system

Abstract

The system according to this embodiment aims to automate the advertising submission process and reduce the burden on advertisers and agencies. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a review unit, an operation unit, and an analysis unit. The reception unit receives the requirements of the advertiser. The generation unit generates advertising content based on the requirements received by the reception unit. The review unit reviews the advertising content generated by the generation unit. The operation unit automates advertising operations based on the advertising content reviewed by the review unit. The analysis unit analyzes the performance data of the advertisements.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the advertising submission process is complicated and imposes a great burden on advertisers and agencies.

[0005] The system according to the embodiment aims to automate the advertising submission process and reduce the burden on advertisers and agencies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a review unit, an operation unit, and an analysis unit. The reception unit receives the requirements of the advertiser. The generation unit generates advertising content based on the requirements received by the reception unit. The review unit reviews the advertising content generated by the generation unit. The operation unit automates advertising operations based on the advertising content reviewed by the review unit. The analysis unit analyzes the performance data of the advertisements. [Effects of the Invention]

[0007] The system according to this embodiment can automate the advertising submission process and reduce the burden on advertisers and agencies. [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, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The automated advertising submission system according to an embodiment of the present invention is a mechanism that aims to expand the customer base by automating the entire advertising submission process, which places a heavy burden on advertising clients and agencies, using a generating AI to reduce the burden. The automated advertising submission system reduces the burden on advertising clients and agencies by automating the generation and review of advertising content. This lowers the barrier to advertising, making it easier for small and medium-sized enterprises (SMEs) to place advertisements. First, when an advertising client or agency generates advertising content, the generating AI automatically generates the content. The generating AI generates optimal advertising content based on the advertiser's requirements and target audience. Next, the generated advertising content is automatically reviewed by the generating AI. The generating AI checks whether the advertising content conforms to the guidelines and regulations of each platform and makes corrections as necessary. Furthermore, the generating AI also automates advertising operations. The generating AI analyzes advertising performance data in real time and proposes the optimal advertising operation strategy. This mechanism reduces the burden on advertising clients and agencies and lowers the barrier to advertising. In particular, for SMEs, the effort and cost of advertising are reduced, making it possible for more companies to place advertisements. Furthermore, advertising agencies will benefit from increased productivity and the ability to support more clients. This invention is expected to not only reduce the burden on advertising clients and agencies but also promote efficiency and growth in the overall advertising market. By utilizing generation AI, the quality of advertising content will improve and the effectiveness of advertising will be maximized, leading to increased advertiser satisfaction. In addition, increased productivity for advertising agencies will strengthen the competitiveness of the entire advertising market. Thus, the automated ad submission system can reduce the burden on advertising clients and agencies and promote efficiency and growth in the overall advertising market.

[0029] The automated ad submission system according to this embodiment comprises a reception unit, a generation unit, a review unit, an operation unit, and an analysis unit. The reception unit receives the advertiser's requirements. The advertiser's requirements include, but are not limited to, text format, image format, video format, etc. For example, if the advertiser inputs "I want to create a promotional ad for a new product," the reception unit passes the information to the generation unit based on those requirements. The generation unit uses a generation AI to generate ad content based on the requirements received by the reception unit. The generation unit generates optimal ad content based on, for example, the advertiser's requirements and target audience. The generation unit can also use a generation AI to generate ad content based on the advertiser's requirements. The generation unit can also use a generation AI to generate ad content based on the attributes of the target audience. The review unit reviews the ad content generated by the generation unit. For example, the review unit checks whether the ad content conforms to the guidelines and regulations of each platform and makes corrections as necessary. The review unit can also use a generation AI to check whether the ad content conforms to the guidelines. The review department can also use generative AI to check whether advertising content complies with regulations. The operations department automates advertising operations based on the advertising content reviewed by the review department. For example, the operations department analyzes ad click-through rates and conversion rates and automatically adjusts the optimal ad delivery timing and targeting settings. The operations department can also use generative AI to analyze ad click-through rates. The operations department can also use generative AI to analyze ad conversion rates. The analytics department analyzes ad performance data in real time and provides feedback to the operations department. For example, the analytics department analyzes performance data such as ad click-through rates, conversion rates, and impressions in real time. The analytics department can also use generative AI to analyze ad performance data in real time. The analytics department can also use generative AI to analyze ad performance data and provide feedback to the operations department.As a result, the automated ad submission system according to this embodiment can automate the entire ad submission process, reducing the burden on advertising clients and agencies.

[0030] The reception department receives requirements from advertisers. These requirements include, but are not limited to, text, image, and video formats. For example, if an advertiser enters "I want to create a promotional ad for a new product," the reception department will pass this information to the production department. The reception department then analyzes the requirements entered by the advertiser in detail and extracts the necessary information. For example, it collects detailed requirements such as the advertising objective, target audience, budget, and delivery period. This ensures that the production department has the accurate information needed to generate the advertising content. Furthermore, the reception department also has the function of receiving materials (images, videos, text, etc.) provided by advertisers and converting them to the appropriate format. For example, it can adjust the resolution of image files or convert the format of video files. This supports the production department in smoothly generating advertising content. The reception department also provides an interface to facilitate communication with advertisers. For example, it can report progress to advertisers and request additional information through chatbots and email notification functions. This allows advertisers to always know the progress of the process and take necessary actions quickly.

[0031] The generation unit uses generation AI to generate advertising content based on requirements received by the reception unit. For example, the generation unit generates optimal advertising content based on the advertiser's requirements and target audience. The generation unit can also generate advertising content based on the advertiser's requirements using generation AI. The generation unit can also generate advertising content based on the attributes of the target audience using generation AI. The generation AI uses natural language processing technology to analyze the advertiser's requirements and extract appropriate keywords and phrases. This ensures that the advertising content effectively appeals to the target audience. Furthermore, the generation AI can use image recognition technology to analyze images and video materials provided by the advertiser and propose optimal layouts and designs. For example, the generation AI analyzes product images provided by the advertiser and automatically generates design elements to emphasize their features. The generation AI can also learn from past advertising data and extract patterns of effective advertising content. This allows the generation unit to quickly and accurately generate advertising content that best suits the advertiser's requirements. Additionally, the generation unit has a function to provide the generated advertising content to the advertiser as a preview and receive feedback. Based on advertiser feedback, the generation AI regenerates the ad content, completing the final ad content. This ensures that advertisers receive ad content they are satisfied with.

[0032] The review department reviews the advertising content generated by the generation department. For example, the review department checks whether the advertising content complies with the guidelines and regulations of each platform and makes corrections as necessary. The review department can also use generation AI to check whether the advertising content complies with the guidelines. The review department can also use generation AI to check whether the advertising content complies with the regulations. Generation AI has learned the guidelines and regulations of each platform in advance and can quickly determine whether the advertising content complies with these standards. For example, generation AI analyzes the text and images in the advertising content to check for inappropriate expressions or prohibited elements. Generation AI can also verify that the format and size of the advertising content complies with the requirements of each platform. This ensures that the review department can deliver the advertising content without problems on each platform. Furthermore, the review department can evaluate the quality of the advertising content and make improvement suggestions as necessary. For example, generation AI evaluates the visual appeal and clarity of the message of the advertising content and suggests areas for improvement. This maximizes the effectiveness of the advertising content. The review department also facilitates the process of revising and improving advertising content through communication with advertisers. For example, the review department notifies advertisers of the review results and provides specific explanations of necessary corrections, supporting advertisers in taking prompt action. This ensures that the review department can guarantee the quality and suitability of the ad content, allowing advertisers to deliver ads that meet their expectations.

