more efficient by centrally managing the collected data. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when centrally managing collected data, the integration unit can automate the data integration process using generative AI.

[0037] The analytics unit can propose the optimal bidding strategy based on the collected data. For example, the analytics unit can propose the optimal bidding strategy based on the collected data. The analytics unit can perform data analysis using AI. For example, the analytics unit can use automated bidding tools to provide bidding strategies that maximize ad conversions and ROI. This maximizes the effectiveness of advertising campaigns by proposing the optimal bidding strategy based on the collected data. Some or all of the above processes in the analytics unit may be performed using AI or not. For example, when the analytics unit proposes the optimal bidding strategy based on the collected data, it can automate the data analysis process using generative AI.

[0038] The analytics department can analyze creative performance data and automatically select the optimal creative. For example, the analytics department can analyze creative performance data and automatically select the optimal creative. The analytics department can use AI to analyze creative performance. For example, the analytics department can use generative AI tools such as CXAI to automatically generate advertising creatives and select the optimal creative through A / B testing. This maximizes the effectiveness of advertising by analyzing creative performance data and automatically selecting the optimal creative. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when analyzing creative performance data, the analytics department can use generative AI to automate the data analysis process.

[0039] The specific unit can merge internal data and advertising data to identify new markets. For example, the specific unit can merge internal data and advertising data to identify untapped markets. The specific unit can use AI to identify target markets. For example, the specific unit can increase the number of new users acquired by having a SaaS company integrate internal data and advertising data, and having AI identify untapped markets. This increases the number of new users acquired by merging internal data and advertising data and identifying untapped markets. Some or all of the above processes in the specific unit may be performed using AI or not. For example, when merging internal data and advertising data, the specific unit can automate the data integration process using generative AI.

[0040] The data collection unit can analyze a user's past ad click history and select the optimal collection method when collecting data from each advertising platform. For example, the data collection unit can analyze the trends of ads a user has clicked in the past and prioritize the collection of relevant data. For example, the data collection unit can focus on collecting data from a specific advertising platform based on a user's click history. For example, the data collection unit can collect data on the most effective ad formats based on a user's click history. This allows for the priority collection of relevant data by analyzing a user's past ad click history and selecting the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when analyzing a user's past ad click history, the data collection unit can automate the data analysis process using generative AI.

[0041] The data collection unit can filter data based on the user's current areas of interest and purchase history. For example, the data collection unit prioritizes collecting data related to the user's current areas of interest. For example, the data collection unit filters and collects relevant advertising data based on the user's purchase history. For example, the data collection unit combines the user's areas of interest and purchase history to formulate an optimal data collection strategy. This allows for the efficient collection of relevant advertising data by filtering based on the user's current areas of interest and purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, when analyzing the user's current areas of interest and purchase history, the data collection unit can automate the data analysis process using generative AI.

[0042] 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-related advertising data based on the user's current location. For example, the data collection unit can analyze the user's past location information and collect relevant data. For example, the data collection unit can perform optimal data collection by matching the user's location information with the advertising target region. This allows for the efficient collection of region-related advertising data 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 or not. For example, when considering the user's geographical location information, the data collection unit can automate the data collection process using generative AI.

[0043] 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 users' interests on social media and collect relevant advertising data. For example, the data collection unit can formulate an optimal data collection strategy based on users' social media activity history. For example, the data collection unit can analyze the activity of users' followers and friends on social media and collect relevant data. This improves the accuracy of advertising data collection by analyzing users' social media activity and collecting relevant data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when analyzing users' social media activity, the data collection unit can automate the data analysis process using generative AI.

[0044] The integration unit can select the optimal integration method when integrating data, taking into account the differences in data formats of each advertising platform. For example, the integration unit can automatically convert and integrate the data formats of each advertising platform. For example, the integration unit can select the optimal integration method, taking into account the differences in data formats. For example, the integration unit can integrate using an intermediate format to absorb the differences in data formats. This improves the accuracy of data integration by selecting the optimal integration method, taking into account the differences in data formats of each advertising platform. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when considering the differences in data formats of each advertising platform, the integration unit can automate the data integration process using generative AI.

[0045] The integration unit can be equipped with functions to automatically detect and correct duplicate or missing data during data integration. For example, the integration unit can automatically detect and delete duplicate data during data integration. For example, the integration unit can automatically fill in missing data during data integration. For example, the integration unit can check and correct data integrity during data integration. This improves the accuracy of data integration by automatically detecting and correcting duplicate or missing data. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when detecting duplicate or missing data, the integration unit can automate the data cleansing process using generative AI.

[0046] The integration unit can provide customized integration methods according to the user's industry and business model during data integration. For example, the integration unit can provide the optimal data integration method according to the user's industry. For example, the integration unit can provide a customized integration method according to the user's business model. For example, the integration unit can provide the optimal data integration method by combining the user's industry and business model. This improves the accuracy of data integration by providing a customized integration method according to the user's industry and business model. Some or all of the above processing in the integration unit may be performed using AI or not. For example, when providing a customized integration method according to the user's industry and business model, the integration unit can automate the process using generative AI.

[0047] The integration unit can improve the accuracy of the integrated data by incorporating external market data and competitor data during data integration. For example, the integration unit can improve the accuracy of the integrated data by incorporating external market data. For example, the integration unit can improve the accuracy of the integrated data by incorporating competitor data. For example, the integration unit can improve the accuracy of the integrated data by combining external and internal data. This enables more accurate data analysis by incorporating external market data and competitor data and improving the accuracy of the integrated data. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when the integration unit incorporates external market data and competitor data, it can automate the data integration process using generative AI.

[0048] The analytics unit can propose the optimal bidding strategy by referring to past successful advertising campaigns during analysis. For example, the analytics unit proposes the optimal bidding strategy based on past successes. For example, the analytics unit analyzes data from successes and formulates the optimal bidding strategy. For example, the analytics unit proposes improvements to the bidding strategy by referring to successes. In this way, by referring to past successes, the optimal bidding strategy is proposed, maximizing the effectiveness of the advertising campaign. Some or all of the above processes in the analytics unit may be performed using AI or not. For example, when the analytics unit refers to past successful advertising campaigns, it can automate the data analysis process using generative AI.

[0049] The analytics unit can be enhanced to monitor ad performance in real time and adjust bidding strategies immediately during analysis. For example, the analytics unit can monitor ad performance in real time and adjust bidding strategies. For example, the analytics unit can instantly change bidding strategies in response to fluctuations in ad performance. For example, the analytics unit can provide the optimal bidding strategy based on real-time data. This maximizes the effectiveness of ad campaigns by monitoring ad performance in real time and adjusting bidding strategies immediately. Some or all of the above processes in the analytics unit may be performed using AI or not. For example, when monitoring ad performance in real time, the analytics unit can automate the data monitoring process using generative AI.

