system

Abstract

The system according to the embodiment aims to automate the generation of advertising images and the optimization of their effectiveness. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a measurement unit, and an adjustment unit. The reception unit receives image generation conditions from a user. The generation unit generates an image based on the conditions received by the reception unit. The measurement unit posts the image generated by the generation unit as an advertisement and measures the advertising effectiveness. The adjustment unit adjusts the image based on the advertising effectiveness measured by the measurement unit.

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] In conventional technologies, the generation of advertising images and optimization of their effectiveness are performed manually, which is inefficient and leaves room for improvement.

[0005] The system according to the embodiment aims to automate the generation of advertising images and the optimization of their effectiveness. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a measurement unit, and an adjustment unit. The reception unit receives image generation conditions from a user. The generation unit generates an image based on the conditions received by the reception unit. The measurement unit posts the image generated by the generation unit as an advertisement and measures the advertising effectiveness. The adjustment unit adjusts the image based on the advertising effectiveness measured by the measurement unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the generation of advertising images and optimize their effectiveness. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 image generation system according to an embodiment of the present invention uses a generation AI to automatically generate images that meet certain conditions, automatically measure the advertising effectiveness of the advertisements when they are displayed, and generate optimized images. In this image generation system, a user inputs the conditions for the image they want to generate, and the generation AI generates an image based on those conditions. The generated image is displayed as an advertisement, and the advertising effectiveness (e.g., number of impressions and number of conversions) is automatically measured. The generation AI generates a new, optimized image based on the measured advertising effectiveness. By repeating this process, advertising effectiveness is maximized. For example, a user inputs conditions such as "a photo of a family enjoying themselves on the beach in the summer" or "an image showcasing a new smartphone product." These conditions are input into the generation AI. The generation AI then generates an image based on the input conditions. The generation AI generates an image that meets the conditions based on data it has learned in advance. For example, based on the condition "a photo of a family enjoying themselves on the beach in the summer," an image depicting a family having fun on the beach is generated. The generated image is displayed as an advertisement. Once the advertisement is displayed, the advertising effectiveness (e.g., number of impressions and number of conversions) is automatically measured. For example, the system measures how many times an ad is displayed (number of impressions) and how many people clicked on the ad and purchased a product (number of conversions). Based on the measured advertising effectiveness, the generation AI generates a new optimized image. The generation AI analyzes the measured data and generates a new image to maximize advertising effectiveness. For example, if the number of impressions is high but the number of conversions is low, the generation AI will change the content of the image and optimize it to increase the number of conversions. By repeating this process, advertising effectiveness is maximized. Users can run effective advertising campaigns by using images automatically generated by the generation AI as advertisements. In this way, the image generation system can maximize advertising effectiveness by generating images based on the user's conditions, measuring advertising effectiveness, and optimizing it.

[0029] An image generation system according to an embodiment includes a reception unit, a generation unit, a measurement unit, and an optimization unit. The reception unit receives image generation conditions from a user. The conditions for the image the user wants to generate include, but are not limited to, resolution, color tone, and style. The reception unit receives user input of conditions such as "a photo of a family having fun on the beach in summer" or "an image showcasing a new smartphone." The generation unit uses a generation AI to generate an image based on the conditions received by the reception unit. The generation AI generates an image that meets the conditions based on data previously learned. For example, the generation AI generates an image depicting a family having fun on the beach based on the condition "a photo of a family having fun on the beach in summer." The generation unit can also adjust the style and color tone of the image using the generation AI. For example, the generation AI can estimate a user's emotions and adjust the style of the image based on the estimated emotions. The measurement unit displays the image generated by the generation unit as an advertisement and measures the advertising effectiveness. The advertising effectiveness includes, but is not limited to, the number of impressions and the number of conversions. The measurement unit measures, for example, how many times an advertisement is displayed (number of impressions), or the number of people who clicked on the advertisement and purchased a product (number of conversions). The optimization unit optimizes images based on the advertising effectiveness measured by the measurement unit. The optimization unit analyzes the measured data and generates a new image to maximize advertising effectiveness. For example, if the number of impressions is high but the number of conversions is low, the optimization unit changes the content of the image and optimizes it to increase the number of conversions. As a result, the image generation system according to the embodiment can maximize advertising effectiveness by generating images based on user conditions, measuring advertising effectiveness, and optimizing them.

[0030] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates image generation conditions that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest image generation conditions to be used in a specific time period based on the user's past input history. This improves user convenience by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0031] When inputting image generation conditions, the reception unit can filter the image generation conditions based on the user's current project or field of interest. For example, the reception unit can prioritize displaying image generation conditions related to the user's current project. The reception unit can also suggest highly relevant image generation conditions based on the user's field of interest. Furthermore, the reception unit can filter optimal image generation conditions by referring to the user's past project history. This enables more appropriate image generation by prioritizing conditions based on the user's project or field of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's project data into the generation AI and cause the generation AI to filter optimal image generation conditions.