[0033] The operations department automates ad operations based on ad content reviewed by the review department. For example, the operations department analyzes ad click-through rates and conversion rates and automatically adjusts the optimal ad delivery timing and targeting settings. The operations department can also use generative AI to analyze ad click-through rates. The operations department can also use generative AI to analyze ad conversion rates. The generative AI analyzes ad performance data in real time and proposes the optimal ad delivery strategy. For example, based on past ad data, the generative AI analyzes trends in click-through rates and conversion rates at specific times of day or on specific days of the week and automatically sets the optimal delivery timing. The generative AI can also analyze the attributes and behavioral patterns of the target audience and propose the optimal targeting settings. This allows the operations department to automatically execute strategies to maximize the effectiveness of ads. Furthermore, the operations department has the function to continuously monitor ad performance and make adjustments as needed. For example, if the ad click-through rate or conversion rate does not reach the target value, the generative AI re-evaluates the ad content and delivery settings and proposes improvement measures. This allows the operations department to continuously optimize ad performance. Furthermore, the operations department provides advertisers with performance reports, making the results of ad operations visible. This allows advertisers to understand the effectiveness of their ad operations and use this information to develop future strategies.

[0034] The analytics department analyzes advertising performance data in real time and provides feedback to the operations department. For example, the analytics department analyzes performance data such as click-through rates, conversion rates, and impressions in real time. The analytics department can also use generative AI to analyze advertising performance data in real time. The generative AI analyzes advertising performance data in detail, identifying factors that contribute to and hinder performance improvement. For example, the generative AI analyzes fluctuations in click-through rates and conversion rates, evaluating the impact of specific creatives or targeting settings on performance. Furthermore, the generative AI can use anomaly detection algorithms to detect unusual performance fluctuations early and warn the operations department. This allows the analytics department to continuously monitor advertising performance and respond quickly. In addition, the analytics department provides advertisers with detailed performance reports, visualizing the results of advertising operations. This allows advertisers to understand the effectiveness of their advertising and use this information to develop future strategies. Furthermore, the analytics department can perform trend analysis and predictive analysis based on past data, contributing to the optimization of future advertising operations. This allows the analytics department to analyze advertising performance with high accuracy and work in conjunction with the operations department to maximize the effectiveness of advertising operations.

[0035] The generation unit can generate optimal advertising content based on the advertiser's requirements and target audience. For example, the generation unit generates advertising content based on the advertiser's requirements. The generation unit can also generate advertising content based on the advertiser's requirements using generation AI. The generation unit can also generate advertising content based on the attributes of the target audience using generation AI. The generation unit can also generate optimal advertising content based on the advertiser's requirements and target audience using generation AI. This maximizes the effectiveness of advertising by generating optimal advertising content based on the advertiser's requirements and target audience.

[0036] The review department can check whether advertising content complies with the guidelines and regulations of each platform and make corrections as necessary. For example, the review department can check whether advertising content complies with the guidelines of each platform. The review department can also use generative AI to check whether advertising content complies with the guidelines. The review department can also use generative AI to check whether advertising content complies with the regulations. The review department can also use generative AI to check whether advertising content complies with the guidelines and regulations of each platform and make corrections as necessary. This ensures the compliance of advertisements by checking whether advertising content complies with the guidelines and regulations of each platform and making corrections as necessary.

[0037] The operations department can analyze ad click-through rates and conversion rates and automatically adjust the optimal ad delivery timing and targeting settings. For example, the operations department can analyze ad click-through rates and adjust the optimal ad delivery timing. The operations department can also use generative AI to analyze ad click-through rates. The operations department can also use generative AI to analyze ad conversion rates. The operations department can use generative AI to analyze ad click-through rates and conversion rates and automatically adjust the optimal ad delivery timing and targeting settings. This allows for the maximization of ad effectiveness by analyzing ad click-through rates and conversion rates and automatically adjusting the optimal ad delivery timing and targeting settings.

[0038] The analytics department can analyze advertising performance data in real time and provide feedback to the operations department. For example, the analytics department can analyze performance data such as click-through rates, conversion rates, and impressions in real time. The analytics department can also use generative AI to analyze advertising performance data in real time. By analyzing advertising performance data in real time and providing feedback to the operations department, the accuracy of advertising operations can be improved.

[0039] The reception department can receive input from advertisers, such as "I want to create a promotional ad for a new product," and then pass that information to the generation department based on those requirements. The reception department can also use AI to analyze the advertiser's requirements and pass that information to the generation department. This allows for more efficient generation of advertising content by passing information to the generation department based on the advertiser's requirements.

[0040] The reception department can analyze the advertiser's past requirement submission history and select the optimal reception method. For example, the reception department can automatically display requirements that the advertiser has frequently submitted in the past as candidates. The reception department can also prioritize suggesting input methods (voice, text, etc.) that the advertiser has used in the past. The reception department can also predict and suggest requirements to be used during specific time periods based on the advertiser's past requirement submission history. In this way, by analyzing the advertiser's past requirement submission history, the optimal reception method can be selected, enabling efficient requirement reception. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's past requirement submission history data into a generating AI and have the generating AI select the optimal reception method.

[0041] The reception department can filter the advertiser's current business situation and market trends when receiving requirements. For example, the reception department can propose the most suitable advertising requirements based on the advertiser's current sales data. The reception department can also analyze market trends and filter the requirements that are best suited to the advertiser's business. The reception department can also propose the most suitable advertising requirements by considering the activities of the advertiser's competitors. In this way, the reception department can propose the most suitable advertising requirements by filtering based on the advertiser's current business situation and market trends. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's business situation data and market trend data into a generating AI and have the generating AI perform the filtering of the most suitable advertising requirements.

[0042] The reception department can prioritize receiving requirements that are highly relevant, taking into account the advertiser's geographical location information. For example, the reception department can prioritize receiving region-specific requirements based on the advertiser's location. The reception department can also propose optimal advertising requirements based on the advertiser's geographical location information. The reception department can also prioritize receiving requirements by taking into account market trends related to the advertiser's location. This allows the reception department to address region-specific requirements by prioritizing highly relevant requirements while considering the advertiser's geographical location information. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's geographical location information data into a generating AI and have the generating AI select highly relevant requirements.

[0043] The reception department can analyze the advertiser's social media activity when receiving requirements and accept relevant requirements. For example, the reception department can propose optimal advertising requirements based on the advertiser's social media activity. The reception department can also analyze the reactions of the advertiser's followers and prioritize accepting highly relevant requirements. The reception department can also accept requirements while considering the advertiser's social media trends. In this way, relevant requirements can be accepted by analyzing the advertiser's social media activity. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's social media activity data into a generating AI and have the generating AI select relevant requirements.

[0044] The generation unit can adjust the level of detail generated based on the importance of the advertisement when generating advertising content. For example, in the case of an important campaign advertisement, the generation unit will generate advertising content that includes detailed information. For short-term promotional advertisements, the generation unit can also generate concise and impactful advertising content. For advertisements aimed at increasing brand awareness, the generation unit can also generate visually-oriented advertising content. By adjusting the level of detail generated based on the importance of the advertisement, the effectiveness of the advertisement can be maximized. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input advertising importance data into a generation AI and have the generation AI perform the adjustment of the level of detail generated.

[0045] The generation unit can apply different generation algorithms depending on the ad category when generating advertising content. For example, in the case of product ads, the generation unit can apply a generation algorithm that emphasizes the product's features. In the case of service ads, the generation unit can also apply a generation algorithm that emphasizes the service's convenience. In the case of brand ads, the generation unit can also apply a generation algorithm that emphasizes the brand image. By applying different generation algorithms depending on the ad category, the effectiveness of the ads can be maximized. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input ad category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0046] The generation unit can determine the generation priority based on the ad submission timing when generating ad content. For example, in the case of an urgent ad submission, the generation unit will generate the ad content with the highest priority. In the case of a normal ad submission, the generation unit can also generate the ad content with the normal priority. In the case of a long-term campaign ad, the generation unit can also generate the ad content systematically. This maximizes the effectiveness of the ad by determining the generation priority based on the ad submission timing. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input ad submission timing data into a generation AI and have the generation AI determine the generation priority.

