system

Abstract

An object of the system according to the embodiment is to efficiently generate an advertisement creative with a high CTR or CVR and display an optimum advertisement for each user.SOLUTION: A system according to an embodiment includes an advertising creative analyzer, a generator, and a user analyzer. The advertising creative analysis unit analyzes an advertising creative with a high CTR or CVR. A generation unit generates a new advertising creative based on the features analyzed by the advertising creative analysis unit. The user analysis unit analyzes the advertising creative generated by the generation unit for each user and displays an optimal advertisement.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to efficiently generate advertising creatives with high CTR and CVR and display the most appropriate advertisements for each user.

[0005] The system according to the embodiment aims to efficiently generate advertising creatives with high CTR and CVR, and to display optimal advertisements for each user. [Means for solving the problem]

[0006] The system according to the embodiment includes an ad creative analysis unit, a generation unit, and a user analysis unit. The ad creative analysis unit analyzes ad creatives with high CTR and CVR. The generation unit generates new ad creatives based on the characteristics analyzed by the ad creative analysis unit. The user analysis unit analyzes the ad creatives generated by the generation unit for each user and displays the optimal ad. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate advertising creatives with high CTR and CVR, and can display optimal advertisements for each user. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) An advertisement generation system according to an embodiment of the present invention analyzes advertisement creatives with high CTR and CVR and automatically generates new advertisement creatives based on their characteristics. This system uses Gemini to analyze the characteristics of the content and text in the advertisement creatives, and a generation AI generates new advertisement creatives based on the results. Furthermore, by performing analysis for each user, it becomes possible to display more accurate advertisements for each user. This allows the advertisement generation system to automatically generate advertisements with high CTR and CVR and display the optimal advertisements for each user.

[0029] An advertisement generation system according to an embodiment includes an advertisement creative analysis unit, a generation unit, and a user analysis unit. The advertisement creative analysis unit analyzes advertisement creatives with high CTRs and CVRs. For example, it extracts features such as images, text, layouts, and colors contained in the advertisement creatives and analyzes how they affect the CTRs and CVRs. The generation unit generates new advertisement creatives based on the features analyzed by the advertisement creative analysis unit. For example, it generates advertisement creatives that combine features with high CTRs and CVRs. The user analysis unit analyzes the advertisement creatives generated by the generation unit for each user and displays optimal advertisements. For example, it analyzes the user's past behavioral data and preferences and generates optimal advertisement creatives based on the analyzed data. This allows the advertisement generation system according to an embodiment to automatically generate advertisements with high CTRs and CVRs and display optimal advertisements for each user. For example, by displaying optimal advertisements for specific users, the effectiveness of advertisements can be maximized.

[0030] The ad creative analysis unit can perform a detailed analysis of the visual elements of ad creatives and identify elements that maximize the visual effect. For example, the ad creative analysis unit analyzes the composition of images in ad creatives and gaze guidance to identify elements that maximize the visual effect. For example, if an ad that uses a composition that attracts the eye or gaze guidance techniques has a high CTR, the unit extracts these characteristics. This allows for a detailed analysis of the visual elements of ad creatives and maximizes the visual effect, thereby generating more effective ads.

[0031] The ad creative analysis unit can analyze the audio elements of ad creatives and evaluate the impact of the audio elements on CTR or CVR. For example, the ad creative analysis unit analyzes the narration and background music in ad creatives and evaluates the impact of the audio elements on CTR and CVR. For example, if an ad that uses a particular audio tone or background music has a high CTR, the unit extracts those characteristics. This allows the unit to analyze the audio elements of ad creatives and evaluate their impact on CTR and CVR, thereby generating more effective ads.

[0032] The user analysis unit can compare responses of users in different cultural areas and regions and identify optimal creative elements for each region. For example, the user analysis unit compares responses of users in different cultural areas and regions and identifies optimal creative elements for each region. For example, if a particular color or text tone evokes different responses in different regions, the unit extracts those characteristics. This makes it possible to compare responses of users in different cultural areas and regions and identify optimal creative elements for each region, thereby generating more effective advertisements.