[0032] When inputting image generation conditions, the reception unit can prioritize receiving highly relevant conditions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying image generation conditions related to that area. The reception unit can also suggest image generation conditions related to locations close to the user's current location. Furthermore, the reception unit can filter optimal image generation conditions based on the user's geographical location information. This makes it possible to generate images suitable for the area by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to filter highly relevant conditions.

[0033] When inputting image generation conditions, the reception unit can analyze the user's social media activity and suggest related conditions. The reception unit can suggest related image generation conditions based on, for example, the content frequently posted by the user on social media. The reception unit can also analyze the user's social media interests and suggest optimal image generation conditions. Furthermore, the reception unit can filter highly relevant image generation conditions by referring to the user's social media activity history. This makes it possible to generate images that meet the user's needs by suggesting related conditions based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI suggest related conditions.

[0034] The generation unit can adjust the level of detail of the generation based on the importance of the conditions when generating an image. The generation unit generates a detailed image based on, for example, an important condition. The generation unit can also generate a simplified image based on a condition with a low priority. Furthermore, the generation unit can adjust the image resolution and depiction of details according to the importance of the condition. This enables efficient image generation by generating an image with a level of detail according to the importance of the condition. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input condition importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0035] The generation unit can apply different generation algorithms depending on the category of conditions when generating an image. For example, the generation unit applies a specific algorithm to generate an image of a natural landscape. The generation unit can also apply a different algorithm to generate an image of a product introduction. Furthermore, the generation unit can apply an even different algorithm to generate an image of a portrait. This makes it possible to generate more appropriate images by applying a generation algorithm depending on the category of conditions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input condition category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0036] When generating an image, the generation unit can determine the generation priority based on the submission time of the conditions. For example, the generation unit prioritizes the most recently submitted conditions and generates an image. The generation unit can also prioritize the most recent conditions, leaving the oldest submitted conditions behind. Furthermore, the generation unit can dynamically adjust the order of generation according to the submission time. This enables efficient image generation by setting the priority based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of the conditions into the generation AI and have the generation AI determine the generation priority.

[0037] The generation unit can adjust the order of generation based on the relevance of the conditions when generating an image. For example, the generation unit prioritizes highly relevant conditions to generate an image. The generation unit can also prioritize important conditions, leaving less relevant conditions for later. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the conditions. This enables efficient image generation by setting the order based on the relevance of the conditions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input condition relevance data into the generation AI and have the generation AI adjust the order of generation.

[0038] The measurement unit can improve the accuracy of measurement by taking into account the interrelationships between advertisements during measurement. For example, the measurement unit analyzes the interrelationships between advertisements within the same campaign and corrects the measurement results. Furthermore, when multiple advertisements are displayed simultaneously, the measurement unit can adjust the measurement results by taking into account the impact of such displays. Furthermore, the measurement unit can improve the accuracy of measurement by taking into account the display order and timing of advertisements. In this way, the accuracy of measurement is improved by taking into account the interrelationships between advertisements. Some or all of the above-described processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input advertisement interrelationship data into the generation AI and cause the generation AI to improve the accuracy of measurement.

[0039] The measurement unit can take into account the attribute information of the advertiser when measuring. The measurement unit corrects the measurement results based on, for example, the industry and size of the advertiser. The measurement unit can also improve the accuracy of the measurement by referring to the advertiser's past advertising performance. Furthermore, the measurement unit can adjust the measurement results based on the advertiser's target demographic. This improves the accuracy of the measurement by taking into account the attribute information of the advertiser. Some or all of the above-mentioned processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input the attribute data of the advertiser into the generation AI and cause the generation AI to improve the accuracy of the measurement.

[0040] The measurement unit can perform measurement taking into account the geographical distribution of the advertisement. For example, the measurement unit displays the measurement results separately for each region where the advertisement is displayed. The measurement unit can also correct the advertising effectiveness based on the geographical distribution. Furthermore, the measurement unit can improve the accuracy of the measurement by taking into account the characteristics of the region where the advertisement is displayed. This improves the accuracy of the measurement by taking the geographical distribution into account. Some or all of the above-described processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input the geographical distribution data of the advertisement into the generation AI and cause the generation AI to improve the accuracy of the measurement.