[0050] The analysis unit can provide a customized analysis algorithm according to the user's business objectives during analysis. For example, the analysis unit can provide an optimal analysis algorithm according to the user's business objectives. For example, the analysis unit can provide a customized analysis algorithm based on business objectives. For example, the analysis unit can provide an optimal analysis method considering the user's business objectives. By providing a customized analysis algorithm according to the user's business objectives, the accuracy of the analysis results is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, when the analysis unit provides a customized analysis algorithm according to the user's business objectives, it can automate the process using generative AI.

[0051] The analysis unit can improve the accuracy of its analysis results by incorporating external economic indicators and market trends during the analysis process. For example, the analysis unit can improve the accuracy of its analysis results by incorporating external economic indicators. For example, the analysis unit can improve the accuracy of its analysis results by incorporating market trends. For example, the analysis unit can improve the accuracy of its analysis results by combining external and internal data. This allows for more accurate data analysis by incorporating external economic indicators and market trends and improving the accuracy of the analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, when the analysis unit incorporates external economic indicators and market trends, it can automate the data integration process using generative AI.

[0052] The analytics department can automatically select the optimal creative when analyzing the performance of creatives by referring to past successful creative examples. For example, the analytics department can automatically select the optimal creative based on past successful examples. For example, the analytics department can analyze data from successful examples and propose the optimal creative. For example, the analytics department can automatically select areas for improvement in creatives by referring to successful examples. In this way, by referring to past successful examples, the optimal creative is automatically selected, maximizing the effectiveness of advertising. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when the analytics department refers to past successful creative examples, it can automate the data analysis process using generative AI.

[0053] The analytics department can add a function to monitor the effectiveness of creatives in real time and immediately suggest improvements when analyzing the performance of creatives. For example, the analytics department can monitor the effectiveness of creatives in real time and suggest improvements. For example, the analytics department can immediately suggest improvements in response to fluctuations in the effectiveness of creatives. For example, the analytics department can provide optimal improvement suggestions based on real-time data. This maximizes the effectiveness of advertising by monitoring the effectiveness of creatives in real time and immediately suggesting improvements. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when monitoring the effectiveness of creatives in real time, the analytics department can automate the data monitoring process using generative AI.

[0054] The analysis unit can provide customized analysis algorithms according to the user's target market when analyzing the performance of creative work. For example, the analysis unit provides the optimal analysis algorithm according to the user's target market. For example, the analysis unit provides a customized analysis algorithm based on the target market. For example, the analysis unit provides the optimal analysis method considering the user's target market. This improves the accuracy of creative performance analysis by providing a customized analysis algorithm according to the user's target market. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, when providing a customized analysis algorithm according to the user's target market, the analysis unit can automate the process using generative AI.

[0055] The analytics department can improve the accuracy of its analysis results by incorporating external market trends and competitor creatives when analyzing the performance of creatives. For example, the analytics department can improve the accuracy of its analysis results by incorporating external market trends. For example, the analytics department can improve the accuracy of its analysis results by incorporating competitor creatives. For example, the analytics department can improve the accuracy of its analysis results by combining external and internal data. This allows for more accurate data analysis by incorporating external market trends and competitor creatives and improving the accuracy of the analysis results. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when incorporating external market trends and competitor creatives, the analytics department can automate the data integration process using generative AI.

[0056] The identification unit can identify untapped markets by referring to historical market data when identifying a target market. For example, the identification unit identifies untapped markets based on historical market data. For example, the identification unit identifies untapped markets by analyzing market data. For example, the identification unit provides a method for identifying untapped markets by referring to historical data. This allows for the identification of untapped markets by referring to historical market data, thereby increasing the number of new users acquired. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, when the identification unit refers to historical market data, it can automate the data analysis process using generative AI.

[0057] The identification unit can add a function to monitor market fluctuations in real time and immediately re-evaluate the target market when identifying the target market. For example, the identification unit monitors market fluctuations in real time and re-evaluates the target market. For example, the identification unit immediately re-evaluates the target market in response to market fluctuations. For example, the identification unit re-evaluates the target market based on real-time data. This maximizes the effectiveness of advertising campaigns by monitoring market fluctuations in real time and immediately re-evaluating the target market. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when monitoring market fluctuations in real time, the identification unit can automate the data monitoring process using generative AI.

[0058] The identification unit can provide a customized identification algorithm according to the user's business objectives when identifying a target market. For example, the identification unit provides an optimal identification algorithm according to the user's business objectives. For example, the identification unit provides a customized identification algorithm based on business objectives. For example, the identification unit provides an optimal identification method considering the user's business objectives. This improves the accuracy of target market identification by providing a customized identification algorithm according to the user's business objectives. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when the identification unit provides a customized identification algorithm according to the user's business objectives, the process can be automated using generative AI.

[0059] The identification unit can improve the accuracy of its identification results by incorporating external economic indicators and market trends when identifying target markets. For example, the identification unit can improve the accuracy of its identification results by incorporating external economic indicators. For example, the identification unit can improve the accuracy of its identification results by incorporating market trends. For example, the identification unit can improve the accuracy of its identification results by combining external and internal data. This makes it possible to identify target markets more accurately by incorporating external economic indicators and market trends and improving the accuracy of the identification results. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when the identification unit incorporates external economic indicators and market trends, it can automate the data integration process using generative AI.

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

[0061] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, it can prioritize collecting advertising data related to products the user has previously purchased. It can focus on collecting data from specific advertising platforms based on the user's purchase history. It can collect data for the most effective advertising format based on the user's purchase history. This allows for the priority collection of relevant data by analyzing the user's past purchase history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when analyzing a user's past purchase history, the data collection unit can automate the data analysis process using generative AI.

[0062] The analysis unit can provide customized analysis algorithms tailored to the user's business objectives when optimizing advertising campaigns. For example, it can provide the optimal analysis algorithm according to the user's business objectives. It can provide customized analysis algorithms based on business objectives. It can provide the optimal analysis method considering the user's business objectives. By providing customized analysis algorithms tailored to the user's business objectives, the accuracy of the analysis results is improved. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, when providing customized analysis algorithms tailored to the user's business objectives, the analysis unit can automate the process using generative AI.

[0063] The identification unit can improve the accuracy of its target market identification results by incorporating external economic indicators and market trends. For example, it can improve the accuracy of the identification results by incorporating external economic indicators, market trends, and combining external and internal data. This allows for more accurate target market identification by incorporating external economic indicators and market trends and improving the accuracy of the identification results. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, when the identification unit incorporates external economic indicators and market trends, it can automate the data integration process using generative AI.