[0047] The generation unit can adjust the generation order based on the relevance of the ads when generating advertising content. For example, the generation unit can prioritize generating highly relevant advertising content. It can also generate moderately relevant advertising content next. It can also generate lowly relevant advertising content last. By adjusting the generation order based on the relevance of the ads, the effectiveness of the ads can be maximized. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input advertising relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0048] The review department can improve the accuracy of its review process by considering the interrelationships between advertisements during the review of advertising content. For example, the review department can compare multiple pieces of advertising content from the same advertiser to verify consistency. The review department can also compare advertising content within the same category to evaluate quality. The review department can also analyze the interrelationships between advertising content and apply the most appropriate review criteria. This improves the accuracy of the review process by considering the interrelationships between advertisements. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input data on the interrelationships of advertising content into a generating AI and have the generating AI perform the task of improving the accuracy of the review process.

[0049] The review department can consider the advertiser's attribute information when reviewing advertising content. For example, the review department can apply appropriate review criteria based on the advertiser's industry. The review department can also adjust the review criteria by referring to the advertiser's past review history. The review department can also set review criteria based on the advertiser's target audience. This improves the accuracy of the review by considering the advertiser's attribute information. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input advertiser attribute information data into a generating AI and have the generating AI set the review criteria.

[0050] The review department can consider the geographical distribution of advertisements when reviewing advertising content. For example, the review department can apply appropriate review criteria based on the target area of ​​the advertisement. The review department can also analyze the geographical distribution of advertisements and set optimal review criteria. The review department can also adjust the review criteria considering the regional characteristics of advertisements. This improves the accuracy of the review process by considering the geographical distribution of advertisements. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input geographical distribution data of advertisements into a generating AI and have the generating AI set the review criteria.

[0051] The review department can improve the accuracy of its review process by referring to relevant literature on advertisements during the review of advertising content. For example, the review department can set review criteria based on relevant literature on advertisements. The review department can also improve the accuracy of its review by referring to relevant literature on advertisements. The review department can also analyze relevant literature on advertisements and apply the most appropriate review criteria. This allows the review department to improve the accuracy of its review by referring to relevant literature on advertisements. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input relevant literature data on advertisements into a generating AI and have the generating AI set the review criteria.

[0052] The operations department can select the optimal operation method by referring to past advertising performance data when running advertisements. For example, the operations department can select the optimal timing for ad delivery based on past advertising performance data. The operations department can also analyze past advertising performance data and select the optimal targeting settings. The operations department can also refer to past advertising performance data to select the optimal ad creative. In this way, by referring to past advertising performance data, the optimal operation method can be selected and the effectiveness of the advertisements can be maximized. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department can input past advertising performance data into a generating AI and have the generating AI select the optimal operation method.

[0053] The operations department can customize the methods of advertising operations based on the current market conditions of the advertisement. For example, the operations department can analyze current market trends and propose the optimal advertising operations. The operations department can also customize the methods of advertising operations considering the market conditions of the advertiser's industry. The operations department can also adjust the methods of advertising operations based on the actions of the advertiser's competitors. By customizing the methods of advertising operations based on the current market conditions of the advertisement, the effectiveness of the advertisement can be maximized. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department can input current market data into a generating AI and have the generating AI perform the customization of the methods of advertising operations.

[0054] The operations department can select the optimal operation method when running advertisements, taking into account the geographical location information of the advertisements. For example, the operations department can select the optimal advertising operation method based on the target area of ​​the advertisement. The operations department can also select the optimal timing for ad delivery based on the geographical location information of the advertisements. The operations department can also customize the operation method, taking into account the regional characteristics of the advertisements. In this way, by considering the geographical location information of the advertisements, the optimal operation method can be selected and the effectiveness of the advertisements can be maximized. Some or all of the above processes performed by the operations department may be performed using AI or not. For example, the operations department can input the geographical location information data of the advertisements into a generating AI and have the generating AI perform the selection of the optimal operation method.

[0055] The operations department can analyze the social media activity of an advertisement and propose operational methods during the advertising campaign. For example, the operations department can propose the optimal advertising operational methods based on the advertiser's social media activity. The operations department can also analyze the reactions of the advertiser's followers and propose the optimal advertising operational methods. The operations department can also propose operational methods considering the trends on the advertiser's social media. In this way, by analyzing the social media activity of an advertisement, the optimal operational methods can be proposed, maximizing the effectiveness of the advertisement. Some or all of the above processes performed by the operations department may be carried out using AI or not. For example, the operations department can input the advertiser's social media activity data into a generating AI and have the generating AI execute the proposal of operational methods.

[0056] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing advertising performance data. For example, the analysis unit can select the optimal analysis algorithm based on past advertising performance data. The analysis unit can also analyze past analysis data and optimize the analysis algorithm. The analysis unit can also adjust the analysis algorithm by referring to past advertising performance data. This allows the analysis algorithm to be optimized by referring to past analysis data, thereby improving the accuracy of the analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0057] The analysis unit can apply different analysis methods to each advertising category when analyzing advertising performance data. For example, in the case of product advertisements, the analysis unit can apply an analysis method based on product sales data. In the case of service advertisements, the analysis unit can also apply an analysis method based on service usage data. In the case of brand advertisements, the analysis unit can also apply an analysis method based on brand awareness data. By applying different analysis methods to each advertising category, the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input advertising category data into a generating AI and have the generating AI execute the application of the analysis method.

[0058] The analysis unit can weight the analysis data based on the timing of ad submissions when analyzing ad performance data. For example, the analysis unit can increase the weighting of the analysis data in the case of urgent ad submissions. For normal ad submissions, the analysis unit can also provide analysis data with normal weighting. For long-term campaign ads, the analysis unit can also adjust the weighting systematically. This improves the accuracy of the analysis by weighting the analysis data based on the timing of ad submissions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input ad submission timing data into a generating AI and have the generating AI perform the weighting of the analysis data.

[0059] The analysis unit can perform analysis by referring to relevant market data for advertising when analyzing advertising performance data. For example, the analysis unit applies an analysis method based on the relevant market data for advertising. The analysis unit can also improve the accuracy of the analysis by referring to the relevant market data for advertising. The analysis unit can also analyze the relevant market data for advertising and apply the most suitable analysis method. This allows the accuracy of the analysis to be improved by referring to the relevant market data for advertising. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant market data for advertising into a generating AI and have the generating AI execute the application of the analysis method.

[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 automated ad submission system can further reference the advertiser's past ad performance data to generate optimal ad content. For example, it can generate ad content that incorporates elements from ads that have recorded high click-through rates in the past. It can also incorporate elements from ads with high conversion rates in the past. It can also incorporate elements from ads with a large number of impressions in the past. In this way, by referencing the advertiser's past ad performance data, it is possible to generate optimal ad content and maximize the effectiveness of the ads. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the advertiser's past ad performance data into a generation AI and have the generation AI execute the generation of optimal ad content.

[0062] The automated ad submission system can further generate ad content while considering the advertiser's geographical location. For example, it can generate ad content that incorporates region-specific elements based on the advertiser's location. It can also select the optimal ad delivery timing based on the advertiser's geographical location. It can also generate ad content while considering market trends related to the advertiser's location. In this way, by considering the advertiser's geographical location, it is possible to generate optimal ad content and maximize the effectiveness of the ad. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the advertiser's geographical location data into a generation AI and have the generation AI execute the generation of optimal ad content.

[0063] The automated ad submission system can further analyze the advertiser's past requirement submission history and select the optimal submission method. For example, it can automatically display requirements that the advertiser has frequently submitted in the past as candidates. It can also prioritize suggesting input methods (voice, text, etc.) that the advertiser has used in the past. It can also predict and suggest requirements to be used during specific time periods based on the advertiser's past requirement submission history. In this way, by analyzing the advertiser's past requirement submission history, the optimal submission method can be selected, enabling efficient requirement reception. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's past requirement submission history data into a generating AI and have the generating AI select the optimal submission method.

[0064] The automated ad submission system can further analyze the advertiser's social media activity and accept relevant requirements. For example, it can suggest optimal ad requirements based on the advertiser's social media activity. It can also analyze the reactions of the advertiser's followers and prioritize accepting highly relevant requirements. It can also accept requirements while considering the advertiser's social media trends. In this way, by analyzing the advertiser's social media activity, it can accept relevant requirements. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's social media activity data into a generating AI and have the generating AI select relevant requirements.