[0041] The measurement unit can improve the accuracy of the measurement by referring to literature related to the advertisement during measurement. The measurement unit can correct the measurement results by referring to, for example, academic papers or market research reports related to the advertisement. The measurement unit can also improve the accuracy of the measurement by referring to past research on the effectiveness of the advertisement. Furthermore, the measurement unit can analyze the measurement results based on literature related to the advertisement and improve the accuracy. In this way, the accuracy of the measurement is improved by referring to the related literature. Some or all of the above-mentioned processing in the measurement unit can be performed using, for example, AI, or can be performed without using AI. For example, the measurement unit can input literature data related to the advertisement into the generation AI and cause the generation AI to improve the accuracy of the measurement.

[0042] During optimization, the optimization unit can optimize the optimization algorithm by referring to past optimization data. The optimization unit, for example, selects an optimal algorithm based on past optimization data. The optimization unit can also analyze past optimization results and adjust algorithm parameters. Furthermore, the optimization unit can improve the accuracy of optimization by referring to past optimization data. In this way, the accuracy of optimization is improved by selecting an optimal algorithm based on past optimization data. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past optimization data into a generation AI and cause the generation AI to select an optimal algorithm.

[0043] During optimization, the optimization unit can apply different optimization methods to different categories of advertisements. For example, the optimization unit can apply a specific optimization method to product advertisements. The optimization unit can also apply a different optimization method to service advertisements. Furthermore, the optimization unit can apply an even different optimization method to brand advertisements. This enables more effective optimization by applying an optimization method according to the advertisement category. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input advertisement category data into the generation AI and cause the generation AI to select an appropriate optimization method.

[0044] During optimization, the optimization unit can weight the optimization based on the submission time of the advertisement. For example, the optimization unit prioritizes optimizing the most recently submitted advertisement. The optimization unit can also prioritize the most recent advertisement, leaving older submitted advertisements behind. Furthermore, the optimization unit can dynamically adjust the optimization weight according to the submission time. This enables efficient optimization by weighting based on the submission time. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input advertisement submission time data into the generation AI and have the generation AI perform the optimization weighting.

[0045] During optimization, the optimization unit can perform optimization by referring to market data related to the advertisement. The optimization unit, for example, proposes optimal advertising content based on the market data. The optimization unit can also analyze market trends and determine the direction of optimization. Furthermore, the optimization unit can perform optimization to maximize the effectiveness of the advertisement by referring to the market data. In this way, the advertising effectiveness is maximized by proposing optimal advertising content based on the related market data. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input market data related to the advertisement into the generation AI and cause the generation AI to propose optimal advertising content.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The reception unit can analyze the success rate of the user's past advertising campaigns and propose optimal image generation conditions. For example, it can prioritize the display of conditions from campaigns that have achieved high conversion rates in the past. The reception unit can also propose similar conditions by referring to the style and color tone of images used in past campaigns. Furthermore, the reception unit can also propose conditions suitable for specific seasons or events based on data from past campaigns. This makes it possible to generate more effective advertising images by utilizing past success data.

[0048] When generating images, the generator can analyze the user's social media activity data and generate images based on trends. For example, it can generate related images based on content recently shared by the user on social media and hashtags. It can also adjust the style and theme of the image based on topics of interest to the user's followers. It can also apply popular filters and effects on social media to generate images that match trends. This makes it possible to generate images that reflect social media trends.

[0049] When measuring the effectiveness of an advertisement, the measurement unit can correct the measurement results by taking into account the user's device information. For example, the measurement results can be displayed separately, taking into account differences in advertisement display between desktop and mobile devices. The measurement unit can also correct the effectiveness of an advertisement based on the resolution and screen size of the user's device. Furthermore, the measurement results can be adjusted by taking into account the usage status of the user's device (for example, remaining battery level and network connection status). In this way, by taking into account device information, more accurate measurement of advertisement effectiveness becomes possible.

[0050] When measuring the effectiveness of an advertisement, the measurement unit can correct the measurement results by taking into account the user's browsing history. For example, the current effectiveness of an advertisement can be corrected based on the history of advertisements the user has viewed in the past. The measurement results can also be adjusted by referring to data on websites and apps frequently visited by the user. Furthermore, the measurement results can be corrected by predicting the effectiveness of an advertisement during a specific time period or day of the week from the user's browsing history. In this way, by taking browsing history into account, more accurate measurement of the effectiveness of an advertisement is possible.