[0064] The integration unit can select the optimal integration method when integrating data, taking into account the differences in data formats of each advertising platform. For example, it can automatically convert and integrate the data formats of each advertising platform. It can select the optimal integration method considering the differences in data formats. It can integrate using an intermediate format to absorb the differences in data formats. This improves the accuracy of data integration by selecting the optimal integration method considering the differences in data formats of each advertising platform. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when considering the differences in data formats of each advertising platform, the integration unit can automate the data integration process using generative AI.

[0065] The identification unit can identify untapped markets by referring to historical market data when identifying a target market. For example, it can identify untapped markets based on historical market data. It can identify untapped markets by analyzing market data. It provides a method for identifying untapped markets by referring to historical data. This allows for the identification of untapped markets and an increase in the number of new users acquired by referring to historical market data. Some or all of the above processes in the identification unit may be performed using AI or not. For example, when the identification unit refers to historical market data, it can automate the data analysis process using generative AI.

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

[0067] Step 1: The data collection unit collects data from each advertising platform. For example, it obtains advertising performance data and user behavior data from advertising platforms. The data collection unit can also automate data collection using AI. Step 2: The integration unit integrates the data collected by the collection unit. For example, it centrally manages the collected data, converts data formats, and eliminates duplicate data. The integration unit can also automate data integration using AI. Step 3: The analytics department analyzes the data integrated by the integration department and optimizes advertising campaigns. For example, it proposes the optimal bidding strategy based on the collected data and uses automated bidding tools to provide bidding strategies that maximize advertising conversions and ROI. The analytics department can perform data analysis using AI. Step 4: The analysis department analyzes the performance of the creatives based on the data obtained by the analysis department. For example, it analyzes the performance data of the creatives and automatically selects the optimal creative. The analysis department can use AI to analyze the performance of the creatives, and can use generative AI tools such as CXAI to automatically generate advertising creatives and select the optimal creative through A / B testing. Step 5: The Identification Department identifies target markets for acquiring new users based on the data obtained by the Analysis Department. For example, they merge internal data and advertising data to identify untapped markets. The Identification Department can use AI to identify target markets, and by integrating internal data and advertising data and having AI identify untapped markets, SaaS companies can increase the number of new users acquired.

[0068] (Example of form 2) An advertising campaign optimization system according to an embodiment of the present invention is a system that integrates data from multiple advertising platforms and supports the optimization of advertising campaigns, creative improvement, and acquisition of new users through integration with internal data using AI automation. The advertising campaign optimization system collects and integrates data from each advertising platform. Next, the AI ​​analyzes the integrated data and optimizes the advertising campaign. Furthermore, the AI ​​analyzes the performance of the creative and makes improvement suggestions. Finally, based on the data integrated with internal data, the AI ​​identifies the target market for acquiring new users and proposes the optimal strategy. For example, the advertising campaign optimization system collects and integrates data from each advertising platform. In this process, data is obtained from the advertising platforms and centrally managed. For example, advertising performance data and user behavior data from each platform are collected. This enables centralized management of advertising data. Next, the advertising campaign optimization system uses AI to analyze the integrated data and optimize the advertising campaign. Based on the collected data, the AI ​​proposes the optimal bidding strategy. For example, it uses an automated bidding tool to provide a bidding strategy that maximizes advertising conversions and ROI. This is expected to reduce advertising costs and improve advertising effectiveness. Furthermore, the advertising campaign optimization system uses AI to analyze creative performance and suggest improvements. The AI ​​analyzes creative performance data and automatically selects the optimal creative. For example, it uses generative AI tools like CXAI to automatically generate advertising creatives and select the best one through A / B testing. This maximizes the effectiveness of the creative. Finally, the advertising campaign optimization system uses AI to identify target markets for new user acquisition and propose optimal strategies based on integrated data with internal data. The AI ​​merges internal data and advertising data to identify untapped markets. For example, a SaaS company can increase new user acquisition by integrating internal data and advertising data, allowing AI to identify untapped markets. This enables the automatic generation of advertising messages tailored to the target market, resulting in an effective approach.This system allows marketing professionals and advertising teams to centralize advertising data, optimize creatives, and streamline new user acquisition. For example, retailers can integrate advertising data from multiple platforms, and AI can adjust bids in real time, reducing advertising costs and increasing sales. Fashion brands can also use AI to analyze market trends and dynamically adjust advertising creatives, improving click-through rates and increasing sales during the season. In short, advertising campaign optimization systems can integrate data from multiple advertising platforms and use AI automation to support advertising campaign optimization, creative improvement, and new user acquisition.

[0069] The advertising campaign optimization system according to this embodiment comprises a collection unit, an integration unit, an analysis unit, an analysis unit, and a identification unit. The collection unit collects data from each advertising platform. The collection unit, for example, obtains data from advertising platforms. The collection unit can collect, for example, advertising performance data and user behavior data. The collection unit can also automate data collection using AI. The integration unit integrates the data collected by the collection unit. The integration unit, for example, centrally manages the collected data. The integration unit can perform data format conversion and eliminate duplicate data. The integration unit can also automate data integration using AI. The analysis unit analyzes the data integrated by the integration unit and optimizes advertising campaigns. The analysis unit, for example, proposes the optimal bidding strategy based on the collected data. The analysis unit can perform data analysis using AI. The analysis unit, for example, uses an automated bidding tool to provide a bidding strategy to maximize advertising conversions and ROI. The analysis unit analyzes the performance of creatives based on the data obtained by the analysis unit. The analysis department, for example, analyzes creative performance data and automatically selects the optimal creative. The analysis department can use AI to analyze creative performance. The analysis department can use generative AI tools such as CXAI to automatically generate advertising creatives and select the optimal creative through A / B testing. The identification department identifies target markets for acquiring new users based on the data obtained by the analysis department. The identification department can merge internal data and advertising data to identify untapped markets. The identification department can use AI to identify target markets. The identification department can, for example, enable a SaaS company to increase the number of new users acquired by integrating internal data and advertising data, and having AI identify untapped markets. As a result, the advertising campaign optimization system according to this embodiment can integrate data from multiple advertising platforms and support the optimization of advertising campaigns, creative improvement, and new user acquisition through AI automation.