[0033] The user analysis unit can apply the analysis results of advertising creatives to marketing channels to maximize cross-channel effectiveness. For example, the user analysis unit can apply the analysis results of advertising creatives to social media to maximize cross-channel effectiveness. For example, it can identify creative elements with high CTRs in social media ads and apply those elements to other channels. This allows the analysis results of advertising creatives to be applied to other marketing channels to maximize cross-channel effectiveness, thereby generating more effective ads.

[0034] The generation unit can add dynamic elements to the generated advertising creative to increase user engagement. For example, the generation unit adds animation to the advertising creative generated by the generation AI to increase user engagement. For example, the generation unit generates an advertisement using dynamic visual effects. In this way, by adding dynamic elements to the generated advertising creative and increasing user engagement, more effective advertisements can be provided.

[0035] The generation unit can reflect the user's past behavioral data and generate personalized advertisements based on the past behavior. For example, the generation unit uses a generation AI to analyze the user's past behavioral data and generate personalized advertisements based on the results. For example, the generation unit generates advertisements related to products or services viewed in the past. In this way, by reflecting the user's past behavioral data and generating personalized advertisements based on the user's past behavior, more effective advertisements can be provided.

[0036] The generation unit can optimize the generated advertising creative for the device and generate the optimal advertisement for each device. For example, the generation unit optimizes the advertising creative generated by the generation AI for smartphones and generates the optimal advertisement for each device. For example, the generation unit generates an advertisement that matches the screen size and resolution of a smartphone. This allows the generated advertising creative to be optimized for different devices and the optimal advertisement for each device to be generated, thereby providing more effective advertisements.

[0037] The generation unit can match the generated advertising creative to the format and generate an optimal advertisement for each format. For example, the generation unit matches the advertising creative generated by the generation AI to a banner advertisement and generates an optimal advertisement for each format. For example, the generation unit generates an advertisement that matches the size and design of the banner advertisement. This makes it possible to provide more effective advertisements by matching the generated advertising creative to different formats and generating an optimal advertisement for each format.

[0038] The user analysis unit can perform a detailed analysis of the user's past behavioral data and display advertisements based on the behavioral patterns. The user analysis unit, for example, builds a system that analyzes the user's past behavioral data in detail and displays advertisements based on the behavioral patterns. For example, advertisements are displayed based on past browsing history and purchase history. In this way, by analyzing the user's past behavioral data in detail and displaying advertisements based on the behavioral patterns, more effective advertisements can be provided.

[0039] The user analysis unit can analyze the demographic data of the user and display advertisements based on the demographics. The user analysis unit, for example, builds a system that analyzes the demographic data of the user and displays advertisements based on the demographics. For example, advertisements are displayed according to age and gender. In this way, by analyzing the demographic data of the user and displaying advertisements based on the demographics, more effective advertisements can be provided.

[0040] The user analysis unit can apply the analysis results for each user to other marketing channels to maximize cross-channel effectiveness. The user analysis unit can, for example, apply the analysis results for each user to social media to maximize cross-channel effectiveness. For example, it can identify creative elements with high CTR in social media advertising and apply those elements to other channels. This allows the analysis results for each user to be applied to other marketing channels to maximize cross-channel effectiveness, thereby providing more effective advertising.

[0041] The user analysis unit can display optimal advertisements based on the analysis results for each user at different times of the day or day of the week. The user analysis unit, for example, builds a system that displays optimal advertisements for different times of the day or day of the week based on the analysis results for each user. For example, an advertisement with a high CTR is displayed at a specific time of day or day of the week. This makes it possible to provide more effective advertisements by displaying optimal advertisements based on the analysis results for each user at different times of the day or day of the week.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The ad generation system can also analyze the interactive elements of ad creatives to identify elements that increase user engagement. For example, it analyzes clickable buttons, links, interactive animations, etc. included in ads and evaluates how they affect CTR and CVR. This allows it to optimize interactive elements to increase user engagement and generate more effective ads.

[0044] The advertisement generation system can further analyze the visual elements of the advertisement creative in detail and identify elements that maximize the visual effect. For example, the system can analyze the composition of images in the advertisement creative and the gaze guidance to identify elements that maximize the visual effect. This allows the system to analyze the visual elements of the advertisement creative in detail and maximize the visual effect, thereby generating more effective advertisements.

