system

Abstract

The system according to this embodiment aims to automatically generate advertising banner designs and quickly identify the optimal design. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, an analysis unit, and a selection unit. The collection unit receives input from the user regarding the campaign objective and target audience. The generation unit automatically generates multiple banner designs based on the information received by the collection unit. The analysis unit conducts A / B testing on the banner designs generated by the generation unit and analyzes their performance in real time. The selection unit automatically selects the optimal design based on the results obtained by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it takes a lot of time and effort to optimize the design of an advertising banner, and it is difficult to quickly identify an effective design.

[0005] The system according to the embodiment aims to automatically generate the design of an advertising banner and quickly identify an optimal design. <0000​​​​​The system according to this embodiment comprises a data collection unit, a generation unit, an analysis unit, and a selection unit. The data collection unit receives input from the user regarding the campaign objective and target audience. The generation unit automatically generates multiple banner designs based on the information received by the data collection unit. The analysis unit conducts A / B testing on the banner designs generated by the generation unit and analyzes their performance in real time. The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can automatically generate advertising banner designs and quickly identify the optimal design. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards 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 digital marketing advertising banner creation system according to an embodiment of the present invention is a system that maximizes the ROI of an advertising campaign by quickly identifying the most effective design through A / B testing, after the user inputs the campaign objective and target audience, and the AI ​​automatically generates multiple banner designs. For example, the digital marketing advertising banner creation system allows the user to input the campaign objective and target audience, and the AI ​​automatically generates multiple banner designs, and the AI ​​conducts A / B testing to quickly identify the most effective design and maximize the ROI of an advertising campaign. For example, the digital marketing advertising banner creation system allows the user to input the campaign objective and target audience. For example, the user inputs the objective of the advertising campaign and the target customer group in detail. For example, information such as "promotion of a new product" or "advertising for young people" is input. This information is input to the AI. Next, the digital marketing advertising banner creation system allows the AI ​​to analyze the input information and automatically generate multiple banner designs. The AI ​​learns from past success stories and combinations of design elements to generate the optimal variations. For example, multiple banner designs combining different colors, layouts, and catchphrases are generated. The generated banners are subjected to A / B testing, and their performance is analyzed in real time. The digital marketing ad banner creation system analyzes the click-through rate (CTR) and conversion rate of each banner to identify the most effective design. For example, it tests which banner achieves a higher CTR by displaying banner A and banner B to different user groups. The optimal design is automatically selected and continuously improved. Based on the results of A / B testing, the digital marketing ad banner creation system identifies areas for design improvement and incorporates them into the next banner generation. This maximizes the ROI of the advertising campaign. For example, a new banner incorporating design elements with a high CTR is generated. This system reduces banner creation time by up to 70% and shortens A / B testing periods by 50%. It also improves CTR by an average of 30% and conversion rates by 20%. As a result, the effectiveness of the advertising campaign is significantly improved, and the efficiency of the creative team is increased.This allows the digital marketing ad banner creation system to maximize the ROI of advertising campaigns.

[0029] The digital marketing advertising banner creation system according to this embodiment comprises a collection unit, a generation unit, an analysis unit, and a selection unit. The collection unit receives input from the user regarding the campaign objective and target audience. For example, the collection unit receives information such as the campaign objective and target audience entered by the user. For example, the collection unit accepts information such as "promotion of a new product" or "advertising for young people" from the user. The generation unit automatically generates multiple banner designs based on the information received by the collection unit. For example, the generation unit learns from past success stories and combinations of design elements to generate the optimal variation. For example, the generation unit generates multiple banner designs combining different colors, layouts, and catchphrases. The analysis unit conducts A / B testing on the banner designs generated by the generation unit and analyzes their performance in real time. For example, the analysis unit analyzes the click-through rate and conversion rate of each banner to identify the most effective design. For example, the analysis unit displays banner A and banner B to different user groups and tests which one achieves a higher click-through rate. The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. The selection unit, for example, identifies areas for design improvement based on the results of A / B testing and incorporates these improvements into the next banner generation. This allows the digital marketing advertising banner creation system according to this embodiment to maximize the ROI of advertising campaigns.

[0030] The data collection unit accepts user input regarding campaign objectives and target audience. Specifically, the data collection unit provides an interface that accepts detailed information on campaign objectives and target audience entered by users. For example, users can input campaign objectives such as "promoting a new product" or "increasing brand awareness." They can also input detailed attribute information for the target audience, such as age, gender, region, and interests. The data collection unit efficiently collects this information and stores it in a database. Furthermore, the data collection unit also collects the history and performance data of campaigns previously run by the user, and this data can be used to design future campaigns. The data collection unit can analyze the information entered by the user in real time and display prompts for additional information as needed. For example, if the target audience information is insufficient, the data collection unit will prompt the user to input specific attribute information. This allows the data collection unit to collect detailed information tailored to the user's needs and provide it as input data to the next step, the generation unit.

[0031] The generation unit automatically generates multiple banner designs based on the information received by the collection unit. Specifically, the generation unit uses an AI model that has learned from past success stories and combinations of design elements to generate the optimal variations. For example, the generation unit generates multiple banner designs by combining different color palettes, fonts, layouts, taglines, and image materials. The generation unit considers the campaign objectives and target audience information entered by the user and selects the design elements that will resonate most with the target audience. For example, for advertisements targeting young people, bright and pop colors and casual fonts are used, while for advertisements targeting seniors, calm colors and easy-to-read fonts are used. The generation unit combines these design elements to automatically generate dozens to hundreds of banner designs and provides them to the analysis unit. The generation unit also has an algorithm that checks the consistency and visual balance of the designs to ensure the quality of the generated banner designs. This allows the generation unit to efficiently generate high-quality banner designs that meet the user's needs.