[0070] The data collection unit collects data from various advertising platforms. For example, it retrieves data from advertising platforms. Specifically, it uses the advertising platform's API to collect advertising performance data such as ad impressions, clicks, conversions, click-through rates (CTR), and conversion rates (CVR). It can also collect user behavior data, such as the site behavior, purchase history, time spent on the site, and page views of users who clicked on ads. The data collection unit can also automate data collection using AI. For example, the AI ​​can periodically call the APIs of each platform to automatically retrieve the latest data and store it in the database. This eliminates manual data collection work and makes it possible to obtain the latest data in real time. Furthermore, the data collection unit can perform data integrity checks and detect outliers to ensure data quality. For example, if the collected data contains missing values ​​or outliers, the AI ​​will detect them and perform appropriate imputation or filtering. This allows the data collection unit to provide high-quality data and improve the accuracy of subsequent analysis and integration processes.

[0071] The Integration Unit integrates the data collected by the Collection Unit. For example, the Integration Unit centrally manages the collected data. Specifically, it converts data collected from different advertising platforms into a common format and integrates it into a single database. This allows for consistent handling of data from different platforms. The Integration Unit can perform data format conversion and duplicate data elimination. For example, if the same user clicks on ads on multiple platforms, the data is eliminated as duplicates and integrated into a single record. The Integration Unit can also automate data integration using AI. AI automatically performs data format conversion and detects and eliminates duplicate data, maintaining data integrity. Furthermore, AI can optimize the data integration process and reduce processing time. In addition, the Integration Unit can implement appropriate access control and encryption to ensure data security and privacy. For example, it can restrict access to the database to authenticated users only and encrypt data during transmission and reception. This allows the Integration Unit to integrate data securely and efficiently, improving the overall reliability of the system.

[0072] The analytics department analyzes data integrated by the integration department to optimize advertising campaigns. For example, the analytics department proposes optimal bidding strategies based on the collected data. Specifically, it uses AI to analyze data and provide bidding strategies to maximize advertising conversions and ROI. For instance, the AI ​​analyzes historical advertising performance data to calculate the optimal bid amount for specific times of day or days of the week. The AI ​​also analyzes user behavior patterns and proposes strategies to display the most suitable ads to specific user segments. The analytics department uses automated bidding tools to provide bidding strategies that maximize advertising conversions and ROI. This allows advertisers to eliminate manual bid adjustments and benefit from automated optimization by AI. Furthermore, the analytics department can analyze data in real time and continuously monitor the performance of advertising campaigns. For example, if an ad's click-through rate or conversion rate falls below the target, the AI ​​immediately detects the anomaly and proposes appropriate countermeasures. This allows the analytics department to maximize advertising campaign performance and improve advertisers' ROI.

[0073] The analytics department analyzes the performance of creatives based on the data obtained by the analysis department. For example, the analytics department analyzes creative performance data and automatically selects the optimal creative. Specifically, it uses AI to analyze creative performance and identify which creative is most effective. For example, the AI ​​compares the effectiveness of different creatives based on ad click-through rates and conversion rates and selects the most effective creative. The analytics department uses generative AI tools such as CXAI to automatically generate ad creatives and selects the optimal creative through A / B testing. This allows advertisers to eliminate manual creative creation work and enjoy automated creative generation and optimization by AI. Furthermore, the analytics department can continuously monitor creative performance and update creatives as needed. For example, if ad performance declines, the AI ​​automatically generates new creatives and selects the optimal creative through A / B testing. This allows the analytics department to maximize the performance of ad campaigns and improve advertisers' ROI.

[0074] The Specialist Department identifies target markets for acquiring new users based on data obtained by the Analysis Department. For example, the Specialist Department merges internal data and advertising data to identify untapped markets. Specifically, it uses AI to identify target markets and propose the most effective advertising strategies. For instance, AI analyzes historical advertising performance data and user behavior data to identify untapped markets in specific regions or demographics. AI also integrates internal data and advertising data to predict potential demand for specific products or services. The Specialist Department enables SaaS companies to increase new user acquisition by integrating internal and advertising data and using AI to identify untapped markets. This allows advertisers to implement effective targeting strategies and maximize new user acquisition. Furthermore, the Specialist Department can analyze the characteristics and needs of target markets in detail and propose optimal advertising messages and creatives. For example, it selects the most effective advertising messages and creatives for specific target markets to maximize the performance of advertising campaigns. This allows the Specialist Department to effectively identify target markets for acquiring new users and improve advertisers' ROI.

[0075] The data collection unit can acquire data from multiple advertising platforms. For example, the data collection unit can acquire data from advertising platforms. The data collection unit can collect advertising performance data and user behavior data. The data collection unit can also automate data collection using AI. This makes it possible to acquire data from multiple advertising platforms and manage it centrally. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, when the data collection unit acquires data from advertising platforms, it can automate the data collection process using generative AI.

[0076] The integration unit can centrally manage the collected data. For example, the integration unit can centrally manage the collected data. The integration unit can perform data format conversion and eliminate duplicate data. The integration unit can also automate data integration using AI. This makes data integration more efficient by centrally managing the collected data. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when centrally managing collected data, the integration unit can automate the data integration process using generative AI.

[0077] The analytics unit can propose the optimal bidding strategy based on the collected data. For example, the analytics unit can propose the optimal bidding strategy based on the collected data. The analytics unit can perform data analysis using AI. For example, the analytics unit can use automated bidding tools to provide bidding strategies that maximize ad conversions and ROI. This maximizes the effectiveness of advertising campaigns by proposing the optimal bidding strategy based on the collected data. Some or all of the above processes in the analytics unit may be performed using AI or not. For example, when the analytics unit proposes the optimal bidding strategy based on the collected data, it can automate the data analysis process using generative AI.

[0078] The analytics department can analyze creative performance data and automatically select the optimal creative. For example, the analytics department can analyze creative performance data and automatically select the optimal creative. The analytics department can use AI to analyze creative performance. For example, the analytics department can use generative AI tools such as CXAI to automatically generate advertising creatives and select the optimal creative through A / B testing. This maximizes the effectiveness of advertising by analyzing creative performance data and automatically selecting the optimal creative. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when analyzing creative performance data, the analytics department can use generative AI to automate the data analysis process.

[0079] The specific unit can merge internal data and advertising data to identify new markets. For example, the specific unit can merge internal data and advertising data to identify untapped markets. The specific unit can use AI to identify target markets. For example, the specific unit can increase the number of new users acquired by having a SaaS company integrate internal data and advertising data, and having AI identify untapped markets. This increases the number of new users acquired by merging internal data and advertising data and identifying untapped markets. Some or all of the above processes in the specific unit may be performed using AI or not. For example, when merging internal data and advertising data, the specific unit can automate the data integration process using generative AI.

[0080] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may delay data collection to reduce the user's burden. For example, if the user is relaxed, the data collection unit may collect data quickly to increase efficiency. For example, if the user is in a hurry, the data collection unit may optimize data collection to quickly obtain the necessary information. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden is reduced and efficiency is increased. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, when estimating the user's emotions, the data collection unit may automate the emotion estimation process using generative AI.