[0045] The ad generation system can further analyze audio elements of the ad creative and evaluate the impact of the audio elements on CTR or CVR. For example, the system can analyze narration and background music in the ad creative and evaluate the impact of the audio elements on CTR and CVR. This allows for the generation of more effective ads by analyzing the audio elements of the ad creative and evaluating their impact on CTR and CVR.

[0046] The advertisement generation system can further analyze the user's past behavioral data in detail and display advertisements based on the user's behavioral patterns. For example, advertisements can be displayed based on the user's past browsing history or purchase history. This allows for the provision of more effective advertisements by analyzing the user's past behavioral data in detail and displaying advertisements based on the user's behavioral patterns.

[0047] The advertisement generation system can further analyze the user's demographic data and display advertisements based on the demographics. For example, advertisements based on age and gender can be displayed. By analyzing the user's demographic data and displaying advertisements based on the demographics, more effective advertisements can be provided.

[0048] The ad generation system can further display optimal ads based on the analysis results for each user at different times of the day or day of the week. For example, it can display ads with high CTRs at specific times of the day or day of the week. This allows for more effective ads to be provided by displaying optimal ads based on the analysis results for each user at different times of the day or day of the week.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The Ad Creative Analysis Department analyzes ad creatives with high CTR and CVR. Specifically, they extract features such as images, text, layout, and color contained in the ad creatives and analyze how they affect CTR and CVR. Step 2: The generation unit generates new ad creatives based on the features analyzed by the ad creative analysis unit. For example, it generates ad creatives that combine features with high CTR and CVR. Step 3: The user analysis unit analyzes the ad creatives generated by the generation unit for each user and displays the most appropriate ad. Specifically, it analyzes the user's past behavioral data and preferences and generates the most appropriate ad creative based on that.

[0051] (Example 2) An advertisement generation system according to an embodiment of the present invention analyzes advertisement creatives with high CTR and CVR and automatically generates new advertisement creatives based on their characteristics. This system uses Gemini to analyze the characteristics of the content and text in the advertisement creatives, and a generation AI generates new advertisement creatives based on the results. Furthermore, by performing analysis for each user, it becomes possible to display more accurate advertisements for each user. This allows the advertisement generation system to automatically generate advertisements with high CTR and CVR and display the optimal advertisements for each user.

[0052] An advertisement generation system according to an embodiment includes an advertisement creative analysis unit, a generation unit, and a user analysis unit. The advertisement creative analysis unit analyzes advertisement creatives with high CTRs and CVRs. For example, it extracts features such as images, text, layouts, and colors contained in the advertisement creatives and analyzes how they affect the CTRs and CVRs. The generation unit generates new advertisement creatives based on the features analyzed by the advertisement creative analysis unit. For example, it generates advertisement creatives that combine features with high CTRs and CVRs. The user analysis unit analyzes the advertisement creatives generated by the generation unit for each user and displays optimal advertisements. For example, it analyzes the user's past behavioral data and preferences and generates optimal advertisement creatives based on the analyzed data. This allows the advertisement generation system according to an embodiment to automatically generate advertisements with high CTRs and CVRs and display optimal advertisements for each user. For example, by displaying optimal advertisements for specific users, the effectiveness of advertisements can be maximized.

[0053] The ad creative analysis unit can analyze emotional elements in ad creatives and evaluate users' emotional responses using an emotion estimation function. For example, the ad creative analysis unit analyzes the colors and emotional tones of text in ad creatives and evaluates users' emotional responses using the emotion estimation function. For example, if an ad that uses bright colors and positive-toned text has a high CTR, this feature can be extracted. This allows for the analysis of emotional elements in ad creatives and the evaluation of users' emotional responses, enabling the generation of more effective ads.

[0054] The ad creative analysis unit can perform a detailed analysis of the visual elements of ad creatives and identify elements that maximize the visual effect. For example, the ad creative analysis unit analyzes the composition of images in ad creatives and gaze guidance to identify elements that maximize the visual effect. For example, if an ad that uses a composition that attracts the eye or gaze guidance techniques has a high CTR, the unit extracts these characteristics. This allows for a detailed analysis of the visual elements of ad creatives and maximizes the visual effect, thereby generating more effective ads.