[0032] The analytics department conducts A / B testing on banner designs generated by the generation department and analyzes their performance in real time. Specifically, the analytics department monitors performance metrics such as click-through rates, conversion rates, and engagement rates for each banner design in real time. For example, the analytics department displays banner A and banner B to different user groups and tests which one achieves a higher click-through rate. The analytics department uses AI to analyze the collected data and quantitatively evaluate the effectiveness of each banner design. Furthermore, the analytics department identifies areas for improvement in banner designs based on user behavior data and feedback. For example, it analyzes the impact of specific colors or taglines on click-through rates and extracts the optimal design elements. Based on these analysis results, the analytics department provides feedback to the generation department to be reflected in the next banner generation. This allows the analytics department to continuously improve the performance of banner designs and maximize the effectiveness of advertising campaigns.

[0033] The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. Specifically, the selection unit identifies and automatically selects the banner design that showed the highest performance based on the results of A / B testing. For example, the selection unit selects the design with the highest click-through rate or conversion rate and uses it for the next advertising campaign. Furthermore, the selection unit identifies areas for design improvement based on feedback provided by the analysis unit and reflects these improvements in the next banner generation. For example, if certain colors or layouts are found to be effective, the selection unit incorporates these elements into the next banner design. This allows the selection unit to provide the optimal design to maximize the ROI of the advertising campaign. In addition, the selection unit collects user feedback and continuously improves the accuracy of the selection process. For example, based on user feedback, it adjusts the parameters of the selection algorithm to perform more accurate design selection. This allows the selection unit to maximize the effectiveness of the advertising campaign and improve user satisfaction.

[0034] The generation unit can learn from past success stories and combinations of design elements to generate optimal variations. For example, it can analyze past success stories and generate banner designs incorporating the most effective design elements. The generation unit can also generate detailed designs based on past success stories. For example, it can adjust the level of detail of the design by referring to past success stories to generate the optimal banner design. In this way, by learning from past success stories, it can generate more effective banner designs.

[0035] The analytics unit can analyze the click-through rate and conversion rate of each banner to identify the most effective design. For example, the analytics unit can analyze the click-through rate of each banner to identify the most effective design. The analytics unit can also analyze the conversion rate of each banner to identify the most effective design. Furthermore, the analytics unit can analyze both the click-through rate and conversion rate to identify the most effective design. This allows for the identification of the most effective banner design by analyzing click-through rates and conversion rates.

[0036] The selection unit can identify areas for design improvement based on the results of A / B testing and reflect them in the next banner generation. For example, the selection unit analyzes the results of A / B testing and identifies areas for design improvement. The selection unit can also identify areas for design improvement based on the results of A / B testing and reflect them in the next banner generation. This allows for continuous improvement of banner design effectiveness by incorporating A / B test results.

[0037] The data collection unit can receive information about campaign objectives and target audiences entered by users. For example, the data collection unit can receive information about campaign objectives and target audiences entered by users. For example, the data collection unit can receive information such as "new product promotion" or "advertising targeting young people." The data collection unit can also receive information about campaign objectives and target audiences based on information entered by users. This allows for the accurate reception of user-entered information, enabling the generation of appropriate banner designs.

[0038] The generation unit can generate multiple banner designs by combining different colors, layouts, and taglines. For example, it can generate banner designs combining different colors. It can also generate banner designs combining different layouts. It can also generate banner designs combining different taglines. This allows for the generation of diverse banner designs by combining different elements.

[0039] The data collection unit can analyze a user's past campaign history and suggest the optimal input method. For example, the data collection unit can automatically display campaign objectives and target audiences that the user has frequently entered in the past as suggestions. The data collection unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the data collection unit can predict and suggest campaign objectives and target audiences to use at specific times of day based on the user's past campaign history. In this way, by analyzing past history, the system can suggest the most suitable input method for the user.

[0040] The data collection unit can filter campaign objectives and target audiences based on the user's current marketing strategy and market trends. For example, it can prioritize displaying highly relevant campaign objectives and target audiences based on the user's current marketing strategy. The data collection unit can also analyze market trends and suggest campaign objectives and target audiences that align with those trends. For example, it can suggest optimal campaign objectives and target audiences by referencing the activities of the user's competitors. This allows for more effective campaigns by filtering based on current marketing strategies and market trends.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when they input campaign objectives and target audience. For example, the data collection unit can suggest region-specific campaign objectives and target audiences based on the user's geographical location. For example, the data collection unit can analyze regional market trends based on the user's location and suggest optimal campaign objectives and target audiences. For example, the data collection unit can suggest campaign objectives and target audiences that take into account the trends of regional competitors, considering the user's geographical location. In this way, by considering geographical location, region-specific information can be collected preferentially.

[0042] The data collection unit analyzes users' social media activity and collects relevant information when campaign objectives and target audiences are entered. For example, the data collection unit analyzes users' social media activity and suggests relevant campaign objectives and target audiences. The data collection unit can also suggest optimal campaign objectives and target audiences by referring to the activities of users' followers and friends. For example, the data collection unit can analyze users' interests and preferences on social media and suggest campaign objectives and target audiences based on that. In this way, information relevant to users can be collected by analyzing their social media activity.

[0043] The generation unit can adjust the level of detail in banner designs based on past success stories. For example, the generation unit can analyze past success stories and generate banner designs that incorporate the most effective design elements. The generation unit can also generate detailed designs based on past success stories. For example, the generation unit can adjust the level of detail in designs by referring to past success stories to generate the optimal banner design. This allows for the generation of more effective banner designs by adjusting the level of detail based on past success stories.

[0044] The generation unit can apply different design algorithms to banner designs depending on the attributes of the target audience. For example, the generation unit can apply different design algorithms depending on the age group of the target audience. The generation unit can also apply different design algorithms depending on the gender of the target audience. The generation unit can also apply different design algorithms depending on the interests and concerns of the target audience. By applying different design algorithms according to the attributes of the target audience, more effective banner designs can be generated.