[0065] The automated ad submission system can further filter based on the advertiser's current business situation and market trends. For example, it can suggest optimal advertising requirements based on the advertiser's current sales data. It can also analyze market trends and filter the requirements that are best suited to the advertiser's business. It can also suggest optimal advertising requirements by considering the actions of the advertiser's competitors. In this way, by filtering based on the advertiser's current business situation and market trends, it can suggest the most suitable advertising requirements. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input advertiser business situation data and market trend data into a generating AI and have the generating AI perform the filtering of optimal advertising requirements.

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

[0067] Step 1: The reception department receives the advertiser's requirements. These requirements include text format, image format, video format, etc. For example, if an advertiser enters "I want to create a promotional ad for a new product," the information is passed to the generation department based on that requirement. Step 2: The generation unit generates advertising content based on the requirements received by the reception unit. The generation unit uses generation AI to generate optimal advertising content based on the advertiser's requirements and target audience. Step 3: The review department reviews the advertising content generated by the generation department. The review department checks whether the advertising content complies with the guidelines and regulations of each platform and makes corrections as necessary. Step 4: The operations department automates ad operations based on the ad content reviewed by the review department. The operations department analyzes the ad click-through rate and conversion rate and automatically adjusts the optimal ad delivery timing and targeting settings. Step 5: The analytics department analyzes advertising performance data in real time and provides feedback to the operations department. The analytics department analyzes performance data such as click-through rates, conversion rates, and impressions in real time.

[0068] (Example of form 2) The automated advertising submission system according to an embodiment of the present invention is a mechanism that aims to expand the customer base by automating the entire advertising submission process, which places a heavy burden on advertising clients and agencies, using a generating AI to reduce the burden. The automated advertising submission system reduces the burden on advertising clients and agencies by automating the generation and review of advertising content. This lowers the barrier to advertising, making it easier for small and medium-sized enterprises (SMEs) to place advertisements. First, when an advertising client or agency generates advertising content, the generating AI automatically generates the content. The generating AI generates optimal advertising content based on the advertiser's requirements and target audience. Next, the generated advertising content is automatically reviewed by the generating AI. The generating AI checks whether the advertising content conforms to the guidelines and regulations of each platform and makes corrections as necessary. Furthermore, the generating AI also automates advertising operations. The generating AI analyzes advertising performance data in real time and proposes the optimal advertising operation strategy. This mechanism reduces the burden on advertising clients and agencies and lowers the barrier to advertising. In particular, for SMEs, the effort and cost of advertising are reduced, making it possible for more companies to place advertisements. Furthermore, advertising agencies will benefit from increased productivity and the ability to support more clients. This invention is expected to not only reduce the burden on advertising clients and agencies but also promote efficiency and growth in the overall advertising market. By utilizing generation AI, the quality of advertising content will improve and the effectiveness of advertising will be maximized, leading to increased advertiser satisfaction. In addition, increased productivity for advertising agencies will strengthen the competitiveness of the entire advertising market. Thus, the automated ad submission system can reduce the burden on advertising clients and agencies and promote efficiency and growth in the overall advertising market.

[0069] The automated ad submission system according to this embodiment comprises a reception unit, a generation unit, a review unit, an operation unit, and an analysis unit. The reception unit receives the advertiser's requirements. The advertiser's requirements include, but are not limited to, text format, image format, video format, etc. For example, if the advertiser inputs "I want to create a promotional ad for a new product," the reception unit passes the information to the generation unit based on those requirements. The generation unit uses a generation AI to generate ad content based on the requirements received by the reception unit. The generation unit generates optimal ad content based on, for example, the advertiser's requirements and target audience. The generation unit can also use a generation AI to generate ad content based on the advertiser's requirements. The generation unit can also use a generation AI to generate ad content based on the attributes of the target audience. The review unit reviews the ad content generated by the generation unit. For example, the review unit checks whether the ad content conforms to the guidelines and regulations of each platform and makes corrections as necessary. The review unit can also use a generation AI to check whether the ad content conforms to the guidelines. The review department can also use generative AI to check whether advertising content complies with regulations. The operations department automates advertising operations based on the advertising content reviewed by the review department. For example, the operations department analyzes ad click-through rates and conversion rates and automatically adjusts the optimal ad delivery timing and targeting settings. The operations department can also use generative AI to analyze ad click-through rates. The operations department can also use generative AI to analyze ad conversion rates. The analytics department analyzes ad performance data in real time and provides feedback to the operations department. For example, the analytics department analyzes performance data such as ad click-through rates, conversion rates, and impressions in real time. The analytics department can also use generative AI to analyze ad performance data in real time. The analytics department can also use generative AI to analyze ad performance data and provide feedback to the operations department.As a result, the automated ad submission system according to this embodiment can automate the entire ad submission process, reducing the burden on advertising clients and agencies.

[0070] The reception department receives requirements from advertisers. These requirements include, but are not limited to, text, image, and video formats. For example, if an advertiser enters "I want to create a promotional ad for a new product," the reception department will pass this information to the production department. The reception department then analyzes the requirements entered by the advertiser in detail and extracts the necessary information. For example, it collects detailed requirements such as the advertising objective, target audience, budget, and delivery period. This ensures that the production department has the accurate information needed to generate the advertising content. Furthermore, the reception department also has the function of receiving materials (images, videos, text, etc.) provided by advertisers and converting them to the appropriate format. For example, it can adjust the resolution of image files or convert the format of video files. This supports the production department in smoothly generating advertising content. The reception department also provides an interface to facilitate communication with advertisers. For example, it can report progress to advertisers and request additional information through chatbots and email notification functions. This allows advertisers to always know the progress of the process and take necessary actions quickly.

[0071] The generation unit uses generation AI to generate advertising content based on requirements received by the reception unit. For example, the generation unit generates optimal advertising content based on the advertiser's requirements and target audience. The generation unit can also generate advertising content based on the advertiser's requirements using generation AI. The generation unit can also generate advertising content based on the attributes of the target audience using generation AI. The generation AI uses natural language processing technology to analyze the advertiser's requirements and extract appropriate keywords and phrases. This ensures that the advertising content effectively appeals to the target audience. Furthermore, the generation AI can use image recognition technology to analyze images and video materials provided by the advertiser and propose optimal layouts and designs. For example, the generation AI analyzes product images provided by the advertiser and automatically generates design elements to emphasize their features. The generation AI can also learn from past advertising data and extract patterns of effective advertising content. This allows the generation unit to quickly and accurately generate advertising content that best suits the advertiser's requirements. Additionally, the generation unit has a function to provide the generated advertising content to the advertiser as a preview and receive feedback. Based on advertiser feedback, the generation AI regenerates the ad content, completing the final ad content. This ensures that advertisers receive ad content they are satisfied with.

[0072] The review department reviews the advertising content generated by the generation department. For example, the review department checks whether the advertising content complies with the guidelines and regulations of each platform and makes corrections as necessary. The review department can also use generation AI to check whether the advertising content complies with the guidelines. The review department can also use generation AI to check whether the advertising content complies with the regulations. Generation AI has learned the guidelines and regulations of each platform in advance and can quickly determine whether the advertising content complies with these standards. For example, generation AI analyzes the text and images in the advertising content to check for inappropriate expressions or prohibited elements. Generation AI can also verify that the format and size of the advertising content complies with the requirements of each platform. This ensures that the review department can deliver the advertising content without problems on each platform. Furthermore, the review department can evaluate the quality of the advertising content and make improvement suggestions as necessary. For example, generation AI evaluates the visual appeal and clarity of the message of the advertising content and suggests areas for improvement. This maximizes the effectiveness of the advertising content. The review department also facilitates the process of revising and improving advertising content through communication with advertisers. For example, the review department notifies advertisers of the review results and provides specific explanations of necessary corrections, supporting advertisers in taking prompt action. This ensures that the review department can guarantee the quality and suitability of the ad content, allowing advertisers to deliver ads that meet their expectations.