[0081] The data collection unit can analyze a user's past ad click history and select the optimal collection method when collecting data from each advertising platform. For example, the data collection unit can analyze the trends of ads a user has clicked in the past and prioritize the collection of relevant data. For example, the data collection unit can focus on collecting data from a specific advertising platform based on a user's click history. For example, the data collection unit can collect data on the most effective ad formats based on a user's click history. This allows for the priority collection of relevant data by analyzing a user's past ad click history and selecting the optimal collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when analyzing a user's past ad click history, the data collection unit can automate the data analysis process using generative AI.

[0082] The data collection unit can filter data based on the user's current areas of interest and purchase history. For example, the data collection unit prioritizes collecting data related to the user's current areas of interest. For example, the data collection unit filters and collects relevant advertising data based on the user's purchase history. For example, the data collection unit combines the user's areas of interest and purchase history to formulate an optimal data collection strategy. This allows for the efficient collection of relevant advertising data by filtering based on the user's current areas of interest and purchase history. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, when analyzing the user's current areas of interest and purchase history, the data collection unit can automate the data analysis process using generative AI.

[0083] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will postpone the collection of less important data. For example, if the user is relaxed, the data collection unit will prioritize the collection of highly important data. For example, if the user is in a hurry, the data collection unit will quickly collect the most important data. This allows for the priority collection of important data by prioritizing the data to be collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, when estimating the user's emotions, the data collection unit can automate the emotion estimation process using generative AI.

[0084] 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-related advertising data based on the user's current location. For example, the data collection unit can analyze the user's past location information and collect relevant data. For example, the data collection unit can perform optimal data collection by matching the user's location information with the advertising target region. This allows for the efficient collection of region-related advertising data 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 or not. For example, when considering the user's geographical location information, the data collection unit can automate the data collection process using generative AI.

[0085] 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 users' interests on social media and collect relevant advertising data. For example, the data collection unit can formulate an optimal data collection strategy based on users' social media activity history. For example, the data collection unit can analyze the activity of users' followers and friends on social media and collect relevant data. This improves the accuracy of advertising data collection by analyzing users' social media activity and collecting relevant data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when analyzing users' social media activity, the data collection unit can automate the data analysis process using generative AI.

[0086] The integration unit can estimate the user's emotions and adjust the data integration method based on the estimated user emotions. For example, if the user is stressed, the integration unit provides a simple integration method. For example, if the user is relaxed, the integration unit provides a detailed integration method. For example, if the user is in a hurry, the integration unit provides a method for quickly integrating the data. This reduces the user's burden and increases efficiency by adjusting the data integration method according to 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 integration unit may be performed using AI or not. For example, when estimating the user's emotions, the integration unit can automate the emotion estimation process using generative AI.

[0087] The integration unit can select the optimal integration method when integrating data, taking into account the differences in data formats of each advertising platform. For example, the integration unit can automatically convert and integrate the data formats of each advertising platform. For example, the integration unit can select the optimal integration method, taking into account the differences in data formats. For example, the integration unit can integrate using an intermediate format to absorb the differences in data formats. This improves the accuracy of data integration by selecting the optimal integration method, taking into account the differences in data formats of each advertising platform. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when considering the differences in data formats of each advertising platform, the integration unit can automate the data integration process using generative AI.

[0088] The integration unit can be equipped with functions to automatically detect and correct duplicate or missing data during data integration. For example, the integration unit can automatically detect and delete duplicate data during data integration. For example, the integration unit can automatically fill in missing data during data integration. For example, the integration unit can check and correct data integrity during data integration. This improves the accuracy of data integration by automatically detecting and correcting duplicate or missing data. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when detecting duplicate or missing data, the integration unit can automate the data cleansing process using generative AI.

[0089] The integration unit can estimate the user's emotions and adjust the display method of the integrated data based on the estimated user emotions. For example, if the user is stressed, the integration unit provides a simple and highly visible display method. For example, if the user is relaxed, the integration unit provides a display method that includes detailed information. For example, if the user is in a hurry, the integration unit provides a display method that gets straight to the point. By adjusting the display method of the integrated data according to the user's emotions, the integration unit reduces the burden on the user and increases efficiency. 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 integration unit may be performed using AI or not. For example, when estimating the user's emotions, the integration unit can automate the emotion estimation process using generative AI.

[0090] The integration unit can provide customized integration methods according to the user's industry and business model during data integration. For example, the integration unit can provide the optimal data integration method according to the user's industry. For example, the integration unit can provide a customized integration method according to the user's business model. For example, the integration unit can provide the optimal data integration method by combining the user's industry and business model. This improves the accuracy of data integration by providing a customized integration method according to the user's industry and business model. Some or all of the above processing in the integration unit may be performed using AI or not. For example, when providing a customized integration method according to the user's industry and business model, the integration unit can automate the process using generative AI.

[0091] The integration unit can improve the accuracy of the integrated data by incorporating external market data and competitor data during data integration. For example, the integration unit can improve the accuracy of the integrated data by incorporating external market data. For example, the integration unit can improve the accuracy of the integrated data by incorporating competitor data. For example, the integration unit can improve the accuracy of the integrated data by combining external and internal data. This enables more accurate data analysis by incorporating external market data and competitor data and improving the accuracy of the integrated data. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when the integration unit incorporates external market data and competitor data, it can automate the data integration process using generative AI.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. For example, if the user is relaxed, the analysis unit provides a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting the display method of the analysis results according to the user's emotions, the burden on the user is reduced and efficiency is increased. 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 analysis unit may be performed using AI or not. For example, when estimating the user's emotions, the analysis unit can automate the emotion estimation process using generative AI.

[0093] The analytics unit can propose the optimal bidding strategy by referring to past successful advertising campaigns during analysis. For example, the analytics unit proposes the optimal bidding strategy based on past successes. For example, the analytics unit analyzes data from successes and formulates the optimal bidding strategy. For example, the analytics unit proposes improvements to the bidding strategy by referring to successes. In this way, by referring to past successes, the optimal bidding strategy is proposed, maximizing the effectiveness of the advertising campaign. Some or all of the above processes in the analytics unit may be performed using AI or not. For example, when the analytics unit refers to past successful advertising campaigns, it can automate the data analysis process using generative AI.

[0094] The analytics unit can be enhanced to monitor ad performance in real time and adjust bidding strategies immediately during analysis. For example, the analytics unit can monitor ad performance in real time and adjust bidding strategies. For example, the analytics unit can instantly change bidding strategies in response to fluctuations in ad performance. For example, the analytics unit can provide the optimal bidding strategy based on real-time data. This maximizes the effectiveness of ad campaigns by monitoring ad performance in real time and adjusting bidding strategies immediately. Some or all of the above processes in the analytics unit may be performed using AI or not. For example, when monitoring ad performance in real time, the analytics unit can automate the data monitoring process using generative AI.