[0055] The ad creative analysis unit can analyze the audio elements of ad creatives and evaluate the impact of the audio elements on CTR or CVR. For example, the ad creative analysis unit analyzes the narration and background music in ad creatives and evaluates the impact of the audio elements on CTR and CVR. For example, if an ad that uses a particular audio tone or background music has a high CTR, the unit extracts those characteristics. This allows the unit to analyze the audio elements of ad creatives and evaluate their impact on CTR and CVR, thereby generating more effective ads.

[0056] The user analysis unit can compare responses of users in different cultural areas and regions and identify optimal creative elements for each region. For example, the user analysis unit compares responses of users in different cultural areas and regions and identifies optimal creative elements for each region. For example, if a particular color or text tone evokes different responses in different regions, the unit extracts those characteristics. This makes it possible to compare responses of users in different cultural areas and regions and identify optimal creative elements for each region, thereby generating more effective advertisements.

[0057] The user analysis unit can apply the analysis results of advertising creatives to marketing channels to maximize cross-channel effectiveness. For example, the user analysis unit can apply the analysis results of advertising creatives to social media to maximize cross-channel effectiveness. For example, it can identify creative elements with high CTRs in social media ads and apply those elements to other channels. This allows the analysis results of advertising creatives to be applied to other marketing channels to maximize cross-channel effectiveness, thereby generating more effective ads.

[0058] The user analysis unit can use the emotion estimation function to monitor the emotional impact of the advertising creative in real time and identify elements that are likely to resonate emotionally. The user analysis unit, for example, uses the emotion estimation function to monitor the emotional impact of the advertising creative in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. In this way, by using the emotion estimation function to monitor the emotional impact of the advertising creative in real time and identifying elements that are likely to resonate emotionally, more effective advertisements can be generated.

[0059] The generation unit can incorporate an emotion estimation function into the generated advertising creative and generate a personalized advertisement based on the user's emotions. The generation unit, for example, incorporates the emotion estimation function into the advertising creative generated by the generation AI and generates a personalized advertisement based on the user's emotions. For example, the generation unit generates an optimal advertisement based on the user's emotion score. In this way, by incorporating the emotion estimation function into the generated advertising creative and generating a personalized advertisement based on the user's emotions, more effective advertisements can be provided.

[0060] The generation unit can add dynamic elements to the generated advertising creative to increase user engagement. For example, the generation unit adds animation to the advertising creative generated by the generation AI to increase user engagement. For example, the generation unit generates an advertisement using dynamic visual effects. In this way, by adding dynamic elements to the generated advertising creative and increasing user engagement, more effective advertisements can be provided.

[0061] The generation unit can reflect the user's past behavioral data and generate personalized advertisements based on the past behavior. For example, the generation unit uses a generation AI to analyze the user's past behavioral data and generate personalized advertisements based on the results. For example, the generation unit generates advertisements related to products or services viewed in the past. In this way, by reflecting the user's past behavioral data and generating personalized advertisements based on the user's past behavior, more effective advertisements can be provided.

[0062] The generation unit can optimize the generated advertising creative for the device and generate the optimal advertisement for each device. For example, the generation unit optimizes the advertising creative generated by the generation AI for smartphones and generates the optimal advertisement for each device. For example, the generation unit generates an advertisement that matches the screen size and resolution of a smartphone. This allows the generated advertising creative to be optimized for different devices and the optimal advertisement for each device to be generated, thereby providing more effective advertisements.

[0063] The generation unit can match the generated advertising creative to the format and generate an optimal advertisement for each format. For example, the generation unit matches the advertising creative generated by the generation AI to a banner advertisement and generates an optimal advertisement for each format. For example, the generation unit generates an advertisement that matches the size and design of the banner advertisement. This makes it possible to provide more effective advertisements by matching the generated advertising creative to different formats and generating an optimal advertisement for each format.