[0051] The optimization unit can take into account the attribute information of the target audience of the advertisement during optimization. For example, it can propose optimal ad content based on the age group and gender of the target audience. It can also adjust the style and theme of the advertisement based on the interests and concerns of the target audience. Furthermore, it can optimize the timing and location of ad display by taking into account the geographic distribution of the target audience. This allows for more effective ad optimization by taking into account the attribute information of the target audience.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The reception unit receives image generation conditions from the user. The conditions for the image the user wants to generate include resolution, color tone, style, etc. For example, the unit accepts user input conditions such as "a photo of a family having fun on the beach in the summer" or "an image showcasing a new smartphone product." Step 2: The generation unit uses the generation AI to generate an image based on the conditions received by the reception unit. The generation AI generates an image that matches the conditions based on data it has learned in advance. For example, based on the condition "a photo of a family enjoying themselves at the beach in summer," the generation unit generates an image depicting a family having fun at the beach. The generation unit can also use the generation AI to adjust the style and color tone of the image. Step 3: The measurement unit posts the image generated by the generation unit as an advertisement and measures the advertising effectiveness. Advertising effectiveness includes the number of impressions and the number of conversions. For example, it measures how many times an advertisement is displayed (number of impressions) and how many people clicked on the advertisement and purchased a product (number of conversions). Step 4: The adjustment unit adjusts the image based on the advertising effectiveness measured by the measurement unit. For example, if the number of impressions is high but the number of conversions is low, the adjustment unit changes the content of the image to increase the number of conversions.

[0054] (Example 2) An image generation system according to an embodiment of the present invention uses a generation AI to automatically generate images that meet certain conditions, automatically measure the advertising effectiveness of the advertisements when they are displayed, and generate optimized images. In this image generation system, a user inputs the conditions for the image they want to generate, and the generation AI generates an image based on those conditions. The generated image is displayed as an advertisement, and the advertising effectiveness (e.g., number of impressions and number of conversions) is automatically measured. The generation AI generates a new, optimized image based on the measured advertising effectiveness. By repeating this process, advertising effectiveness is maximized. For example, a user inputs conditions such as "a photo of a family enjoying themselves on the beach in the summer" or "an image showcasing a new smartphone product." These conditions are input into the generation AI. The generation AI then generates an image based on the input conditions. The generation AI generates an image that meets the conditions based on data it has learned in advance. For example, based on the condition "a photo of a family enjoying themselves on the beach in the summer," an image depicting a family having fun on the beach is generated. The generated image is displayed as an advertisement. Once the advertisement is displayed, the advertising effectiveness (e.g., number of impressions and number of conversions) is automatically measured. For example, the system measures how many times an ad is displayed (number of impressions) and how many people clicked on the ad and purchased a product (number of conversions). Based on the measured advertising effectiveness, the generation AI generates a new optimized image. The generation AI analyzes the measured data and generates a new image to maximize advertising effectiveness. For example, if the number of impressions is high but the number of conversions is low, the generation AI will change the content of the image and optimize it to increase the number of conversions. By repeating this process, advertising effectiveness is maximized. Users can run effective advertising campaigns by using images automatically generated by the generation AI as advertisements. In this way, the image generation system can maximize advertising effectiveness by generating images based on the user's conditions, measuring advertising effectiveness, and optimizing it.

[0055] An image generation system according to an embodiment includes a reception unit, a generation unit, a measurement unit, and an optimization unit. The reception unit receives image generation conditions from a user. The conditions for the image the user wants to generate include, but are not limited to, resolution, color tone, and style. The reception unit receives user input of conditions such as "a photo of a family having fun on the beach in summer" or "an image showcasing a new smartphone." The generation unit uses a generation AI to generate an image based on the conditions received by the reception unit. The generation AI generates an image that meets the conditions based on data previously learned. For example, the generation AI generates an image depicting a family having fun on the beach based on the condition "a photo of a family having fun on the beach in summer." The generation unit can also adjust the style and color tone of the image using the generation AI. For example, the generation AI can estimate a user's emotions and adjust the style of the image based on the estimated emotions. The measurement unit displays the image generated by the generation unit as an advertisement and measures the advertising effectiveness. The advertising effectiveness includes, but is not limited to, the number of impressions and the number of conversions. The measurement unit measures, for example, how many times an advertisement is displayed (number of impressions), or the number of people who clicked on the advertisement and purchased a product (number of conversions). The optimization unit optimizes images based on the advertising effectiveness measured by the measurement unit. The optimization unit analyzes the measured data and generates a new image to maximize advertising effectiveness. For example, if the number of impressions is high but the number of conversions is low, the optimization unit changes the content of the image and optimizes it to increase the number of conversions. As a result, the image generation system according to the embodiment can maximize advertising effectiveness by generating images based on user conditions, measuring advertising effectiveness, and optimizing them.