[0045] The generation unit can prioritize banner designs based on the campaign submission deadline. For example, it can prioritize generating the most effective design based on the campaign submission deadline. If the submission deadline is approaching, it can also prioritize designs that can be generated quickly. If there is ample time before the submission deadline, it can also prioritize generating detailed designs. By prioritizing designs based on the campaign submission deadline, designs can be generated at the optimal time.

[0046] The generation unit can adjust the order of banner designs based on the relevance of the target audience during the banner design generation process. For example, the generation unit can prioritize generating the most relevant designs based on the target audience's interests and preferences. The generation unit can also adjust the order of designs based on the target audience's attributes. For example, the generation unit can determine the optimal order of designs based on the target audience's behavioral history. By adjusting the order of designs based on the relevance of the target audience, more effective banner designs can be generated.

[0047] The analysis unit can improve the accuracy of its analysis during A / B testing by considering the interrelationships between banner designs. For example, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between the colors and layout of the banner designs. The analysis unit can also improve the accuracy of its analysis by considering the interrelationships between the catchphrases of the banner designs. The analysis unit can also improve the accuracy of its analysis by considering the interrelationships between the images and fonts of the banner designs. In this way, the accuracy of the analysis is improved by considering the interrelationships between the banner designs.

[0048] The analysis unit can perform A / B testing while considering the attribute information of the target group. For example, the analysis unit can perform A / B testing analysis according to the age group of the target group. For example, the analysis unit can also perform A / B testing analysis according to the gender of the target group. For example, the analysis unit can also perform A / B testing analysis according to the interests and preferences of the target group. This allows for more effective analysis by considering the attribute information of the target group.

[0049] The analysis unit can perform analysis during A / B testing while considering the geographical distribution of banner designs. For example, the analysis unit can analyze the performance of banner designs in different regions and identify the optimal design. The analysis unit can also analyze the effectiveness of banner designs based on geographical distribution. Furthermore, the analysis unit can analyze banner designs while considering regional market trends. This allows for the identification of the optimal banner design for each region by considering geographical distribution.

[0050] The analysis unit can improve the accuracy of its analysis during A / B testing by referring to relevant literature on banner design. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on banner design. The analysis unit can also analyze the effectiveness of banner design based on data from relevant literature. For example, the analysis unit can identify areas for improvement in banner design by utilizing data from relevant literature. Thus, referring to relevant literature improves the accuracy of the analysis.

[0051] The selection unit can optimize its selection algorithm by referring to past A / B test results when selecting the optimal design. For example, the selection unit analyzes past A / B test results and optimizes the algorithm for selecting the optimal design. The selection unit can also adjust the design selection criteria based on past A / B test results. Furthermore, the selection unit can improve the method for selecting the optimal design by referring to past A / B test results. This allows for the optimization of the selection algorithm by referring to past A / B test results.

[0052] The selection function can consider the target audience's attribute information when selecting the optimal design. For example, the selection function can select the optimal design according to the target audience's age group. For example, the selection function can also select the optimal design according to the target audience's gender. For example, the selection function can also select the optimal design according to the target audience's interests and preferences. This allows for the selection of a more effective design by considering the target audience's attribute information.

[0053] The selection function can consider the geographical distribution of banner designs when selecting the optimal design. For example, the selection function can select the optimal design by considering the performance of banner designs in each region. The selection function can also select the optimal design based on geographical distribution. For example, the selection function can also select the optimal design by considering market trends in each region. This allows for the selection of the optimal design for each region by considering geographical distribution.

[0054] The selection unit can improve the accuracy of its selection by referring to relevant literature on banner design when selecting the optimal design. For example, the selection unit can refer to relevant literature on banner design to improve the accuracy of its selection. The selection unit can also select the optimal design based on data from relevant literature. For example, the selection unit can improve the method of selecting the optimal design by utilizing data from relevant literature. This improves the accuracy of the selection by referring to relevant literature.

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

[0056] The data collection unit can analyze a user's past behavior history and automatically suggest the most suitable campaign objectives and target audiences. For example, it can suggest similar campaign objectives based on the success rate of campaigns the user has previously run. It can also analyze the responses of customer segments the user has previously targeted and suggest the most effective target audiences. Furthermore, it can suggest optimal design elements by referencing the design elements of advertising banners the user has used in the past. This allows for the suggestion of more effective campaign objectives and target audiences by leveraging the user's past behavior history.

[0057] The analysis unit can implement algorithms that predict the effectiveness of banner designs based on A / B test results. For example, the analysis unit can use an algorithm that learns from past A / B test results to predict the results of the next test. The analysis unit can also predict the effects of different design element combinations and identify the most effective combination. For example, the analysis unit can predict what effect a particular design will have based on the attribute information of the target audience. This makes it possible to select banner designs more efficiently by predicting effects based on A / B test results.

[0058] The data collection unit can analyze users' social media activity and propose optimal campaign objectives and target audiences. For example, it can analyze users' social media posts and reactions to suggest relevant campaign objectives. It can also propose optimal target audiences based on the attributes of users' followers and friends. Furthermore, it can analyze users' interests and preferences on social media and propose campaign objectives and target audiences based on that analysis. In this way, by analyzing social media activity, it is possible to collect information relevant to users and propose optimal campaign objectives and target audiences.

[0059] The analytics unit can be equipped with a function to provide real-time feedback on the effectiveness of banner designs based on the results of A / B tests. For example, the analytics unit can display the click-through rate and conversion rate of each banner in real time, allowing users to immediately see the effects. The analytics unit can also instantly reflect changes to banner designs and analyze their effects in real time. For example, the analytics unit can provide an interface that allows users to adjust banner designs while checking the effects in real time. This allows for the rapid identification of effective banner designs through real-time feedback.