[0064] The generation unit can evaluate the emotional impact of the advertising creative generated using the emotion estimation function and generate advertisements that are likely to resonate emotionally. The generation unit, for example, uses the emotion estimation function to evaluate the emotional impact of the advertising creative generated by the generation AI. For example, the generation unit analyzes the effectiveness of the advertisement based on the user's emotion score and generates advertisements that are likely to resonate emotionally. This makes it possible to provide more effective advertisements by evaluating the emotional impact of the advertising creative generated using the emotion estimation function and generating advertisements that are likely to resonate emotionally.

[0065] The user analysis unit can use the emotion estimation function for each user to display advertisements based on the emotional state of the user in real time. The user analysis unit, for example, uses the emotion estimation function for each user to build a system that displays advertisements based on the emotional state of the user in real time. For example, the user's facial expressions and voice are analyzed, and advertisements are displayed based on an emotion score. In this way, more effective advertisements can be provided by using the emotion estimation function for each user to display advertisements based on the emotional state of the user in real time.

[0066] The user analysis unit can perform a detailed analysis of the user's past behavioral data and display advertisements based on the behavioral patterns. The user analysis unit, for example, builds a system that analyzes the user's past behavioral data in detail and displays advertisements based on the behavioral patterns. For example, advertisements are displayed based on past browsing history and purchase history. In this way, by analyzing the user's past behavioral data in detail and displaying advertisements based on the behavioral patterns, more effective advertisements can be provided.

[0067] The user analysis unit can analyze the demographic data of the user and display advertisements based on the demographics. The user analysis unit, for example, builds a system that analyzes the demographic data of the user and displays advertisements based on the demographics. For example, advertisements are displayed according to age and gender. In this way, by analyzing the demographic data of the user and displaying advertisements based on the demographics, more effective advertisements can be provided.

[0068] The user analysis unit can apply the analysis results for each user to other marketing channels to maximize cross-channel effectiveness. The user analysis unit can, for example, apply the analysis results for each user to social media to maximize cross-channel effectiveness. For example, it can identify creative elements with high CTR in social media advertising and apply those elements to other channels. This allows the analysis results for each user to be applied to other marketing channels to maximize cross-channel effectiveness, thereby providing more effective advertising.

[0069] The user analysis unit can display optimal advertisements based on the analysis results for each user at different times of the day or day of the week. The user analysis unit, for example, builds a system that displays optimal advertisements for different times of the day or day of the week based on the analysis results for each user. For example, an advertisement with a high CTR is displayed at a specific time of day or day of the week. This makes it possible to provide more effective advertisements by displaying optimal advertisements based on the analysis results for each user at different times of the day or day of the week.

[0070] The user analysis unit uses the emotion estimation function to display advertisements based on the user's emotional state in real time, thereby enabling the display of advertisements that are likely to resonate emotionally. The user analysis unit, for example, builds a system that uses the emotion estimation function to display advertisements based on the user's emotional state in real time. For example, the system analyzes the user's facial expressions and voice and displays advertisements based on an emotion score. This allows the emotion estimation function to be used to display advertisements based on the user's emotional state in real time, thereby enabling the provision of more effective advertisements by displaying advertisements that are likely to resonate emotionally.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The ad generation system can also analyze the interactive elements of ad creatives to identify elements that increase user engagement. For example, it analyzes clickable buttons, links, interactive animations, etc. included in ads and evaluates how they affect CTR and CVR. This allows it to optimize interactive elements to increase user engagement and generate more effective ads.

[0073] The advertisement generation system can further use a user emotion estimation function to evaluate the emotional impact of the advertisement creative and identify elements that are likely to resonate emotionally. For example, the system can analyze the user's facial expressions and voice and calculate an emotion score. This allows the system to generate more effective advertisements by using the emotion estimation function to evaluate the emotional impact of the advertisement creative and identify elements that are likely to resonate emotionally.

[0074] The advertisement generation system can further analyze the visual elements of the advertisement creative in detail and identify elements that maximize the visual effect. For example, the system can analyze the composition of images in the advertisement creative and the gaze guidance to identify elements that maximize the visual effect. This allows the system to analyze the visual elements of the advertisement creative in detail and maximize the visual effect, thereby generating more effective advertisements.

