System

Abstract

An object of a system according to an embodiment is to efficiently generate and optimize advertisement content.SOLUTION: A system according to an embodiment includes a data set learning unit, an advertisement generation unit, and an adjustment unit. The data set learning section learns a data set. The advertisement generation unit generates advertisement content based on the data learned by the data set learning unit. The adjustment unit performs adjustment based on the performance data of the advertisement content generated by the advertisement generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that generating and optimizing advertising content requires a lot of time and money.

[0005] The system according to the embodiment aims to efficiently generate and optimize advertising content. [Means for solving the problem]

[0006] A system according to an embodiment includes a dataset learning unit, an advertisement generation unit, and an adjustment unit. The dataset learning unit learns a dataset. The advertisement generation unit generates advertisement content based on data learned by the dataset learning unit. The adjustment unit adjusts the advertisement content generated by the advertisement generation unit based on performance data. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate and optimize advertising content. [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) The advertisement generation system according to an embodiment of the present invention is a system that enables advertisers to significantly save time and money and provide highly personalized content to consumers, thereby generating unique advertisement content tailored to the advertiser's needs and optimizing advertisement performance.

[0029] An advertisement generation system according to an embodiment includes a dataset learning unit, an advertisement generation unit, and an adjustment unit. The dataset learning unit learns a dataset. For example, the dataset learning unit collects data from past advertising campaigns and learns consumer behavior data and market trends. The dataset learning unit can also select a specific dataset based on the needs of an advertiser and perform learning. For example, the dataset learning unit collects data related to a specific target demographic and performs learning based on the data. Furthermore, the dataset learning unit analyzes the dataset using a generation AI and extracts information necessary for generating advertisement content. The advertisement generation unit generates advertisement content based on the data learned by the dataset learning unit. For example, the advertisement generation unit generates creative content tailored to the needs of the advertiser using the generation AI. The advertisement generation unit can also generate advertisement content that takes into account the preferences and behavioral patterns of the target demographic. For example, the advertisement generation unit generates advertisements aimed at a specific target demographic and evaluates the effectiveness of the advertisements. Furthermore, the advertisement generation unit can also adjust and improve the advertisement content using the generation AI. The adjustment unit makes adjustments based on performance data of the advertisement content generated by the advertisement generation unit. For example, the adjustment unit analyzes advertisement performance data in real time and automatically corrects ineffective portions. The adjustment unit can also optimize advertisement content based on advertisement performance data. For example, the adjustment unit adjusts advertisement content based on advertisement click rates and conversion rates. Furthermore, the adjustment unit can improve advertisement content using a generation AI. As a result, the advertisement generation system according to the embodiment can significantly save advertisers time and money and provide consumers with more highly personalized content. For example, advertisers can shorten the preparation time and reduce costs for advertising campaigns. Furthermore, by providing consumers with highly personalized advertisements, the effectiveness of advertisements can be maximized. Furthermore, optimizing advertisement performance in real time can improve ROI.

[0030] The dataset learning unit can add datasets specific to the advertiser's industry and generate advertising content that reflects the characteristics of each industry. For example, the dataset learning unit collects datasets specific to the advertiser's industry and trains the AI ​​engine. For example, it uses trend data from the fashion industry and consumer purchasing history. The dataset learning unit also develops algorithms specialized for specific industries to generate advertising content that reflects the characteristics of each industry. For example, it generates recipe video ads for the food industry. The dataset learning unit also analyzes consumer behavior patterns for each industry based on the datasets specific to the advertiser's industry and generates creative content based on that. For example, it uses data on popular travel destinations by season in the travel industry. This makes it possible to generate advertising content that reflects the characteristics of each industry.

[0031] The dataset learning unit can automatically detect noisy data included in a dataset and clean the noisy data. For example, the dataset learning unit develops an algorithm that automatically detects noisy data included in a dataset. For example, it identifies and removes outliers and missing values. The dataset learning unit also introduces automated tools for cleaning the noisy data. For example, it builds a system that normalizes and filters data. The dataset learning unit also develops an algorithm that detects and cleans noisy data in real time to improve the quality of the dataset. For example, it automatically removes noise when data is collected. This can improve the quality of the dataset.

[0032] The dataset learning unit can learn datasets from different languages ​​and cultures and generate advertising content from a global perspective. For example, the dataset learning unit collects datasets from different languages ​​and cultures and trains the AI ​​engine. For example, advertising data in English, French, Chinese, etc. is used. The dataset learning unit also develops cross-cultural understanding algorithms to generate advertising content from a global perspective. For example, it reflects culture-specific expressions and designs. The dataset learning unit also generates advertising content for the global market based on datasets from different languages ​​and cultures. For example, it creates advertising videos that support multiple languages. This makes it possible to generate advertising content from a global perspective.

[0033] The dataset learning unit can add audio data and image data to the dataset and generate creative content based on multimodal information. For example, the dataset learning unit adds audio data and image data to the dataset and trains the AI ​​engine. For example, it uses audio narration from an advertisement and product images. The dataset learning unit also develops a creative generation algorithm based on multimodal information. For example, it generates an advertising video that combines audio and images. The dataset learning unit also generates richer advertising content based on audio data and image data. For example, it uses voice recognition technology to generate advertisements in response to voice instructions. This makes it possible to generate creative content based on multimodal information.