[0060] The generation unit can adjust banner designs to take into account the cultural background of the target audience. For example, if the target audience belongs to a different cultural sphere, the generation unit will generate banner designs using colors and symbols appropriate to that culture. The generation unit can also generate banner designs that include appropriate taglines and messages based on the cultural background of the target audience. The generation unit can also adjust design details to take into account the cultural customs and values ​​of the target audience. This allows for the generation of more effective banner designs by considering the cultural background of the target audience.

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

[0062] Step 1: The data collection unit accepts input from users regarding the campaign objective and target audience. For example, it accepts information such as "promotion of a new product" or "advertising targeting young people." Step 2: The generation unit automatically generates multiple banner designs based on the information received by the collection unit. For example, it learns from past success stories and combinations of design elements to generate multiple banner designs with different colors, layouts, and taglines. Step 3: The analysis unit conducts A / B testing on the banner designs generated by the generation unit and analyzes their performance in real time. For example, it analyzes the click-through rate and conversion rate of each banner to identify the most effective design. Step 4: The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. For example, based on the results of A / B testing, it identifies areas for design improvement and incorporates them into the next banner generation.

[0063] (Example of form 2) The digital marketing advertising banner creation system according to an embodiment of the present invention is a system that maximizes the ROI of an advertising campaign by quickly identifying the most effective design through A / B testing, after the user inputs the campaign objective and target audience, and the AI ​​automatically generates multiple banner designs. For example, the digital marketing advertising banner creation system allows the user to input the campaign objective and target audience, and the AI ​​automatically generates multiple banner designs, and the AI ​​conducts A / B testing to quickly identify the most effective design and maximize the ROI of an advertising campaign. For example, the digital marketing advertising banner creation system allows the user to input the campaign objective and target audience. For example, the user inputs the objective of the advertising campaign and the target customer group in detail. For example, information such as "promotion of a new product" or "advertising for young people" is input. This information is input to the AI. Next, the digital marketing advertising banner creation system allows the AI ​​to analyze the input information and automatically generate multiple banner designs. The AI ​​learns from past success stories and combinations of design elements to generate the optimal variations. For example, multiple banner designs combining different colors, layouts, and catchphrases are generated. The generated banners are subjected to A / B testing, and their performance is analyzed in real time. The digital marketing ad banner creation system analyzes the click-through rate (CTR) and conversion rate of each banner to identify the most effective design. For example, it tests which banner achieves a higher CTR by displaying banner A and banner B to different user groups. The optimal design is automatically selected and continuously improved. Based on the results of A / B testing, the digital marketing ad banner creation system identifies areas for design improvement and incorporates them into the next banner generation. This maximizes the ROI of the advertising campaign. For example, a new banner incorporating design elements with a high CTR is generated. This system reduces banner creation time by up to 70% and shortens A / B testing periods by 50%. It also improves CTR by an average of 30% and conversion rates by 20%. As a result, the effectiveness of the advertising campaign is significantly improved, and the efficiency of the creative team is increased.This allows the digital marketing ad banner creation system to maximize the ROI of advertising campaigns.

[0064] The digital marketing advertising banner creation system according to this embodiment comprises a collection unit, a generation unit, an analysis unit, and a selection unit. The collection unit receives input from the user regarding the campaign objective and target audience. For example, the collection unit receives information such as the campaign objective and target audience entered by the user. For example, the collection unit accepts information such as "promotion of a new product" or "advertising for young people" from the user. The generation unit automatically generates multiple banner designs based on the information received by the collection unit. For example, the generation unit learns from past success stories and combinations of design elements to generate the optimal variation. For example, the generation unit generates multiple banner designs combining different colors, layouts, and catchphrases. The analysis unit conducts A / B testing on the banner designs generated by the generation unit and analyzes their performance in real time. For example, the analysis unit analyzes the click-through rate and conversion rate of each banner to identify the most effective design. For example, the analysis unit displays banner A and banner B to different user groups and tests which one achieves a higher click-through rate. The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. The selection unit, for example, identifies areas for design improvement based on the results of A / B testing and incorporates these improvements into the next banner generation. This allows the digital marketing advertising banner creation system according to this embodiment to maximize the ROI of advertising campaigns.

[0065] The data collection unit accepts user input regarding campaign objectives and target audience. Specifically, the data collection unit provides an interface that accepts detailed information on campaign objectives and target audience entered by users. For example, users can input campaign objectives such as "promoting a new product" or "increasing brand awareness." They can also input detailed attribute information for the target audience, such as age, gender, region, and interests. The data collection unit efficiently collects this information and stores it in a database. Furthermore, the data collection unit also collects the history and performance data of campaigns previously run by the user, and this data can be used to design future campaigns. The data collection unit can analyze the information entered by the user in real time and display prompts for additional information as needed. For example, if the target audience information is insufficient, the data collection unit will prompt the user to input specific attribute information. This allows the data collection unit to collect detailed information tailored to the user's needs and provide it as input data to the next step, the generation unit.

[0066] The generation unit automatically generates multiple banner designs based on the information received by the collection unit. Specifically, the generation unit uses an AI model that has learned from past success stories and combinations of design elements to generate the optimal variations. For example, the generation unit generates multiple banner designs by combining different color palettes, fonts, layouts, taglines, and image materials. The generation unit considers the campaign objectives and target audience information entered by the user and selects the design elements that will resonate most with the target audience. For example, for advertisements targeting young people, bright and pop colors and casual fonts are used, while for advertisements targeting seniors, calm colors and easy-to-read fonts are used. The generation unit combines these design elements to automatically generate dozens to hundreds of banner designs and provides them to the analysis unit. The generation unit also has an algorithm that checks the consistency and visual balance of the designs to ensure the quality of the generated banner designs. This allows the generation unit to efficiently generate high-quality banner designs that meet the user's needs.