[0075] The ad generation system can further analyze audio elements of the ad creative and evaluate the impact of the audio elements on CTR or CVR. For example, the system can analyze narration and background music in the ad creative and evaluate the impact of the audio elements on CTR and CVR. This allows for the generation of more effective ads by analyzing the audio elements of the ad creative and evaluating their impact on CTR and CVR.

[0076] The advertising generation system can further use a user emotion estimation function to monitor the emotional impact of advertising creatives in real time and identify elements that are likely to resonate emotionally. For example, the system can analyze the user's facial expressions and voice and calculate an emotion score. This allows the emotional impact of advertising creatives to be monitored in real time using the emotion estimation function and identify elements that are likely to resonate emotionally, thereby generating more effective advertisements.

[0077] The advertisement generation system can further analyze the user's past behavioral data in detail and display advertisements based on the user's behavioral patterns. For example, advertisements can be displayed based on the user's past browsing history or purchase history. This allows for the provision of more effective advertisements by analyzing the user's past behavioral data in detail and displaying advertisements based on the user's behavioral patterns.

[0078] The advertisement generation system can further use a user emotion estimation function to display advertisements based on the user's emotional state in real time. For example, it can analyze the user's facial expressions and voice and display advertisements based on an emotion score. This allows for more effective advertisements to be provided by displaying advertisements based on the user's emotional state in real time.

[0079] The advertisement generation system can further analyze the user's demographic data and display advertisements based on the demographics. For example, advertisements based on age and gender can be displayed. By analyzing the user's demographic data and displaying advertisements based on the demographics, more effective advertisements can be provided.

[0080] The advertisement generation system further uses a user emotion estimation function to display advertisements based on the user's emotional state in real time, thereby displaying advertisements that are likely to resonate with the user emotionally. For example, the advertisement generation system analyzes the user's facial expressions and voice and displays advertisements based on an emotion score. This allows the advertisement generation system to provide more effective advertisements by using the emotion estimation function to display advertisements based on the user's emotional state in real time, thereby displaying advertisements that are likely to resonate with the user emotionally.

[0081] The ad generation system can further display optimal ads based on the analysis results for each user at different times of the day or day of the week. For example, it can display ads with high CTRs at specific times of the day or day of the week. This allows for more effective ads to be provided by displaying optimal ads based on the analysis results for each user at different times of the day or day of the week.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The Ad Creative Analysis Department analyzes ad creatives with high CTR and CVR. Specifically, they extract features such as images, text, layout, and color contained in the ad creatives and analyze how they affect CTR and CVR. Step 2: The generation unit generates new ad creatives based on the features analyzed by the ad creative analysis unit. For example, it generates ad creatives that combine features with high CTR and CVR. Step 3: The user analysis unit analyzes the ad creatives generated by the generation unit for each user and displays the most appropriate ad. Specifically, it analyzes the user's past behavioral data and preferences and generates the most appropriate ad creative based on that.

[0084] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0098] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0115] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an 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 emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0128] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0129] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. We have an advertising creative analysis department that analyzes advertising creatives with high CTR and CVR, a generation unit that generates new advertising creatives based on the features analyzed by the advertising creative analysis unit; a user analysis unit that analyzes the advertising creative generated by the generation unit for each user and displays an optimal advertisement. A system characterized by:

2. The advertising creative analysis unit Analyze emotional elements in ad creatives and use emotion estimation to assess users' emotional responses 2. The system of claim 1.

3. The user analysis unit Compare user responses across different cultures and regions to identify the best creative elements for each of those regions 2. The system of claim 1.

4. The generation unit Incorporating emotion estimation functionality into the generated ad creatives to generate personalized ads based on user emotions 2. The system of claim 1.

5. The user analysis unit Using the emotion estimation function for each user, the advertisement is displayed in real time based on the emotional state of the user.

2. The system of claim 1.

6. The advertising creative analysis unit Analyze the visual elements of your ad creative in detail to identify the elements that will maximize visual impact 2. The system of claim 1.

7. The user analysis unit Apply the results of the analysis of the advertising creative to marketing channels to maximize cross-channel effectiveness.

2. The system of claim 1.

8. The generation unit The advertising creative to be generated is optimized for a device, and the optimal advertisement for each device is generated.

2. The system of claim 1.

Images