[0056] The reception unit can estimate the user's emotions and customize the input interface for image generation conditions based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit can provide a simple and intuitive interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input and enable quick input of image generation conditions. This improves the efficiency and accuracy of input by providing an interface tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0057] The reception unit can analyze the user's past input history and suggest the optimal input method. For example, the reception unit can automatically display as candidates image generation conditions that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception unit can predict and suggest image generation conditions to be used in a specific time period based on the user's past input history. This improves user convenience by suggesting the optimal input method based on the past input history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI suggest the optimal input method.

[0058] When inputting image generation conditions, the reception unit can filter the image generation conditions based on the user's current project or field of interest. For example, the reception unit can prioritize displaying image generation conditions related to the user's current project. The reception unit can also suggest highly relevant image generation conditions based on the user's field of interest. Furthermore, the reception unit can filter optimal image generation conditions by referring to the user's past project history. This enables more appropriate image generation by prioritizing conditions based on the user's project or field of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's project data into the generation AI and cause the generation AI to filter optimal image generation conditions.

[0059] The reception unit can estimate the user's emotions and prioritize the input conditions based on the estimated user emotions. For example, if the user is nervous, the reception unit can prioritize displaying important conditions, simplifying the input process. Furthermore, if the user is relaxed, the reception unit can prioritize displaying detailed conditions and provide a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize displaying the most important conditions, enabling quick input. This enables efficient condition input by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI. For example, the reception unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0060] When inputting image generation conditions, the reception unit can prioritize receiving highly relevant conditions by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying image generation conditions related to that area. The reception unit can also suggest image generation conditions related to locations close to the user's current location. Furthermore, the reception unit can filter optimal image generation conditions based on the user's geographical location information. This makes it possible to generate images suitable for the area by taking the geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location data to the generation AI and cause the generation AI to filter highly relevant conditions.

[0061] When inputting image generation conditions, the reception unit can analyze the user's social media activity and suggest related conditions. The reception unit can suggest related image generation conditions based on, for example, the content frequently posted by the user on social media. The reception unit can also analyze the user's social media interests and suggest optimal image generation conditions. Furthermore, the reception unit can filter highly relevant image generation conditions by referring to the user's social media activity history. This makes it possible to generate images that meet the user's needs by suggesting related conditions based on social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and have the generation AI suggest related conditions.

[0062] The generation unit can estimate the user's emotions and adjust the style of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an image with soft colors and a calm design. If the user is excited, the generation unit can also generate an image with vivid colors and a dynamic design. If the user is depressed, the generation unit can also generate an image with a bright, uplifting design. This allows for the creation of more effective advertising images by generating images with a style appropriate to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0063] The generation unit can adjust the level of detail of the generation based on the importance of the conditions when generating an image. The generation unit generates a detailed image based on, for example, an important condition. The generation unit can also generate a simplified image based on a condition with a low priority. Furthermore, the generation unit can adjust the image resolution and depiction of details according to the importance of the condition. This enables efficient image generation by generating an image with a level of detail according to the importance of the condition. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input condition importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0064] The generation unit can apply different generation algorithms depending on the category of conditions when generating an image. For example, the generation unit applies a specific algorithm to generate an image of a natural landscape. The generation unit can also apply a different algorithm to generate an image of a product introduction. Furthermore, the generation unit can apply an even different algorithm to generate an image of a portrait. This makes it possible to generate more appropriate images by applying a generation algorithm depending on the category of conditions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input condition category data into the generation AI and cause the generation AI to select an appropriate generation algorithm.

[0065] The generation unit can estimate the user's emotions and adjust the color tone of the generated image based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate an image with soft colors. If the user is excited, the generation unit can also generate an image with bright colors. If the user is depressed, the generation unit can also generate an image with bright, encouraging colors. This allows for the creation of more effective advertising images by generating images with colors that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0066] When generating an image, the generation unit can determine the generation priority based on the submission time of the conditions. For example, the generation unit prioritizes the most recently submitted conditions and generates an image. The generation unit can also prioritize the most recent conditions, leaving the oldest submitted conditions behind. Furthermore, the generation unit can dynamically adjust the order of generation according to the submission time. This enables efficient image generation by setting the priority based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the submission time of the conditions into the generation AI and have the generation AI determine the generation priority.

[0067] The generation unit can adjust the order of generation based on the relevance of the conditions when generating an image. For example, the generation unit prioritizes highly relevant conditions to generate an image. The generation unit can also prioritize important conditions, leaving less relevant conditions for later. Furthermore, the generation unit can dynamically adjust the order of generation according to the relevance of the conditions. This enables efficient image generation by setting the order based on the relevance of the conditions. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input condition relevance data into the generation AI and have the generation AI adjust the order of generation.