[0067] The analytics department conducts A / B testing on banner designs generated by the generation department and analyzes their performance in real time. Specifically, the analytics department monitors performance metrics such as click-through rates, conversion rates, and engagement rates for each banner design in real time. For example, the analytics department displays banner A and banner B to different user groups and tests which one achieves a higher click-through rate. The analytics department uses AI to analyze the collected data and quantitatively evaluate the effectiveness of each banner design. Furthermore, the analytics department identifies areas for improvement in banner designs based on user behavior data and feedback. For example, it analyzes the impact of specific colors or taglines on click-through rates and extracts the optimal design elements. Based on these analysis results, the analytics department provides feedback to the generation department to be reflected in the next banner generation. This allows the analytics department to continuously improve the performance of banner designs and maximize the effectiveness of advertising campaigns.

[0068] The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. Specifically, the selection unit identifies and automatically selects the banner design that showed the highest performance based on the results of A / B testing. For example, the selection unit selects the design with the highest click-through rate or conversion rate and uses it for the next advertising campaign. Furthermore, the selection unit identifies areas for design improvement based on feedback provided by the analysis unit and reflects these improvements in the next banner generation. For example, if certain colors or layouts are found to be effective, the selection unit incorporates these elements into the next banner design. This allows the selection unit to provide the optimal design to maximize the ROI of the advertising campaign. In addition, the selection unit collects user feedback and continuously improves the accuracy of the selection process. For example, based on user feedback, it adjusts the parameters of the selection algorithm to perform more accurate design selection. This allows the selection unit to maximize the effectiveness of the advertising campaign and improve user satisfaction.

[0069] The generation unit can learn from past success stories and combinations of design elements to generate optimal variations. For example, it can analyze past success stories and generate banner designs incorporating the most effective design elements. The generation unit can also generate detailed designs based on past success stories. For example, it can adjust the level of detail of the design by referring to past success stories to generate the optimal banner design. In this way, by learning from past success stories, it can generate more effective banner designs.

[0070] The analytics unit can analyze the click-through rate and conversion rate of each banner to identify the most effective design. For example, the analytics unit can analyze the click-through rate of each banner to identify the most effective design. The analytics unit can also analyze the conversion rate of each banner to identify the most effective design. Furthermore, the analytics unit can analyze both the click-through rate and conversion rate to identify the most effective design. This allows for the identification of the most effective banner design by analyzing click-through rates and conversion rates.

[0071] The selection unit can identify areas for design improvement based on the results of A / B testing and reflect them in the next banner generation. For example, the selection unit analyzes the results of A / B testing and identifies areas for design improvement. The selection unit can also identify areas for design improvement based on the results of A / B testing and reflect them in the next banner generation. This allows for continuous improvement of banner design effectiveness by incorporating A / B test results.

[0072] The data collection unit can receive information about campaign objectives and target audiences entered by users. For example, the data collection unit can receive information about campaign objectives and target audiences entered by users. For example, the data collection unit can receive information such as "new product promotion" or "advertising targeting young people." The data collection unit can also receive information about campaign objectives and target audiences based on information entered by users. This allows for the accurate reception of user-entered information, enabling the generation of appropriate banner designs.

[0073] The generation unit can generate multiple banner designs by combining different colors, layouts, and taglines. For example, it can generate banner designs combining different colors. It can also generate banner designs combining different layouts. It can also generate banner designs combining different taglines. This allows for the generation of diverse banner designs by combining different elements.

[0074] The data collection unit can estimate the user's emotions and adjust the input method for campaign objectives and target audience based on the estimated emotions. For example, if the user is stressed, the data collection unit can provide a simple interface and minimize the input steps. For example, if the user is relaxed, the data collection unit can provide detailed input options and suggest customizable input methods. For example, if the user is in a hurry, the data collection unit can prioritize voice input to allow for quick input of campaign objectives and target audience. This reduces the burden on the user by adjusting the input method according to the user'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.

[0075] The data collection unit can analyze a user's past campaign history and suggest the optimal input method. For example, the data collection unit can automatically display campaign objectives and target audiences that the user has frequently entered in the past as suggestions. The data collection unit can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. For example, the data collection unit can predict and suggest campaign objectives and target audiences to use at specific times of day based on the user's past campaign history. In this way, by analyzing past history, the system can suggest the most suitable input method for the user.

[0076] The data collection unit can filter campaign objectives and target audiences based on the user's current marketing strategy and market trends. For example, it can prioritize displaying highly relevant campaign objectives and target audiences based on the user's current marketing strategy. The data collection unit can also analyze market trends and suggest campaign objectives and target audiences that align with those trends. For example, it can suggest optimal campaign objectives and target audiences by referencing the activities of the user's competitors. This allows for more effective campaigns by filtering based on current marketing strategies and market trends.

[0077] The data collection unit can estimate the user's emotions and prioritize the campaign information to be entered based on the estimated emotions. For example, if the user is feeling stressed, the data collection unit may prompt them to prioritize entering important campaign information. For example, if the user is relaxed, the data collection unit may suggest entering detailed campaign information. For example, if the user is in a hurry, the data collection unit may enable them to quickly enter the most important campaign information. This allows important information to be entered preferentially by prioritizing the input information according to the user'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.

[0078] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when they input campaign objectives and target audience. For example, the data collection unit can suggest region-specific campaign objectives and target audiences based on the user's geographical location. For example, the data collection unit can analyze regional market trends based on the user's location and suggest optimal campaign objectives and target audiences. For example, the data collection unit can suggest campaign objectives and target audiences that take into account the trends of regional competitors, considering the user's geographical location. In this way, by considering geographical location, region-specific information can be collected preferentially.