[0073] The operations department automates ad operations based on ad content reviewed by the review department. For example, the operations department analyzes ad click-through rates and conversion rates and automatically adjusts the optimal ad delivery timing and targeting settings. The operations department can also use generative AI to analyze ad click-through rates. The operations department can also use generative AI to analyze ad conversion rates. The generative AI analyzes ad performance data in real time and proposes the optimal ad delivery strategy. For example, based on past ad data, the generative AI analyzes trends in click-through rates and conversion rates at specific times of day or on specific days of the week and automatically sets the optimal delivery timing. The generative AI can also analyze the attributes and behavioral patterns of the target audience and propose the optimal targeting settings. This allows the operations department to automatically execute strategies to maximize the effectiveness of ads. Furthermore, the operations department has the function to continuously monitor ad performance and make adjustments as needed. For example, if the ad click-through rate or conversion rate does not reach the target value, the generative AI re-evaluates the ad content and delivery settings and proposes improvement measures. This allows the operations department to continuously optimize ad performance. Furthermore, the operations department provides advertisers with performance reports, making the results of ad operations visible. This allows advertisers to understand the effectiveness of their ad operations and use this information to develop future strategies.

[0074] The analytics department analyzes advertising performance data in real time and provides feedback to the operations department. For example, the analytics department analyzes performance data such as click-through rates, conversion rates, and impressions in real time. The analytics department can also use generative AI to analyze advertising performance data in real time. The generative AI analyzes advertising performance data in detail, identifying factors that contribute to and hinder performance improvement. For example, the generative AI analyzes fluctuations in click-through rates and conversion rates, evaluating the impact of specific creatives or targeting settings on performance. Furthermore, the generative AI can use anomaly detection algorithms to detect unusual performance fluctuations early and warn the operations department. This allows the analytics department to continuously monitor advertising performance and respond quickly. In addition, the analytics department provides advertisers with detailed performance reports, visualizing the results of advertising operations. This allows advertisers to understand the effectiveness of their advertising and use this information to develop future strategies. Furthermore, the analytics department can perform trend analysis and predictive analysis based on past data, contributing to the optimization of future advertising operations. This allows the analytics department to analyze advertising performance with high accuracy and work in conjunction with the operations department to maximize the effectiveness of advertising operations.

[0075] The generation unit can generate optimal advertising content based on the advertiser's requirements and target audience. For example, the generation unit generates advertising content based on the advertiser's requirements. The generation unit can also generate advertising content based on the advertiser's requirements using generation AI. The generation unit can also generate advertising content based on the attributes of the target audience using generation AI. The generation unit can also generate optimal advertising content based on the advertiser's requirements and target audience using generation AI. This maximizes the effectiveness of advertising by generating optimal advertising content based on the advertiser's requirements and target audience.

[0076] The review department can check whether advertising content complies with the guidelines and regulations of each platform and make corrections as necessary. For example, the review department can check whether advertising content complies with the guidelines of each platform. The review department can also use generative AI to check whether advertising content complies with the guidelines. The review department can also use generative AI to check whether advertising content complies with the regulations. The review department can also use generative AI to check whether advertising content complies with the guidelines and regulations of each platform and make corrections as necessary. This ensures the compliance of advertisements by checking whether advertising content complies with the guidelines and regulations of each platform and making corrections as necessary.

[0077] The operations department can analyze ad click-through rates and conversion rates and automatically adjust the optimal ad delivery timing and targeting settings. For example, the operations department can analyze ad click-through rates and adjust the optimal ad delivery timing. The operations department can also use generative AI to analyze ad click-through rates. The operations department can also use generative AI to analyze ad conversion rates. The operations department can use generative AI to analyze ad click-through rates and conversion rates and automatically adjust the optimal ad delivery timing and targeting settings. This allows for the maximization of ad effectiveness by analyzing ad click-through rates and conversion rates and automatically adjusting the optimal ad delivery timing and targeting settings.

[0078] The analytics department can analyze advertising performance data in real time and provide feedback to the operations department. For example, the analytics department can analyze performance data such as click-through rates, conversion rates, and impressions in real time. The analytics department can also use generative AI to analyze advertising performance data in real time. By analyzing advertising performance data in real time and providing feedback to the operations department, the accuracy of advertising operations can be improved.

[0079] The reception department can receive input from advertisers, such as "I want to create a promotional ad for a new product," and then pass that information to the generation department based on those requirements. The reception department can also use AI to analyze the advertiser's requirements and pass that information to the generation department. This allows for more efficient generation of advertising content by passing information to the generation department based on the advertiser's requirements.

[0080] The reception desk can estimate the advertiser's emotions and adjust how requirements are received based on the estimated emotions. For example, if the advertiser is stressed, the reception desk can provide a simple interface and minimize the input steps. If the advertiser is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the advertiser is in a hurry, the reception desk can prioritize voice input to allow for quick requirement entry. This improves advertiser satisfaction by adjusting how requirements are received based on the advertiser's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input advertiser emotion data into a generative AI and have the generative AI perform emotion estimation.

[0081] The reception department can analyze the advertiser's past requirement submission history and select the optimal reception method. For example, the reception department can automatically display requirements that the advertiser has frequently submitted in the past as candidates. The reception department can also prioritize suggesting input methods (voice, text, etc.) that the advertiser has used in the past. The reception department can also predict and suggest requirements to be used during specific time periods based on the advertiser's past requirement submission history. In this way, by analyzing the advertiser's past requirement submission history, the optimal reception method can be selected, enabling efficient requirement reception. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's past requirement submission history data into a generating AI and have the generating AI select the optimal reception method.

[0082] The reception department can filter the advertiser's current business situation and market trends when receiving requirements. For example, the reception department can propose the most suitable advertising requirements based on the advertiser's current sales data. The reception department can also analyze market trends and filter the requirements that are best suited to the advertiser's business. The reception department can also propose the most suitable advertising requirements by considering the activities of the advertiser's competitors. In this way, the reception department can propose the most suitable advertising requirements by filtering based on the advertiser's current business situation and market trends. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's business situation data and market trend data into a generating AI and have the generating AI perform the filtering of the most suitable advertising requirements.

[0083] The reception desk can estimate the advertiser's emotions and determine the priority of requests to be accepted based on the estimated emotions. For example, if the advertiser feels urgent, the reception desk will accept the request with the highest priority. If the advertiser is relaxed, the reception desk may accept the request with the normal priority. If the advertiser is anxious, the reception desk may also accept the request with priority in order to respond quickly. This can improve advertiser satisfaction by determining the priority of requests to be accepted based on the advertiser'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 reception desk may be performed using AI or not. For example, the reception desk can input advertiser emotion data into a generative AI and have the generative AI perform emotion estimation.

[0084] The reception department can prioritize receiving requirements that are highly relevant, taking into account the advertiser's geographical location information. For example, the reception department can prioritize receiving region-specific requirements based on the advertiser's location. The reception department can also propose optimal advertising requirements based on the advertiser's geographical location information. The reception department can also prioritize receiving requirements by taking into account market trends related to the advertiser's location. This allows the reception department to address region-specific requirements by prioritizing highly relevant requirements while considering the advertiser's geographical location information. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's geographical location information data into a generating AI and have the generating AI select highly relevant requirements.

[0085] The reception department can analyze the advertiser's social media activity when receiving requirements and accept relevant requirements. For example, the reception department can propose optimal advertising requirements based on the advertiser's social media activity. The reception department can also analyze the reactions of the advertiser's followers and prioritize accepting highly relevant requirements. The reception department can also accept requirements while considering the advertiser's social media trends. In this way, relevant requirements can be accepted by analyzing the advertiser's social media activity. Some or all of the above processes in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's social media activity data into a generating AI and have the generating AI select relevant requirements.