[0068] The measurement unit can estimate the user's emotions and adjust the display method of the measurement results based on the estimated user emotions. For example, if the user is nervous, the measurement unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the measurement unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the measurement unit can provide a display method that focuses on the main points. This allows for a deeper understanding of the measurement results by providing a display method that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the measurement unit can be performed using, for example, an AI, or without an AI. For example, the measurement unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0069] The measurement unit can improve the accuracy of measurement by taking into account the interrelationships between advertisements during measurement. For example, the measurement unit analyzes the interrelationships between advertisements within the same campaign and corrects the measurement results. Furthermore, when multiple advertisements are displayed simultaneously, the measurement unit can adjust the measurement results by taking into account the impact of such displays. Furthermore, the measurement unit can improve the accuracy of measurement by taking into account the display order and timing of advertisements. In this way, the accuracy of measurement is improved by taking into account the interrelationships between advertisements. Some or all of the above-described processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input advertisement interrelationship data into the generation AI and cause the generation AI to improve the accuracy of measurement.

[0070] The measurement unit can take into account the attribute information of the advertiser when measuring. The measurement unit corrects the measurement results based on, for example, the industry and size of the advertiser. The measurement unit can also improve the accuracy of the measurement by referring to the advertiser's past advertising performance. Furthermore, the measurement unit can adjust the measurement results based on the advertiser's target demographic. This improves the accuracy of the measurement by taking into account the attribute information of the advertiser. Some or all of the above-mentioned processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input the attribute data of the advertiser into the generation AI and cause the generation AI to improve the accuracy of the measurement.

[0071] The measurement unit can estimate the user's emotions and adjust the display order of the measurement results based on the estimated user emotions. For example, if the user is nervous, the measurement unit can prioritize displaying important measurement results. Furthermore, if the user is relaxed, the measurement unit can prioritize displaying detailed measurement results. Furthermore, if the user is in a hurry, the measurement unit can prioritize displaying measurement results that highlight key points. This allows for a deeper understanding of the measurement results by providing a display order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the measurement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the measurement unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0072] The measurement unit can perform measurement taking into account the geographical distribution of the advertisement. For example, the measurement unit displays the measurement results separately for each region where the advertisement is displayed. The measurement unit can also correct the advertising effectiveness based on the geographical distribution. Furthermore, the measurement unit can improve the accuracy of the measurement by taking into account the characteristics of the region where the advertisement is displayed. This improves the accuracy of the measurement by taking the geographical distribution into account. Some or all of the above-described processing in the measurement unit may be performed using, for example, AI, or may be performed without using AI. For example, the measurement unit can input the geographical distribution data of the advertisement into the generation AI and cause the generation AI to improve the accuracy of the measurement.

[0073] The measurement unit can improve the accuracy of the measurement by referring to literature related to the advertisement during measurement. The measurement unit can correct the measurement results by referring to, for example, academic papers or market research reports related to the advertisement. The measurement unit can also improve the accuracy of the measurement by referring to past research on the effectiveness of the advertisement. Furthermore, the measurement unit can analyze the measurement results based on literature related to the advertisement and improve the accuracy. In this way, the accuracy of the measurement is improved by referring to the related literature. Some or all of the above-mentioned processing in the measurement unit can be performed using, for example, AI, or can be performed without using AI. For example, the measurement unit can input literature data related to the advertisement into the generation AI and cause the generation AI to improve the accuracy of the measurement.

[0074] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated user's emotions. For example, the optimization unit can apply a detailed optimization method when the user is relaxed. The optimization unit can also apply a quick optimization method when the user is in a hurry. Furthermore, the optimization unit can apply a visually stimulating optimization method when the user is excited. This enables more effective optimization by providing an optimization method that corresponds to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the optimization unit can be performed using, for example, an AI, or without an AI. For example, the optimization unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0075] During optimization, the optimization unit can optimize the optimization algorithm by referring to past optimization data. The optimization unit, for example, selects an optimal algorithm based on past optimization data. The optimization unit can also analyze past optimization results and adjust algorithm parameters. Furthermore, the optimization unit can improve the accuracy of optimization by referring to past optimization data. In this way, the accuracy of optimization is improved by selecting an optimal algorithm based on past optimization data. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input past optimization data into a generation AI and cause the generation AI to select an optimal algorithm.

[0076] During optimization, the optimization unit can apply different optimization methods to different categories of advertisements. For example, the optimization unit can apply a specific optimization method to product advertisements. The optimization unit can also apply a different optimization method to service advertisements. Furthermore, the optimization unit can apply an even different optimization method to brand advertisements. This enables more effective optimization by applying an optimization method according to the advertisement category. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input advertisement category data into the generation AI and cause the generation AI to select an appropriate optimization method.