[0079] The data collection unit analyzes users' social media activity and collects relevant information when campaign objectives and target audiences are entered. For example, the data collection unit analyzes users' social media activity and suggests relevant campaign objectives and target audiences. The data collection unit can also suggest optimal campaign objectives and target audiences by referring to the activities of users' followers and friends. For example, the data collection unit can analyze users' interests and preferences on social media and suggest campaign objectives and target audiences based on that. In this way, information relevant to users can be collected by analyzing their social media activity.

[0080] The generation unit can estimate the user's emotions and adjust the banner design generation method based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate a banner design that progresses at a relaxed pace. If the user is in a hurry, the generation unit can also generate a banner design that emphasizes the shortest route. If the user is excited, the generation unit can also generate a banner design with visually stimulating effects. By adjusting the banner design generation method according to the user's emotions, more effective designs can be generated. 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.

[0081] The generation unit can adjust the level of detail in banner designs based on past success stories. For example, the generation unit can analyze past success stories and generate banner designs that incorporate the most effective design elements. The generation unit can also generate detailed designs based on past success stories. For example, the generation unit can adjust the level of detail in designs by referring to past success stories to generate the optimal banner design. This allows for the generation of more effective banner designs by adjusting the level of detail based on past success stories.

[0082] The generation unit can apply different design algorithms to banner designs depending on the attributes of the target audience. For example, the generation unit can apply different design algorithms depending on the age group of the target audience. The generation unit can also apply different design algorithms depending on the gender of the target audience. The generation unit can also apply different design algorithms depending on the interests and concerns of the target audience. By applying different design algorithms according to the attributes of the target audience, more effective banner designs can be generated.

[0083] The generation unit can estimate the user's emotions and adjust the length and size of the banner design based on those emotions. For example, if the user is relaxed, the generation unit will generate a longer banner design. If the user is in a hurry, for example, the generation unit can also generate a shorter, more concise banner design. If the user is excited, for example, the generation unit can also generate a banner design with visually stimulating effects. By adjusting the length and size of the banner design according to the user's emotions, a more effective design can be generated. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The generation unit can prioritize banner designs based on the campaign submission deadline. For example, it can prioritize generating the most effective design based on the campaign submission deadline. If the submission deadline is approaching, it can also prioritize designs that can be generated quickly. If there is ample time before the submission deadline, it can also prioritize generating detailed designs. By prioritizing designs based on the campaign submission deadline, designs can be generated at the optimal time.

[0085] The generation unit can adjust the order of banner designs based on the relevance of the target audience during the banner design generation process. For example, the generation unit can prioritize generating the most relevant designs based on the target audience's interests and preferences. The generation unit can also adjust the order of designs based on the target audience's attributes. For example, the generation unit can determine the optimal order of designs based on the target audience's behavioral history. By adjusting the order of designs based on the relevance of the target audience, more effective banner designs can be generated.

[0086] The analysis unit can estimate the user's emotions and adjust the A / B test criteria based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can set detailed A / B test criteria. For example, if the user is in a hurry, the analysis unit can set simple A / B test criteria. For example, if the user is excited, the analysis unit can set visually stimulating A / B test criteria. This allows for more appropriate testing by adjusting the A / B test criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The analysis unit can improve the accuracy of its analysis during A / B testing by considering the interrelationships between banner designs. For example, the analysis unit can improve the accuracy of its analysis by considering the interrelationships between the colors and layout of the banner designs. The analysis unit can also improve the accuracy of its analysis by considering the interrelationships between the catchphrases of the banner designs. The analysis unit can also improve the accuracy of its analysis by considering the interrelationships between the images and fonts of the banner designs. In this way, the accuracy of the analysis is improved by considering the interrelationships between the banner designs.

[0088] The analysis unit can perform A / B testing while considering the attribute information of the target group. For example, the analysis unit can perform A / B testing analysis according to the age group of the target group. For example, the analysis unit can also perform A / B testing analysis according to the gender of the target group. For example, the analysis unit can also perform A / B testing analysis according to the interests and preferences of the target group. This allows for more effective analysis by considering the attribute information of the target group.

[0089] The analysis unit can estimate the user's emotions and adjust the order in which A / B test results are displayed based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize displaying detailed A / B test results. If the user is in a hurry, the analysis unit may also prioritize displaying concise A / B test results. If the user is excited, the analysis unit may also prioritize displaying visually stimulating A / B test results. This allows for more appropriate result display by adjusting the order in which A / B test results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis unit can perform analysis during A / B testing while considering the geographical distribution of banner designs. For example, the analysis unit can analyze the performance of banner designs in different regions and identify the optimal design. The analysis unit can also analyze the effectiveness of banner designs based on geographical distribution. Furthermore, the analysis unit can analyze banner designs while considering regional market trends. This allows for the identification of the optimal banner design for each region by considering geographical distribution.

[0091] The analysis unit can improve the accuracy of its analysis during A / B testing by referring to relevant literature on banner design. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on banner design. The analysis unit can also analyze the effectiveness of banner design based on data from relevant literature. For example, the analysis unit can identify areas for improvement in banner design by utilizing data from relevant literature. Thus, referring to relevant literature improves the accuracy of the analysis.

[0092] The selection unit can estimate the user's emotions and adjust the optimal design selection method based on the estimated emotions. For example, if the user is relaxed, the selection unit can provide a detailed design selection method. For example, if the user is in a hurry, the selection unit can also provide a concise design selection method. For example, if the user is excited, the selection unit can also provide a visually stimulating design selection method. This allows for the selection of a more appropriate design by adjusting the design selection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The selection unit can optimize its selection algorithm by referring to past A / B test results when selecting the optimal design. For example, the selection unit analyzes past A / B test results and optimizes the algorithm for selecting the optimal design. The selection unit can also adjust the design selection criteria based on past A / B test results. Furthermore, the selection unit can improve the method for selecting the optimal design by referring to past A / B test results. This allows for the optimization of the selection algorithm by referring to past A / B test results.