[0086] The generation unit can estimate the advertiser's emotions and adjust the presentation of the advertising content based on the estimated emotions. For example, if the advertiser is relaxed, the generation unit can generate advertising content with a soft tone. If the advertiser is in a hurry, the generation unit can also generate concise advertising content that gets straight to the point. If the advertiser is excited, the generation unit can also generate visually stimulating advertising content. This allows advertiser satisfaction to be improved by adjusting the presentation of advertising content based on the advertiser's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input advertiser emotion data into a generation AI and have the generation AI adjust the presentation of the advertising content.

[0087] The generation unit can adjust the level of detail generated based on the importance of the advertisement when generating advertising content. For example, in the case of an important campaign advertisement, the generation unit will generate advertising content that includes detailed information. For short-term promotional advertisements, the generation unit can also generate concise and impactful advertising content. For advertisements aimed at increasing brand awareness, the generation unit can also generate visually-oriented advertising content. By adjusting the level of detail generated based on the importance of the advertisement, the effectiveness of the advertisement can be maximized. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input advertising importance data into a generation AI and have the generation AI perform the adjustment of the level of detail generated.

[0088] The generation unit can apply different generation algorithms depending on the ad category when generating advertising content. For example, in the case of product ads, the generation unit can apply a generation algorithm that emphasizes the product's features. In the case of service ads, the generation unit can also apply a generation algorithm that emphasizes the service's convenience. In the case of brand ads, the generation unit can also apply a generation algorithm that emphasizes the brand image. By applying different generation algorithms depending on the ad category, the effectiveness of the ads can be maximized. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input ad category data into a generation AI and have the generation AI execute the application of the generation algorithm.

[0089] The generation unit can estimate the advertiser's emotions and adjust the length of the ad content based on the estimated emotions. For example, if the advertiser is in a hurry, the generation unit can generate short, concise ad content. If the advertiser is relaxed, the generation unit can also generate longer ad content with detailed explanations. If the advertiser is excited, the generation unit can also generate ad content with visually stimulating effects. This allows advertiser satisfaction to be improved by adjusting the length of the ad content based on the advertiser's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input advertiser emotion data into a generation AI and have the generation AI adjust the length of the ad content.

[0090] The generation unit can determine the generation priority based on the ad submission timing when generating ad content. For example, in the case of an urgent ad submission, the generation unit will generate the ad content with the highest priority. In the case of a normal ad submission, the generation unit can also generate the ad content with the normal priority. In the case of a long-term campaign ad, the generation unit can also generate the ad content systematically. This maximizes the effectiveness of the ad by determining the generation priority based on the ad submission timing. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input ad submission timing data into a generation AI and have the generation AI determine the generation priority.

[0091] The generation unit can adjust the generation order based on the relevance of the ads when generating advertising content. For example, the generation unit can prioritize generating highly relevant advertising content. It can also generate moderately relevant advertising content next. It can also generate lowly relevant advertising content last. By adjusting the generation order based on the relevance of the ads, the effectiveness of the ads can be maximized. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input advertising relevance data into a generation AI and have the generation AI perform the adjustment of the generation order.

[0092] The review department can estimate the advertiser's emotions and adjust the review criteria based on those estimated emotions. For example, if the advertiser is stressed, the review department may relax the review criteria and conduct a quick review. If the advertiser is relaxed, the review department may conduct a review using the normal criteria. If the advertiser is in a hurry, the review department may simplify the review criteria and conduct a quick review. This allows for improved advertiser satisfaction by adjusting the review criteria based on the advertiser's emotions. Emotion estimation is achieved using emotion estimation functions, 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 review department may be performed using AI or not. For example, the review department can input advertiser emotion data into a generative AI and have the generative AI adjust the review criteria.

[0093] The review department can improve the accuracy of its review process by considering the interrelationships between advertisements during the review of advertising content. For example, the review department can compare multiple pieces of advertising content from the same advertiser to verify consistency. The review department can also compare advertising content within the same category to evaluate quality. The review department can also analyze the interrelationships between advertising content and apply the most appropriate review criteria. This improves the accuracy of the review process by considering the interrelationships between advertisements. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input data on the interrelationships of advertising content into a generating AI and have the generating AI perform the task of improving the accuracy of the review process.

[0094] The review department can consider the advertiser's attribute information when reviewing advertising content. For example, the review department can apply appropriate review criteria based on the advertiser's industry. The review department can also adjust the review criteria by referring to the advertiser's past review history. The review department can also set review criteria based on the advertiser's target audience. This improves the accuracy of the review by considering the advertiser's attribute information. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input advertiser attribute information data into a generating AI and have the generating AI set the review criteria.

[0095] The review unit can estimate the advertiser's emotions and adjust the order in which the review results are displayed based on the estimated emotions. For example, if the advertiser is stressed, the review unit can display the results quickly. If the advertiser is relaxed, the review unit can also display the results in the normal order. If the advertiser is in a hurry, the review unit can also prioritize displaying the results. This allows advertiser satisfaction to be improved by adjusting the order in which the review results are displayed based on the advertiser'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 review unit may be performed using AI or not. For example, the review unit can input advertiser emotion data into a generative AI and have the generative AI adjust the order in which the review results are displayed.

[0096] The review department can consider the geographical distribution of advertisements when reviewing advertising content. For example, the review department can apply appropriate review criteria based on the target area of ​​the advertisement. The review department can also analyze the geographical distribution of advertisements and set optimal review criteria. The review department can also adjust the review criteria considering the regional characteristics of advertisements. This improves the accuracy of the review process by considering the geographical distribution of advertisements. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input geographical distribution data of advertisements into a generating AI and have the generating AI set the review criteria.

[0097] The review department can improve the accuracy of its review process by referring to relevant literature on advertisements during the review of advertising content. For example, the review department can set review criteria based on relevant literature on advertisements. The review department can also improve the accuracy of its review by referring to relevant literature on advertisements. The review department can also analyze relevant literature on advertisements and apply the most appropriate review criteria. This allows the review department to improve the accuracy of its review by referring to relevant literature on advertisements. Some or all of the above processes in the review department may be performed using AI or not. For example, the review department can input relevant literature data on advertisements into a generating AI and have the generating AI set the review criteria.

[0098] The operations department can estimate the advertiser's emotions and adjust the advertising strategy based on those emotions. For example, if the advertiser is relaxed, the operations department will apply the normal advertising strategy. If the advertiser is in a hurry, the operations department can also quickly start the advertising strategy. If the advertiser is feeling anxious, the operations department can also provide a detailed operational report. By adjusting the advertising strategy based on the advertiser's emotions, advertiser satisfaction can be improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department can input advertiser emotion data into a generative AI and have the generative AI adjust the advertising strategy.

[0099] The operations department can select the optimal operation method by referring to past advertising performance data when running advertisements. For example, the operations department can select the optimal timing for ad delivery based on past advertising performance data. The operations department can also analyze past advertising performance data and select the optimal targeting settings. The operations department can also refer to past advertising performance data to select the optimal ad creative. In this way, by referring to past advertising performance data, the optimal operation method can be selected and the effectiveness of the advertisements can be maximized. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department can input past advertising performance data into a generating AI and have the generating AI select the optimal operation method.

[0100] The operations department can customize the methods of advertising operations based on the current market conditions of the advertisement. For example, the operations department can analyze current market trends and propose the optimal advertising operations. The operations department can also customize the methods of advertising operations considering the market conditions of the advertiser's industry. The operations department can also adjust the methods of advertising operations based on the actions of the advertiser's competitors. By customizing the methods of advertising operations based on the current market conditions of the advertisement, the effectiveness of the advertisement can be maximized. Some or all of the above processes in the operations department may be performed using AI or not. For example, the operations department can input current market data into a generating AI and have the generating AI perform the customization of the methods of advertising operations.

[0101] The operations department can estimate the advertiser's emotions and determine the priority of advertising operations based on those estimated emotions. For example, if the advertiser feels urgent, the operations department will prioritize advertising operations. If the advertiser is relaxed, the operations department can also prioritize advertising operations with normal priorities. If the advertiser is anxious, the operations department can prioritize advertising operations to respond quickly. By determining the priority of advertising operations based on the advertiser's emotions, advertiser satisfaction can be improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the operations department may be performed using AI or not. For example, the operations department can input advertiser emotion data into a generative AI and have the generative AI determine the priority of advertising operations.