[0095] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will postpone less important analysis results. For example, if the user is relaxed, the analysis unit will prioritize displaying more important analysis results. For example, if the user is in a hurry, the analysis unit will quickly display the most important analysis results. In this way, by prioritizing the analysis results according to the user's emotions, important analysis results can be displayed preferentially. 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 analysis unit may be performed using AI or not. For example, when estimating the user's emotions, the analysis unit can automate the emotion estimation process using generative AI.

[0096] The analysis unit can provide a customized analysis algorithm according to the user's business objectives during analysis. For example, the analysis unit can provide an optimal analysis algorithm according to the user's business objectives. For example, the analysis unit can provide a customized analysis algorithm based on business objectives. For example, the analysis unit can provide an optimal analysis method considering the user's business objectives. By providing a customized analysis algorithm according to the user's business objectives, the accuracy of the analysis results is improved. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, when the analysis unit provides a customized analysis algorithm according to the user's business objectives, it can automate the process using generative AI.

[0097] The analysis unit can improve the accuracy of its analysis results by incorporating external economic indicators and market trends during the analysis process. For example, the analysis unit can improve the accuracy of its analysis results by incorporating external economic indicators. For example, the analysis unit can improve the accuracy of its analysis results by incorporating market trends. For example, the analysis unit can improve the accuracy of its analysis results by combining external and internal data. This allows for more accurate data analysis by incorporating external economic indicators and market trends and improving the accuracy of the analysis results. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, when the analysis unit incorporates external economic indicators and market trends, it can automate the data integration process using generative AI.

[0098] The analysis unit can estimate the user's emotions and adjust the method of analyzing the creative performance based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides a simple analysis method. For example, if the user is relaxed, the analysis unit provides a detailed analysis method. For example, if the user is in a hurry, the analysis unit provides a method for quickly analyzing the creative performance. This reduces the user's burden and increases efficiency by adjusting the method of analyzing the creative performance according to 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 analysis unit may be performed using AI or not. For example, when estimating the user's emotions, the analysis unit can automate the emotion estimation process using generative AI.

[0099] The analytics department can automatically select the optimal creative when analyzing the performance of creatives by referring to past successful creative examples. For example, the analytics department can automatically select the optimal creative based on past successful examples. For example, the analytics department can analyze data from successful examples and propose the optimal creative. For example, the analytics department can automatically select areas for improvement in creatives by referring to successful examples. In this way, by referring to past successful examples, the optimal creative is automatically selected, maximizing the effectiveness of advertising. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when the analytics department refers to past successful creative examples, it can automate the data analysis process using generative AI.

[0100] The analytics department can add a function to monitor the effectiveness of creatives in real time and immediately suggest improvements when analyzing the performance of creatives. For example, the analytics department can monitor the effectiveness of creatives in real time and suggest improvements. For example, the analytics department can immediately suggest improvements in response to fluctuations in the effectiveness of creatives. For example, the analytics department can provide optimal improvement suggestions based on real-time data. This maximizes the effectiveness of advertising by monitoring the effectiveness of creatives in real time and immediately suggesting improvements. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when monitoring the effectiveness of creatives in real time, the analytics department can automate the data monitoring process using generative AI.

[0101] The analysis unit can estimate the user's emotions and adjust how creatives are displayed based on those estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit provides a display method that includes detailed information. If the user is in a hurry, the analysis unit provides a display method that gets straight to the point. By adjusting how creatives are displayed according to the user's emotions, the system reduces the user's burden and increases efficiency. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, when estimating the user's emotions, the analysis unit can automate the emotion estimation process using generative AI.

[0102] The analysis unit can provide customized analysis algorithms according to the user's target market when analyzing the performance of creative work. For example, the analysis unit provides the optimal analysis algorithm according to the user's target market. For example, the analysis unit provides a customized analysis algorithm based on the target market. For example, the analysis unit provides the optimal analysis method considering the user's target market. This improves the accuracy of creative performance analysis by providing a customized analysis algorithm according to the user's target market. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, when providing a customized analysis algorithm according to the user's target market, the analysis unit can automate the process using generative AI.

[0103] The analytics department can improve the accuracy of its analysis results by incorporating external market trends and competitor creatives when analyzing the performance of creatives. For example, the analytics department can improve the accuracy of its analysis results by incorporating external market trends. For example, the analytics department can improve the accuracy of its analysis results by incorporating competitor creatives. For example, the analytics department can improve the accuracy of its analysis results by combining external and internal data. This allows for more accurate data analysis by incorporating external market trends and competitor creatives and improving the accuracy of the analysis results. Some or all of the above processes in the analytics department may be performed using AI or not. For example, when incorporating external market trends and competitor creatives, the analytics department can automate the data integration process using generative AI.

[0104] The identification unit can estimate the user's emotions and adjust the target market identification method based on the estimated user emotions. For example, if the user is stressed, the identification unit provides a simple identification method. For example, if the user is relaxed, the identification unit provides a detailed identification method. For example, if the user is in a hurry, the identification unit provides a method for quickly identifying the target market. This reduces the user's burden and increases efficiency by adjusting the target market identification method according to 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 identification unit may be performed using AI or not. For example, when estimating the user's emotions, the identification unit can automate the emotion estimation process using generative AI.

[0105] The identification unit can identify untapped markets by referring to historical market data when identifying a target market. For example, the identification unit identifies untapped markets based on historical market data. For example, the identification unit identifies untapped markets by analyzing market data. For example, the identification unit provides a method for identifying untapped markets by referring to historical data. This allows for the identification of untapped markets by referring to historical market data, thereby increasing the number of new users acquired. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, when the identification unit refers to historical market data, it can automate the data analysis process using generative AI.

[0106] The identification unit can add a function to monitor market fluctuations in real time and immediately re-evaluate the target market when identifying the target market. For example, the identification unit monitors market fluctuations in real time and re-evaluates the target market. For example, the identification unit immediately re-evaluates the target market in response to market fluctuations. For example, the identification unit re-evaluates the target market based on real-time data. This maximizes the effectiveness of advertising campaigns by monitoring market fluctuations in real time and immediately re-evaluating the target market. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when monitoring market fluctuations in real time, the identification unit can automate the data monitoring process using generative AI.

[0107] The identification unit can estimate the user's emotions and prioritize target markets based on the estimated emotions. For example, if the user is stressed, the identification unit will postpone target markets of lower importance. For example, if the user is relaxed, the identification unit will prioritize identifying high-importance target markets. For example, if the user is in a hurry, the identification unit will quickly identify the most important target markets. This allows for the priority identification of important target markets by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when estimating the user's emotions, the identification unit can automate the emotion estimation process using generative AI.