[0077] The optimization unit can estimate the user's emotions and determine optimization priorities based on the estimated user emotions. For example, if the user is nervous, the optimization unit can prioritize important optimization items. Furthermore, if the user is relaxed, the optimization unit can also prioritize detailed optimization items. Furthermore, if the user is in a hurry, the optimization unit can prioritize optimization items that can be processed quickly. This enables efficient optimization by setting priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the optimization unit can be performed using, for example, AI, or without AI. For example, the optimization unit can input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0078] During optimization, the optimization unit can weight the optimization based on the submission time of the advertisement. For example, the optimization unit prioritizes optimizing the most recently submitted advertisement. The optimization unit can also prioritize the most recent advertisement, leaving older submitted advertisements behind. Furthermore, the optimization unit can dynamically adjust the optimization weight according to the submission time. This enables efficient optimization by weighting based on the submission time. Some or all of the above-described processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input advertisement submission time data into the generation AI and have the generation AI perform the optimization weighting.

[0079] During optimization, the optimization unit can perform optimization by referring to market data related to the advertisement. The optimization unit, for example, proposes optimal advertising content based on the market data. The optimization unit can also analyze market trends and determine the direction of optimization. Furthermore, the optimization unit can perform optimization to maximize the effectiveness of the advertisement by referring to the market data. In this way, the advertising effectiveness is maximized by proposing optimal advertising content based on the related market data. Some or all of the above-mentioned processing in the optimization unit may be performed using, for example, AI, or may be performed without using AI. For example, the optimization unit can input market data related to the advertisement into the generation AI and cause the generation AI to propose optimal advertising content. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, measurement unit, and optimization unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives image generation conditions from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image that meets the conditions using a generation AI. The measurement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and measures the advertising effectiveness of the generated image. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the image based on the measured advertising effectiveness. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, measurement unit, and optimization unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives image generation conditions from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image that meets the conditions using a generation AI. The measurement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and measures the advertising effectiveness of the generated image. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the image based on the measured advertising effectiveness. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, measurement unit, and optimization unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and receives image generation conditions from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image that meets the conditions using a generation AI. The measurement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and measures the advertising effectiveness of the generated image. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the image based on the measured advertising effectiveness. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, measurement unit, and optimization unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives image generation conditions from a user. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an image that meets the conditions using a generation AI. The measurement unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and measures the advertising effectiveness of the generated image. The optimization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and optimizes the image based on the measured advertising effectiveness.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The reception unit can analyze the success rate of the user's past advertising campaigns and propose optimal image generation conditions. For example, it can prioritize the display of conditions from campaigns that have achieved high conversion rates in the past. The reception unit can also propose similar conditions by referring to the style and color tone of images used in past campaigns. Furthermore, the reception unit can also propose conditions suitable for specific seasons or events based on data from past campaigns. This makes it possible to generate more effective advertising images by utilizing past success data.

[0082] The reception unit can estimate the user's emotions and, based on the estimated user's emotions, provide relaxing music and backgrounds when inputting image generation conditions. For example, if the user is feeling stressed, calm music and a natural landscape can be displayed as a background. Also, if the user is relaxed, the reception unit can play the user's favorite music to provide a comfortable environment. Furthermore, if the user is in a hurry, music that enhances concentration can be provided to enable quick input. In this way, by providing an environment that corresponds to the user's emotions, the efficiency and accuracy of input can be improved.

[0083] The generation unit can estimate the user's emotions and apply specific filters and effects to the generated image based on the estimated user emotions. For example, if the user is relaxed, a soft filter can be applied to generate an image with a calm atmosphere. If the user is excited, a vivid effect can be applied to generate a dynamic image. Furthermore, if the user is depressed, a bright and uplifting effect can be applied. In this way, more effective advertising images can be created by applying filters and effects according to the user's emotions.

[0084] When generating images, the generator can analyze the user's social media activity data and generate images based on trends. For example, it can generate related images based on content recently shared by the user on social media and hashtags. It can also adjust the style and theme of the image based on topics of interest to the user's followers. It can also apply popular filters and effects on social media to generate images that match trends. This makes it possible to generate images that reflect social media trends.

[0085] The generation unit can estimate the user's emotions and adjust the composition of the generated image based on the estimated user emotions. For example, if the user is relaxed, a spacious composition can be used to create a calm atmosphere. If the user is excited, a dynamic image can be generated by using a moving composition. Furthermore, if the user is depressed, a bright and encouraging composition can be used. In this way, by generating an image with a composition that corresponds to the user's emotions, more effective advertising images can be created.