[0094] The selection function can consider the target audience's attribute information when selecting the optimal design. For example, the selection function can select the optimal design according to the target audience's age group. For example, the selection function can also select the optimal design according to the target audience's gender. For example, the selection function can also select the optimal design according to the target audience's interests and preferences. This allows for the selection of a more effective design by considering the target audience's attribute information.

[0095] The selection unit can estimate the user's emotions and adjust the optimal design display method based on the estimated emotions. For example, if the user is relaxed, the selection unit may provide a detailed design display method. For example, if the user is in a hurry, the selection unit may provide a concise design display method. For example, if the user is excited, the selection unit may provide a visually stimulating design display method. This allows for the display of more appropriate designs by adjusting the design display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The selection function can consider the geographical distribution of banner designs when selecting the optimal design. For example, the selection function can select the optimal design by considering the performance of banner designs in each region. The selection function can also select the optimal design based on geographical distribution. For example, the selection function can also select the optimal design by considering market trends in each region. This allows for the selection of the optimal design for each region by considering geographical distribution.

[0097] The selection unit can improve the accuracy of its selection by referring to relevant literature on banner design when selecting the optimal design. For example, the selection unit can refer to relevant literature on banner design to improve the accuracy of its selection. The selection unit can also select the optimal design based on data from relevant literature. For example, the selection unit can improve the method of selecting the optimal design by utilizing data from relevant literature. This improves the accuracy of the selection by referring to relevant literature.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The data collection unit can analyze a user's past behavior history and automatically suggest the most suitable campaign objectives and target audiences. For example, it can suggest similar campaign objectives based on the success rate of campaigns the user has previously run. It can also analyze the responses of customer segments the user has previously targeted and suggest the most effective target audiences. Furthermore, it can suggest optimal design elements by referencing the design elements of advertising banners the user has used in the past. This allows for the suggestion of more effective campaign objectives and target audiences by leveraging the user's past behavior history.

[0100] The generation unit can estimate the user's emotions and adjust the colors and fonts of the banner design based on those emotions. For example, if the user is relaxed, the generation unit will generate a banner design using calm colors and soft fonts. If the user is excited, for example, the generation unit can also generate a banner design using vibrant colors and strong fonts. If the user is stressed, for example, the generation unit can also generate a simple and visually less burdensome design. By adjusting the colors and fonts of the banner design according to the user's emotions, a more effective design can be generated.

[0101] The analysis unit can implement algorithms that predict the effectiveness of banner designs based on A / B test results. For example, the analysis unit can use an algorithm that learns from past A / B test results to predict the results of the next test. The analysis unit can also predict the effects of different design element combinations and identify the most effective combination. For example, the analysis unit can predict what effect a particular design will have based on the attribute information of the target audience. This makes it possible to select banner designs more efficiently by predicting effects based on A / B test results.

[0102] The selection function can estimate the user's emotions and adjust the design selection criteria based on those emotions. For example, if the user is relaxed, the selection function may provide detailed selection criteria and encourage the user to choose a design they find appealing. If the user is in a hurry, for example, the selection function may provide concise selection criteria and allow the user to quickly choose the best design. If the user is excited, for example, the selection function may provide visually stimulating selection criteria and allow the user to choose a design that captures their interest. This allows for the selection of a more appropriate design by adjusting the selection criteria according to the user's emotions.

[0103] The data collection unit can analyze users' social media activity and propose optimal campaign objectives and target audiences. For example, it can analyze users' social media posts and reactions to suggest relevant campaign objectives. It can also propose optimal target audiences based on the attributes of users' followers and friends. Furthermore, it can analyze users' interests and preferences on social media and propose campaign objectives and target audiences based on that analysis. In this way, by analyzing social media activity, it is possible to collect information relevant to users and propose optimal campaign objectives and target audiences.

[0104] The generation unit can estimate the user's emotions and adjust the animation effects of the banner design based on those emotions. For example, if the user is relaxed, the generation unit will generate a banner design with a relaxed animation effect. If the user is excited, for example, the generation unit can also generate a banner design with a fast-paced animation effect. If the user is stressed, for example, the generation unit can also generate a simple design with minimal animation effects. This allows for the creation of more effective designs by adjusting animation effects according to the user's emotions.

[0105] The analytics unit can be equipped with a function to provide real-time feedback on the effectiveness of banner designs based on the results of A / B tests. For example, the analytics unit can display the click-through rate and conversion rate of each banner in real time, allowing users to immediately see the effects. The analytics unit can also instantly reflect changes to banner designs and analyze their effects in real time. For example, the analytics unit can provide an interface that allows users to adjust banner designs while checking the effects in real time. This allows for the rapid identification of effective banner designs through real-time feedback.

[0106] The data collection unit can estimate the user's emotions and adjust the input interface design based on those emotions. For example, if the user is relaxed, the data collection unit can provide a colorful and visually pleasing interface. If the user is in a hurry, for example, the data collection unit can provide a simple and intuitive interface. If the user is stressed, for example, the data collection unit can provide a visually less burdensome interface with calming colors. By adjusting the input interface design according to the user's emotions, the user's burden can be reduced and input efficiency can be improved.

[0107] The generation unit can adjust banner designs to take into account the cultural background of the target audience. For example, if the target audience belongs to a different cultural sphere, the generation unit will generate banner designs using colors and symbols appropriate to that culture. The generation unit can also generate banner designs that include appropriate taglines and messages based on the cultural background of the target audience. The generation unit can also adjust design details to take into account the cultural customs and values ​​of the target audience. This allows for the generation of more effective banner designs by considering the cultural background of the target audience.