[0108] The identification unit can provide a customized identification algorithm according to the user's business objectives when identifying a target market. For example, the identification unit provides an optimal identification algorithm according to the user's business objectives. For example, the identification unit provides a customized identification algorithm based on business objectives. For example, the identification unit provides an optimal identification method considering the user's business objectives. This improves the accuracy of target market identification by providing a customized identification algorithm according to the user's business objectives. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when the identification unit provides a customized identification algorithm according to the user's business objectives, the process can be automated using generative AI.

[0109] The identification unit can improve the accuracy of its identification results by incorporating external economic indicators and market trends when identifying target markets. For example, the identification unit can improve the accuracy of its identification results by incorporating external economic indicators. For example, the identification unit can improve the accuracy of its identification results by incorporating market trends. For example, the identification unit can improve the accuracy of its identification results by combining external and internal data. This makes it possible to identify target markets more accurately by incorporating external economic indicators and market trends and improving the accuracy of the identification results. Some or all of the above processing in the identification unit may be performed using AI or not. For example, when the identification unit incorporates external economic indicators and market trends, it can automate the data integration process using generative AI.

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

[0111] The analytics unit can estimate the user's emotions and optimize advertising campaigns based on those estimated emotions. For example, if a user is stressed, the analytics unit reduces the frequency of ads to lessen the user's burden. If a user is relaxed, the analytics unit increases the frequency of ads and displays them at effective times. If a user is in a hurry, the analytics unit displays effective ads in a short amount of time. This maximizes the effectiveness of advertising by optimizing campaigns according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analytics unit may be performed using AI or not. For example, when estimating a user's emotions, the analytics unit can automate the emotion estimation process using generative AI.

[0112] The data collection unit can analyze a user's past purchase history and select the optimal data collection method. For example, it can prioritize collecting advertising data related to products the user has previously purchased. It can focus on collecting data from specific advertising platforms based on the user's purchase history. It can collect data for the most effective advertising format based on the user's purchase history. This allows for the priority collection of relevant data by analyzing the user's past purchase history and selecting the optimal data collection method. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, when analyzing a user's past purchase history, the data collection unit can automate the data analysis process using generative AI.

[0113] The integration unit can estimate the user's emotions and determine the priority of data integration based on the estimated emotions. For example, if the user is stressed, less important data integration will be postponed. If the user is relaxed, high-priority data will be prioritized for integration. If the user is in a hurry, the most important data will be quickly integrated. This allows for the priority of integrating important data by determining the priority of data integration according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the integration unit may be performed using AI or not. For example, when estimating the user's emotions, the integration unit can automate the emotion estimation process using generative AI.

[0114] The analysis unit can provide customized analysis algorithms tailored to the user's business objectives when optimizing advertising campaigns. For example, it can provide the optimal analysis algorithm according to the user's business objectives. It can provide customized analysis algorithms based on business objectives. It can provide the optimal analysis method considering the user's business objectives. By providing customized analysis algorithms tailored to the user's business objectives, the accuracy of the analysis results is improved. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, when providing customized analysis algorithms tailored to the user's business objectives, the analysis unit can automate the process using generative AI.

[0115] The analysis unit can estimate user emotions during creative performance analysis and adjust how the creative is displayed based on the estimated user emotions. For example, if a user is stressed, a simple and highly visible display method is provided. If a user is relaxed, a display method including detailed information is provided. If a user is in a hurry, a display method that gets straight to the point is provided. By adjusting how the creative is displayed according to the user's emotions, the burden on the user is reduced and efficiency is increased. 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 analysis unit may be performed using AI or not. For example, when estimating user emotions, the analysis unit can automate the emotion estimation process using generative AI.

[0116] The identification unit can improve the accuracy of its target market identification results by incorporating external economic indicators and market trends. For example, it can improve the accuracy of the identification results by incorporating external economic indicators, market trends, and combining external and internal data. This allows for more accurate target market identification by incorporating external economic indicators and market trends and improving the accuracy of the identification results. Some or all of the above-described processes in the identification unit may be performed using AI or not. For example, when the identification unit incorporates external economic indicators and market trends, it can automate the data integration process using generative AI.

[0117] The data collection unit can estimate the user's emotions and adjust the data collection method based on the estimated emotions. For example, if the user is stressed, the frequency of data collection can be reduced to lessen the user's burden. If the user is relaxed, the frequency of data collection can be increased to improve efficiency. If the user is in a hurry, the necessary data can be collected quickly. In this way, by adjusting the data collection method according to the user's emotions, the user's burden is reduced and efficiency is improved. 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, when estimating the user's emotions, the data collection unit can automate the emotion estimation process using generative AI.

[0118] The integration unit can select the optimal integration method when integrating data, taking into account the differences in data formats of each advertising platform. For example, it can automatically convert and integrate the data formats of each advertising platform. It can select the optimal integration method considering the differences in data formats. It can integrate using an intermediate format to absorb the differences in data formats. This improves the accuracy of data integration by selecting the optimal integration method considering the differences in data formats of each advertising platform. Some or all of the above processes in the integration unit may be performed using AI or not. For example, when considering the differences in data formats of each advertising platform, the integration unit can automate the data integration process using generative AI.

[0119] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, less important analysis results will be displayed later. If the user is relaxed, more important analysis results will be displayed preferentially. If the user is in a hurry, the most important analysis results will be displayed quickly. In this way, by prioritizing the analysis results according to the user's emotions, important analysis results can be displayed preferentially. 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 analysis unit may be performed using AI or not. For example, when estimating the user's emotions, the analysis unit can automate the emotion estimation process using generative AI.

[0120] The identification unit can identify untapped markets by referring to historical market data when identifying a target market. For example, it can identify untapped markets based on historical market data. It can identify untapped markets by analyzing market data. It provides a method for identifying untapped markets by referring to historical data. This allows for the identification of untapped markets and an increase in the number of new users acquired by referring to historical market data. Some or all of the above processes in the identification unit may be performed using AI or not. For example, when the identification unit refers to historical market data, it can automate the data analysis process using generative AI.