[0102] The operations department can select the optimal operation method when running advertisements, taking into account the geographical location information of the advertisements. For example, the operations department can select the optimal advertising operation method based on the target area of ​​the advertisement. The operations department can also select the optimal timing for ad delivery based on the geographical location information of the advertisements. The operations department can also customize the operation method, taking into account the regional characteristics of the advertisements. In this way, by considering the geographical location information of the advertisements, the optimal operation method can be selected and the effectiveness of the advertisements can be maximized. Some or all of the above processes performed by the operations department may be performed using AI or not. For example, the operations department can input the geographical location information data of the advertisements into a generating AI and have the generating AI perform the selection of the optimal operation method.

[0103] The operations department can analyze the social media activity of an advertisement and propose operational methods during the advertising campaign. For example, the operations department can propose the optimal advertising operational methods based on the advertiser's social media activity. The operations department can also analyze the reactions of the advertiser's followers and propose the optimal advertising operational methods. The operations department can also propose operational methods considering the trends on the advertiser's social media. In this way, by analyzing the social media activity of an advertisement, the optimal operational methods can be proposed, maximizing the effectiveness of the advertisement. Some or all of the above processes performed by the operations department may be carried out using AI or not. For example, the operations department can input the advertiser's social media activity data into a generating AI and have the generating AI execute the proposal of operational methods.

[0104] The analysis unit can estimate the advertiser's emotions and select analysis data based on the estimated emotions. For example, if the advertiser is relaxed, the analysis unit can provide detailed analysis data. If the advertiser is in a hurry, the analysis unit can also provide concise analysis data that gets straight to the point. If the advertiser is feeling anxious, the analysis unit can also provide detailed analysis data to provide reassurance. In this way, by selecting analysis data based on the advertiser's emotions, advertiser satisfaction can be 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 analysis unit may be performed using AI or not. For example, the analysis unit can input the advertiser's emotion data into a generative AI and have the generative AI perform the selection of analysis data.

[0105] The analysis unit can optimize its analysis algorithm by referring to past analysis data when analyzing advertising performance data. For example, the analysis unit can select the optimal analysis algorithm based on past advertising performance data. The analysis unit can also analyze past analysis data and optimize the analysis algorithm. The analysis unit can also adjust the analysis algorithm by referring to past advertising performance data. This allows the analysis algorithm to be optimized by referring to past analysis data, thereby improving the accuracy of the analysis. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0106] The analysis unit can apply different analysis methods to each advertising category when analyzing advertising performance data. For example, in the case of product advertisements, the analysis unit can apply an analysis method based on product sales data. In the case of service advertisements, the analysis unit can also apply an analysis method based on service usage data. In the case of brand advertisements, the analysis unit can also apply an analysis method based on brand awareness data. By applying different analysis methods to each advertising category, the accuracy of the analysis can be improved. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input advertising category data into a generating AI and have the generating AI execute the application of the analysis method.

[0107] The analysis unit can estimate the advertiser's emotions and adjust the analysis frequency based on the estimated emotions. For example, if the advertiser is relaxed, the analysis unit will provide data at a normal analysis frequency. If the advertiser is in a hurry, the analysis unit can increase the analysis frequency and provide data. If the advertiser is feeling anxious, the analysis unit can increase the analysis frequency and provide data to provide reassurance. In this way, adjusting the analysis frequency based on the advertiser's emotions can improve advertiser satisfaction. 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 analysis unit may be performed using AI or not. For example, the analysis unit can input advertiser emotion data into a generative AI and have the generative AI adjust the analysis frequency.

[0108] The analysis unit can weight the analysis data based on the timing of ad submissions when analyzing ad performance data. For example, the analysis unit can increase the weighting of the analysis data in the case of urgent ad submissions. For normal ad submissions, the analysis unit can also provide analysis data with normal weighting. For long-term campaign ads, the analysis unit can also adjust the weighting systematically. This improves the accuracy of the analysis by weighting the analysis data based on the timing of ad submissions. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input ad submission timing data into a generating AI and have the generating AI perform the weighting of the analysis data.

[0109] The analysis unit can perform analysis by referring to relevant market data for advertising when analyzing advertising performance data. For example, the analysis unit applies an analysis method based on the relevant market data for advertising. The analysis unit can also improve the accuracy of the analysis by referring to the relevant market data for advertising. The analysis unit can also analyze the relevant market data for advertising and apply the most suitable analysis method. This allows the accuracy of the analysis to be improved by referring to the relevant market data for advertising. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input relevant market data for advertising into a generating AI and have the generating AI execute the application of the analysis method.

[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 automated ad submission system can further estimate the advertiser's emotions and adjust the method of generating ad content based on those emotions. For example, if the advertiser is stressed, the generation unit can generate simple and intuitive ad content. If the advertiser is relaxed, it can also generate ad content that includes detailed information. If the advertiser is excited, it can also generate visually stimulating ad content. This allows advertiser satisfaction to be improved by adjusting the method of generating ad content based on the advertiser's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the advertiser's emotion data into the generation AI and have the generation AI adjust the method of generating ad content.

[0112] The automated ad submission system can further reference the advertiser's past ad performance data to generate optimal ad content. For example, it can generate ad content that incorporates elements from ads that have recorded high click-through rates in the past. It can also incorporate elements from ads with high conversion rates in the past. It can also incorporate elements from ads with a large number of impressions in the past. In this way, by referencing the advertiser's past ad performance data, it is possible to generate optimal ad content and maximize the effectiveness of the ads. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the advertiser's past ad performance data into a generation AI and have the generation AI execute the generation of optimal ad content.

[0113] The automated ad submission system can further estimate the advertiser's emotions and adjust the ad management method based on those emotions. For example, if the advertiser is relaxed, the standard ad management method can be applied. If the advertiser is in a hurry, the ad management can be started quickly. If the advertiser is feeling anxious, a detailed management report can be provided. This allows advertiser satisfaction to be improved by adjusting the ad management method based on the advertiser'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 operations department may be performed using AI or not. For example, the operations department can input advertiser emotion data into a generative AI and have the generative AI adjust the ad management method.

[0114] The automated ad submission system can further generate ad content while considering the advertiser's geographical location. For example, it can generate ad content that incorporates region-specific elements based on the advertiser's location. It can also select the optimal ad delivery timing based on the advertiser's geographical location. It can also generate ad content while considering market trends related to the advertiser's location. In this way, by considering the advertiser's geographical location, it is possible to generate optimal ad content and maximize the effectiveness of the ad. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the advertiser's geographical location data into a generation AI and have the generation AI execute the generation of optimal ad content.

[0115] The automated ad submission system can further estimate the advertiser's emotions and adjust the presentation of the ad content based on those emotions. For example, if the advertiser is relaxed, it can generate ad content with a soft tone. If the advertiser is in a hurry, it can generate concise ad content that gets straight to the point. If the advertiser is excited, it can generate visually stimulating ad content. This allows advertiser satisfaction to be improved by adjusting the presentation of the ad content based on the advertiser'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 generation unit may be performed using AI or not. For example, the generation unit can input advertiser emotion data into the generative AI and have the generative AI adjust the presentation of the ad content.

[0116] The automated ad submission system can further analyze the advertiser's past requirement submission history and select the optimal submission method. For example, it can automatically display requirements that the advertiser has frequently submitted in the past as candidates. It can also prioritize suggesting input methods (voice, text, etc.) that the advertiser has used in the past. It can also predict and suggest requirements to be used during specific time periods based on the advertiser's past requirement submission history. In this way, by analyzing the advertiser's past requirement submission history, the optimal submission method can be selected, enabling efficient requirement reception. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's past requirement submission history data into a generating AI and have the generating AI select the optimal submission method.