[0108] The analytics unit can estimate the user's emotions and adjust how the A / B test results are visually displayed based on those estimated emotions. For example, if the user is relaxed, the analytics unit can display the results using detailed graphs and charts. If the user is in a hurry, for example, the analytics unit can display the results using concise numbers and icons. If the user is excited, for example, the analytics unit can display the results using visually stimulating animations and effects. This allows for more relevant information to be provided by adjusting how the results are displayed according to the user's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit accepts input from users regarding the campaign objective and target audience. For example, it accepts information such as "promotion of a new product" or "advertising targeting young people." Step 2: The generation unit automatically generates multiple banner designs based on the information received by the collection unit. For example, it learns from past success stories and combinations of design elements to generate multiple banner designs with different colors, layouts, and taglines. Step 3: The analysis unit conducts A / B testing on the banner designs generated by the generation unit and analyzes their performance in real time. For example, it analyzes the click-through rate and conversion rate of each banner to identify the most effective design. Step 4: The selection unit automatically selects the optimal design based on the results obtained by the analysis unit. For example, based on the results of A / B testing, it identifies areas for design improvement and incorporates them into the next banner generation.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and selection unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the receiving device 38 of the smart device 14 and receives information on campaign objectives and target audience entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past success stories and combinations of design elements to generate the optimal variation. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the click-through rate and conversion rate of each banner. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically selects the optimal design based on the results of A / B testing. 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.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and selection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the microphone 238 of the smart glasses 214 and receives information on campaign objectives and target audience entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past success stories and combinations of design elements to generate the optimal variation. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the click-through rate and conversion rate of each banner. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically selects the optimal design based on the results of A / B testing. 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.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and selection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the microphone 238 of the headset terminal 314 and receives information on campaign objectives and target audience entered by the user. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns past success stories and combinations of design elements to generate the optimal variation. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the click-through rate and conversion rate of each banner. The selection unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically selects the optimal design based on the results of A / B testing. 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.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the collection unit, generation unit, analysis unit, and selection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the microphone 238 of the robot 414 and receives information on campaign objectives and target audience entered by the user. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and learns past success stories and combinations of design elements to generate the optimal variation. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the click-through rate and conversion rate of each banner. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically selects the optimal design based on the results of A / B testing. 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A data collection unit that receives input from users regarding campaign objectives and target audience, A generation unit that automatically generates multiple banner designs based on the information received by the collection unit, The banner designs generated by the generation unit are subjected to A / B testing, and the analysis unit analyzes the performance in real time. A selection unit that automatically selects the optimal design based on the results obtained by the analysis unit, Equipped with A system characterized by the following features. (Note 2) The generating unit is It learns from past success stories and combinations of design elements to generate the optimal variations. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the click-through rate and conversion rate of each banner to identify the most effective design. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned selection unit is Based on the results of the A / B test, we will identify areas for design improvement and incorporate them into the next banner generation. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is The system accepts information about campaign objectives and target audience entered by the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Generate multiple banner designs combining different colors, layouts, and taglines. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates user sentiment and adjusts campaign objectives and target audience input methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We analyze the user's past campaign history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When users enter campaign objectives and target audience, filtering is performed based on their current marketing strategy and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates user sentiment and prioritizes campaign information to be entered based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When users input campaign objectives and target audience, the system prioritizes collecting highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When users enter their campaign objectives and target audience, the system analyzes their social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the user's emotions and adjust the banner design generation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating banner designs, adjust the level of detail based on past success stories. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating banner designs, different design algorithms are applied depending on the attributes of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length and size of the banner design 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 banner designs, prioritize designs based on the campaign submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating banner designs, adjust the order of the designs based on the relevance of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Estimate user sentiment and adjust A / B testing criteria based on estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, When conducting A / B testing, consider the interrelationships between banner designs to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit, During A / B testing, the analysis should take into account the attribute information of the target group. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit, It estimates user sentiment and adjusts the order in which A / B test results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit, During A / B testing, the analysis should take into account the geographical distribution of banner designs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit, During A / B testing, refer to relevant literature on banner design to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned selection unit is It estimates the user's emotions and adjusts the optimal design selection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned selection unit is When selecting the optimal design, we optimize the selection algorithm by referring to past A / B test results. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned selection unit is When selecting the optimal design, consider the attribute information of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned selection unit is It estimates the user's emotions and adjusts the optimal design display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned selection unit is When selecting the optimal design, consider the geographical distribution of banner designs. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned selection unit is When selecting the optimal design, refer to relevant literature on banner design to improve the accuracy of your selection. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that receives input from users regarding campaign objectives and target audience, A generation unit that automatically generates multiple banner designs based on the information received by the collection unit, The banner designs generated by the generation unit are subjected to A / B testing, and the analysis unit analyzes the performance in real time. A selection unit that automatically selects the optimal design based on the results obtained by the analysis unit, Equipped with A system characterized by the following features.

2. The generating unit is It learns from past success stories and combinations of design elements to generate the optimal variations. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the click-through rate and conversion rate of each banner to identify the most effective design. The system according to feature 1.

4. The aforementioned selection unit is Based on the results of the A / B test, we will identify areas for design improvement and incorporate them into the next banner generation. The system according to feature 1.

5. The aforementioned collection unit is The system accepts information about campaign objectives and target audience entered by the user. The system according to feature 1.

6. The generating unit is Generate multiple banner designs combining different colors, layouts, and taglines. The system according to feature 1.

7. The aforementioned collection unit is It estimates user sentiment and adjusts campaign objectives and target audience input methods based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned collection unit is We analyze the user's past campaign history and suggest the optimal input method. The system according to feature 1.

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