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

[0122] Step 1: The data collection unit collects data from each advertising platform. For example, it obtains advertising performance data and user behavior data from advertising platforms. The data collection unit can also automate data collection using AI. Step 2: The integration unit integrates the data collected by the collection unit. For example, it centrally manages the collected data, converts data formats, and eliminates duplicate data. The integration unit can also automate data integration using AI. Step 3: The analytics department analyzes the data integrated by the integration department and optimizes advertising campaigns. For example, it proposes the optimal bidding strategy based on the collected data and uses automated bidding tools to provide bidding strategies that maximize advertising conversions and ROI. The analytics department can perform data analysis using AI. Step 4: The analysis department analyzes the performance of the creatives based on the data obtained by the analysis department. For example, it analyzes the performance data of the creatives and automatically selects the optimal creative. The analysis department can use AI to analyze the performance of the creatives, and can use generative AI tools such as CXAI to automatically generate advertising creatives and select the optimal creative through A / B testing. Step 5: The Identification Department identifies target markets for acquiring new users based on the data obtained by the Analysis Department. For example, they merge internal data and advertising data to identify untapped markets. The Identification Department can use AI to identify target markets, and by integrating internal data and advertising data and having AI identify untapped markets, SaaS companies can increase the number of new users acquired.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0126] Each of the multiple elements described above, including the collection unit, integration unit, analysis unit, and identification unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects data from each advertising platform. The integration unit is implemented by the identification unit 290 of the data processing device 12 and centrally manages the collected data. The analysis unit is implemented by the identification unit 290 of the data processing device 12 and analyzes the integrated data to optimize advertising campaigns. The analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the performance of creatives. The identification unit is implemented by the identification unit 290 of the data processing device 12 and identifies target markets. 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.

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

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

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

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

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

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

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

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

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the collection unit, integration unit, analysis unit, and identification unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects data from each advertising platform. The integration unit is implemented by the identification unit 290 of the data processing device 12 and centrally manages the collected data. The analysis unit is implemented by the identification unit 290 of the data processing device 12 and analyzes the integrated data to optimize advertising campaigns. The analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the performance of creatives. The identification unit is implemented by the identification unit 290 of the data processing device 12 and identifies target markets. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

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

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

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

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

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

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

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

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

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the collection unit, integration unit, analysis unit, and identification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects data from each advertising platform. The integration unit is implemented by the identification unit 290 of the data processing unit 12 and centrally manages the collected data. The analysis unit is implemented by the identification unit 290 of the data processing unit 12 and analyzes the integrated data to optimize advertising campaigns. The analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the performance of creatives. The identification unit is implemented by the identification unit 290 of the data processing unit 12 and identifies target markets. 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.

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

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

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

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

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

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

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the collection unit, integration unit, analysis unit, and identification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects data from each advertising platform. The integration unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and centrally manages the collected data. The analysis unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and analyzes the integrated data to optimize advertising campaigns. The analysis unit is implemented by, for example, the control unit 46A of the robot 414 and analyzes the performance of creatives. The identification unit is implemented by, for example, the identification unit 290 of the data processing unit 12 and identifies the target market. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

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

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

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

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

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

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0185] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0194] (Note 1) A data collection unit that collects data from each advertising platform, An integration unit that integrates the data collected by the aforementioned collection unit, The data integrated by the aforementioned integration unit is analyzed by an analysis unit to maximize the effectiveness of the advertising campaign, An analysis unit analyzes the performance of creative work based on the data obtained from the aforementioned analysis unit, The system includes an identification unit that identifies a market for acquiring new users based on the data obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Obtain data from multiple advertising platforms. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned integration unit is Centralized management of collected data The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, We propose the optimal bidding strategy based on the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is It analyzes creative performance data and automatically selects the optimal creative. The system described in Appendix 1, characterized by the features described herein. (Note 6) The specified part is, Merge internal data and advertising data to identify new markets. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data from each advertising platform, the system analyzes the user's past ad click history to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and purchase history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned integration unit is We estimate user sentiment and adjust the data integration method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned integration unit is When integrating data, the optimal integration method is selected, taking into account the differences in data formats across each advertising platform. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned integration unit is Add a feature to automatically detect and correct duplicate and missing data during data integration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned integration unit is It estimates the user's emotions and adjusts how the integrated data is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned integration unit is When integrating data, we provide customized integration methods tailored to the user's industry and business model. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned integration unit is During data integration, external market and competitor data are incorporated to improve the accuracy of the integrated data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, During analysis, we refer to past advertising campaign success stories to suggest the optimal bidding strategy. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During analysis, we will add a feature that monitors ad performance in real time and adjusts bidding strategies immediately. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During analysis, we provide customized analysis algorithms tailored to the user's business objectives. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During analysis, external economic indicators and market trends are incorporated to improve the accuracy of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is We estimate user sentiment and adjust the creative performance analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit is When analyzing the performance of creative projects, the system automatically selects the optimal creative project by referencing past successful examples. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit is We will add a feature that allows us to monitor the effectiveness of creatives in real time and immediately suggest improvements when analyzing creative performance. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is It estimates the user's emotions and adjusts how creative content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is When analyzing the performance of creative content, we provide customized analysis algorithms tailored to the user's target market. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit is When analyzing creative performance, we incorporate external market trends and competitor creatives to improve the accuracy of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 31) The specified part is, We estimate user sentiment and adjust our target market identification methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The specified part is, When identifying a target market, historical market data is used to identify untapped markets. The system described in Appendix 1, characterized by the features described herein. (Note 33) The specified part is, When identifying a target market, we will add a feature that monitors market fluctuations in real time and immediately re-evaluates the target market. The system described in Appendix 1, characterized by the features described herein. (Note 34) The specified part is, It estimates user sentiment and determines target market priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The specified part is, When identifying a target market, we provide a customized algorithm tailored to the user's business objectives. The system described in Appendix 1, characterized by the features described herein. (Note 36) The specified part is, When identifying target markets, incorporating external economic indicators and market trends improves the accuracy of the identification results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. A data collection unit that collects data from each advertising platform, An integration unit that integrates the data collected by the aforementioned collection unit, The data integrated by the aforementioned integration unit is analyzed by an analysis unit to maximize the effectiveness of the advertising campaign, An analysis unit analyzes the performance of creative work based on the data obtained from the aforementioned analysis unit, The system includes an identification unit that identifies a market for acquiring new users based on the data obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Obtain data from multiple advertising platforms. The system according to feature 1.

3. The aforementioned integration unit is Centralized management of collected data The system according to feature 1.

4. The aforementioned analysis unit, We propose the optimal bidding strategy based on the collected data. The system according to feature 1.

5. The aforementioned analysis unit is It analyzes creative performance data and automatically selects the optimal creative. The system according to feature 1.

6. The specified part is, Merge internal data and advertising data to identify new markets. The system according to feature 1.

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

8. The aforementioned collection unit is When collecting data from each advertising platform, the system analyzes the user's past ad click history to select the most suitable data collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and purchase history. The system according to feature 1.

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

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