[0117] The automated ad submission system can further estimate the advertiser's emotions and prioritize the requirements to be accepted based on those emotions. For example, if the advertiser feels urgent, the requirements will be accepted with the highest priority. If the advertiser is relaxed, the requirements can be accepted with the normal priority. If the advertiser is anxious, the requirements can be accepted with priority to ensure a quick response. This improves advertiser satisfaction by prioritizing requirements based on the advertiser'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 reception area may be performed using AI or not. For example, the reception area can input advertiser emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The automated ad submission system can further analyze the advertiser's social media activity and accept relevant requirements. For example, it can suggest optimal ad requirements based on the advertiser's social media activity. It can also analyze the reactions of the advertiser's followers and prioritize accepting highly relevant requirements. It can also accept requirements while considering the advertiser's social media trends. In this way, by analyzing the advertiser's social media activity, it can accept relevant requirements. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the advertiser's social media activity data into a generating AI and have the generating AI select relevant requirements.

[0119] The automated ad submission system can further estimate the advertiser's emotions and adjust the review criteria based on those emotions. For example, if the advertiser is stressed, the review criteria can be relaxed and the review process can be expedited. If the advertiser is relaxed, the review can be conducted using the normal criteria. If the advertiser is in a hurry, the review criteria can be simplified and the review process can be expedited. This allows for improved advertiser satisfaction by adjusting the review criteria based on the advertiser'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 review department may be performed using AI or not. For example, the review department can input advertiser emotion data into a generative AI and have the generative AI adjust the review criteria.

[0120] The automated ad submission system can further filter based on the advertiser's current business situation and market trends. For example, it can suggest optimal advertising requirements based on the advertiser's current sales data. It can also analyze market trends and filter the requirements that are best suited to the advertiser's business. It can also suggest optimal advertising requirements by considering the actions of the advertiser's competitors. In this way, by filtering based on the advertiser's current business situation and market trends, it can suggest the most suitable advertising requirements. Some or all of the above processing at the reception desk may be performed using AI or not. For example, the reception desk can input advertiser business situation data and market trend data into a generating AI and have the generating AI perform the filtering of optimal advertising requirements.

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

[0122] Step 1: The reception department receives the advertiser's requirements. These requirements include text format, image format, video format, etc. For example, if an advertiser enters "I want to create a promotional ad for a new product," the information is passed to the generation department based on that requirement. Step 2: The generation unit generates advertising content based on the requirements received by the reception unit. The generation unit uses generation AI to generate optimal advertising content based on the advertiser's requirements and target audience. Step 3: The review department reviews the advertising content generated by the generation department. The review department checks whether the advertising content complies with the guidelines and regulations of each platform and makes corrections as necessary. Step 4: The operations department automates ad operations based on the ad content reviewed by the review department. The operations department analyzes the ad click-through rate and conversion rate and automatically adjusts the optimal ad delivery timing and targeting settings. Step 5: The analytics department analyzes advertising performance data in real time and provides feedback to the operations department. The analytics department analyzes performance data such as click-through rates, conversion rates, and impressions in real time.

[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 reception unit, generation unit, review unit, operation unit, and analysis unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the advertiser's requirements. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates advertising content using generation AI. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reviews the generated advertising content. The operation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates advertising operations. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes advertising performance data in real time. 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.

[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 reception unit, generation unit, review unit, operation unit, and analysis unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the advertiser's requirements. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates advertising content using generation AI. The review unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and reviews the generated advertising content. The operation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and automates advertising operations. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes advertising performance data in real time. 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.

[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 reception unit, generation unit, review unit, operation unit, and analysis unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the advertiser's requirements. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates advertising content using generation AI. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reviews the generated advertising content. The operation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates advertising operations. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes advertising performance data in real time. 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.

[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 reception unit, generation unit, review unit, operation unit, and analysis unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the advertiser's requirements. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates advertising content using generation AI. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and reviews the generated advertising content. The operation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates advertising operations. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes advertising performance data in real time. 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 reception desk that handles the requirements of advertisers, A generation unit that generates advertising content based on the requirements received by the reception unit, A review unit that reviews the advertising content generated by the generation unit, An operations unit that automates advertising operations based on advertising content reviewed by the aforementioned review unit, It includes an analysis unit that analyzes advertising performance data. A system characterized by the following features. (Note 2) The generating unit is Generate optimal ad content based on advertiser requirements and target audience. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned review department, We check whether the advertising content complies with the guidelines and regulations of each platform and make corrections as necessary. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned operations unit, It analyzes ad click-through rates and conversion rates, and automatically adjusts the optimal ad delivery timing and targeting settings. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze advertising performance data in real time and provide feedback to the operations department. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When an advertiser enters "I want to create a promotional ad for a new product," the information is passed to the generation unit based on that requirement. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the advertiser's sentiment and adjust how we accept their requirements based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze the advertiser's past requirement submission history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving requirements, filtering is performed based on the advertiser's current business situation and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is Estimate the advertiser's sentiment and prioritize the requirements to be accepted based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving requirements, we prioritize accepting highly relevant requirements by considering the advertiser's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving requirements, we analyze the advertiser's social media activity and accept relevant requirements. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the advertiser's emotions and adjust the way the ad content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating ad content, adjust the level of detail based on the importance of the ad. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating ad content, different generation algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the advertiser's emotions and adjusts the length of the ad content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating ad content, the generation priority is determined based on the ad submission date. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating ad content, adjust the generation order based on ad relevance. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned review department, We estimate the advertiser's sentiment and adjust the review criteria based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned review department, When reviewing advertising content, we will improve the accuracy of the review process by considering the interrelationships between advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned review department, When reviewing advertising content, the advertiser's demographic information will be taken into consideration during the review process. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned review department, We estimate the advertiser's sentiment and adjust the order in which the review results are displayed based on the estimated advertiser's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned review department, When reviewing advertising content, the geographical distribution of the advertisement will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned review department, When reviewing advertising content, we improve the accuracy of the review process by referring to relevant literature related to the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned operations unit, We estimate the advertiser's sentiment and adjust the advertising strategy based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned operations unit, When managing advertising campaigns, the optimal management method is selected by referring to past advertising performance data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned operations unit, When managing advertising campaigns, customize the methods of operation based on the current market conditions for the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned operations unit, We estimate the advertiser's sentiment and determine the priority of ad operations based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned operations unit, When managing advertising campaigns, the optimal management method is selected by considering the geographical location information of the advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned operations unit, When managing advertising campaigns, we analyze the social media activity of the ads and propose operational strategies. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, The system estimates the advertiser's emotions and selects analysis data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit, When analyzing advertising performance data, we optimize the analysis algorithm by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit, When analyzing ad performance data, different analytical methods are applied to each ad category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned analysis unit, We estimate the advertiser's sentiment and adjust the frequency of analysis based on the estimated advertiser sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned analysis unit, When analyzing ad performance data, the data is weighted based on when the ad was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned analysis unit, When analyzing advertising performance data, the analysis is performed by referencing relevant market data for the advertisement. 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 reception desk that handles the requirements of advertisers, A generation unit that generates advertising content based on the requirements received by the reception unit, A review unit that reviews the advertising content generated by the generation unit, An operations unit that automates advertising operations based on advertising content reviewed by the aforementioned review unit, It includes an analysis unit that analyzes advertising performance data. A system characterized by the following features.

2. The generating unit is Generate optimal ad content based on advertiser requirements and target audience. The system according to feature 1.

3. The aforementioned review department, We check whether the advertising content complies with the guidelines and regulations of each platform and make corrections as necessary. The system according to feature 1.

4. The aforementioned operations unit, It analyzes ad click-through rates and conversion rates, and automatically adjusts the optimal ad delivery timing and targeting settings. The system according to feature 1.

5. The aforementioned analysis unit, The system analyzes advertising performance data in real time and provides feedback to the operations department. The system according to feature 1.

6. The aforementioned reception unit is We estimate the advertiser's sentiment and adjust how we accept their requirements based on that estimated sentiment. The system according to feature 1.

7. The aforementioned reception unit is We analyze the advertiser's past requirement submission history and select the most suitable submission method. The system according to feature 1.

Images