[0086] When measuring the effectiveness of an advertisement, the measurement unit can correct the measurement results by taking into account the user's device information. For example, the measurement results can be displayed separately, taking into account differences in advertisement display between desktop and mobile devices. The measurement unit can also correct the effectiveness of an advertisement based on the resolution and screen size of the user's device. Furthermore, the measurement results can be adjusted by taking into account the usage status of the user's device (for example, remaining battery level and network connection status). In this way, by taking into account device information, more accurate measurement of advertisement effectiveness becomes possible.

[0087] When measuring the effectiveness of an advertisement, the measurement unit can correct the measurement results by taking into account the user's browsing history. For example, the current effectiveness of an advertisement can be corrected based on the history of advertisements the user has viewed in the past. The measurement results can also be adjusted by referring to data on websites and apps frequently visited by the user. Furthermore, the measurement results can be corrected by predicting the effectiveness of an advertisement during a specific time period or day of the week from the user's browsing history. In this way, by taking browsing history into account, more accurate measurement of the effectiveness of an advertisement is possible.

[0088] The optimization unit can estimate the user's emotions and select a dataset to use during optimization based on the estimated user's emotions. For example, if the user is relaxed, a dataset containing many past success cases can be used. If the user is in a hurry, a dataset that can quickly produce results can be used. Furthermore, if the user is excited, a visually stimulating dataset can be used. This allows for more effective optimization by using a dataset that corresponds to the user's emotions.

[0089] The optimization unit can take into account the attribute information of the target audience of the advertisement during optimization. For example, it can propose optimal ad content based on the age group and gender of the target audience. It can also adjust the style and theme of the advertisement based on the interests and concerns of the target audience. Furthermore, it can optimize the timing and location of ad display by taking into account the geographic distribution of the target audience. This allows for more effective ad optimization by taking into account the attribute information of the target audience.

[0090] The optimization unit can estimate the user's emotions and adjust the parameters of the algorithm used during optimization based on the estimated user emotions. For example, if the user is relaxed, detailed parameter settings can be made to perform highly accurate optimization. If the user is in a hurry, the parameters can be simplified to obtain results quickly. Furthermore, if the user is excited, the parameters can be adjusted to obtain visually stimulating results. This allows for more effective optimization by setting parameters according to the user's emotions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The reception unit receives image generation conditions from the user. The conditions for the image the user wants to generate include resolution, color tone, style, etc. For example, the unit accepts user input conditions such as "a photo of a family having fun on the beach in the summer" or "an image showcasing a new smartphone product." Step 2: The generation unit uses the generation AI to generate an image based on the conditions received by the reception unit. The generation AI generates an image that matches the conditions based on data it has learned in advance. For example, based on the condition "a photo of a family enjoying themselves at the beach in summer," the generation unit generates an image depicting a family having fun at the beach. The generation unit can also use the generation AI to adjust the style and color tone of the image. Step 3: The measurement unit posts the image generated by the generation unit as an advertisement and measures the advertising effectiveness. Advertising effectiveness includes the number of impressions and the number of conversions. For example, it measures how many times an advertisement is displayed (number of impressions) and how many people clicked on the advertisement and purchased a product (number of conversions). Step 4: The adjustment unit adjusts the image based on the advertising effectiveness measured by the measurement unit. For example, if the number of impressions is high but the number of conversions is low, the adjustment unit changes the content of the image to increase the number of conversions.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0095] 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.

[0096] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] 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.

[0109] 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.

[0110] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0130] 7, a 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0140] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0141] 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.

[0142] 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.

[0143] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0144] 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.

[0145] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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).

[0150] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0151] 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."

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] [Explanation of symbols]

[0165] 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. a reception unit that receives image generation conditions from a user; a generation unit that generates an image based on the conditions accepted by the acceptance unit; a measurement unit that displays the image generated by the generation unit as an advertisement and measures the effectiveness of the advertisement; an adjustment unit that adjusts the image based on the advertising effect measured by the measurement unit; A system characterized by:

2. The reception unit Estimate the user's emotions and customize the input interface for image generation conditions based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyzes the user's past input history and suggests appropriate input methods 2. The system of claim 1.

4. The reception unit Filtering image generation criteria based on the user's current project or area of ​​interest 2. The system of claim 1.

5. The reception unit Estimate the user's emotions and prioritize the input conditions based on the estimated user emotions.

2. The system of claim 1.

6. The reception unit When inputting image generation conditions, the system takes into account the user's geographical location information and prioritizes the most relevant conditions.

2. The system of claim 1.

7. The reception unit When entering image generation conditions, the system analyzes the user's social media activity and suggests related conditions.

2. The system of claim 1.

8. The generation unit Estimate the user's emotion and adjust the style of the generated image based on the estimated user emotion.

2. The system of claim 1.

Images