[0034] The advertisement generation unit can analyze the advertiser's past advertisement performance data, extract the most effective elements, and reflect them in new advertisements. For example, the advertisement generation unit collects the advertiser's past advertisement performance data and extracts the most effective elements. For example, it identifies elements with high click-through rates and conversion rates. The advertisement generation unit also generates new advertisement content based on the extracted effective elements. For example, it creates creatives that incorporate elements of successful advertisements. The advertisement generation unit also analyzes advertisement performance data and develops algorithms that automatically extract effective elements. For example, it identifies effective elements using data mining technology. This makes it possible to generate effective advertisements based on past advertisement performance data.

[0035] The advertisement generation unit can monitor the behavioral patterns of the target demographic in real time and dynamically generate advertisement content based on that data. The advertisement generation unit, for example, builds a system that monitors the behavioral patterns of the target demographic in real time. For example, it collects website browsing history and purchase history. The advertisement generation unit also develops an algorithm that dynamically generates advertisement content based on the behavioral data collected in real time. For example, it generates advertisements that match the user's current interests. The advertisement generation unit also analyzes the behavioral patterns of the target demographic and dynamically generates advertisement content based on that data. For example, it creates advertisements that match the user's interests, which change in real time. This makes it possible to dynamically generate advertisement content based on the behavioral patterns of the target demographic.

[0036] The advertisement generation unit can learn the needs of advertisers in different industries and applications and generate highly versatile advertisement content. For example, the advertisement generation unit collects the needs of advertisers in different industries and applications and has the AI ​​engine learn them. For example, it collects advertising needs from the fashion and food industries. The advertisement generation unit also develops algorithms that reflect the needs of different industries in order to generate highly versatile advertisement content. For example, it creates advertisement templates that correspond to multiple industries. The advertisement generation unit also generates highly versatile advertisement content based on the needs of advertisers in different industries and applications. For example, it creates advertisements that incorporate industry-specific elements. This makes it possible to generate highly versatile advertisement content.

[0037] The advertisement generation unit can generate different types of advertisement content, such as video advertisements and interactive advertisements, according to the needs of the advertiser. For example, the advertisement generation unit develops an algorithm for generating different types of advertisement content, such as video advertisements and interactive advertisements, according to the needs of the advertiser. For example, the advertisement generation unit incorporates video editing technology and interactive elements. The advertisement generation unit also generates different types of advertisement content based on the needs of the advertiser. For example, the advertisement generation unit converts still image advertisements into video advertisements. The advertisement generation unit also builds a system that automatically generates different types of advertisement content according to the needs of the advertiser. For example, the advertisement generation unit creates interactive advertisements that change according to user operations. This makes it possible to generate different types of advertisement content.

[0038] The adjustment unit can develop an algorithm that automatically corrects ineffective parts based on advertising performance data. The adjustment unit, for example, analyzes advertising performance data and develops an algorithm that identifies ineffective parts. For example, it extracts elements with low click-through rates or low conversion rates. The adjustment unit also builds an algorithm to automatically correct ineffective parts. For example, it replaces low-performing elements with high-performing elements. The adjustment unit also develops a system that automatically corrects ineffective parts based on advertising performance data. For example, it optimizes advertising content in real time. This makes it possible to automatically correct ineffective parts.

[0039] The adjustment unit can introduce a system that automates A / B testing of advertising content and selects the optimal version. The adjustment unit, for example, develops a system that automates A / B testing of advertising content. For example, it automatically delivers different versions of advertisements and collects performance data. The adjustment unit also builds an algorithm that selects the optimal version based on the results of the A / B testing. For example, it selects the version with a high click-through rate or conversion rate. The adjustment unit also introduces a system that conducts A / B testing of advertising content in real time and selects the optimal version. For example, it analyzes user responses in real time and delivers the optimal advertisement. This makes it possible to automatically select the optimal version.

[0040] The adjustment unit can compare advertising performance on different platforms and make adjustments for each optimal platform. The adjustment unit, for example, builds a system that collects and compares advertising performance data on different platforms. For example, it compares click-through rates and conversion rates on social media and search engines. The adjustment unit also develops an algorithm that generates optimal advertising content based on performance data for each platform. For example, it creates short video ads for social media and text ads for search engines. The adjustment unit also introduces a system that compares advertising performance on different platforms in real time and adjusts advertising content for each optimal platform. For example, it optimizes ads based on user behavior data for each platform. This makes it possible to make optimal adjustments for each platform.

[0041] The adjustment unit can adjust and improve advertising content to suit different devices. For example, the adjustment unit builds a system that collects and compares advertising performance data on different devices. For example, it compares click-through rates and conversion rates on smartphones and tablets. The adjustment unit also develops an algorithm that generates optimal advertising content based on performance data for each device. For example, it creates vertical video ads for smartphones and interactive ads for tablets. The adjustment unit also introduces a system that compares advertising performance on different devices in real time and adjusts advertising content for each optimal device. For example, it optimizes ads based on user behavior data for each device. This makes it possible to adjust and improve advertising content to suit different devices.

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

[0043] The advertisement generation system can also analyze a user's purchase history and generate advertisement content based on past purchase patterns. For example, it can collect data on products and services purchased by a user in the past and generate advertisements for related products and services based on that data. It can also generate advertisements suggesting new products and services that the user might be interested in based on the purchase history. It can also analyze the purchase history to generate advertisements related to specific seasons or events. This makes it possible to provide personalized advertisement content based on the user's purchase history.

[0044] The ad generation system can also analyze a user's social media activity and generate ad content based on the user's interests. For example, it can collect posts that a user has shared on social media and content that the user has "liked," and generate relevant ads based on that information. It can also analyze the activities of the user's followers and friends to suggest ads that the user might be interested in. It can also monitor social media trends in real time and generate ads that match the trends. This makes it possible to provide personalized ad content based on the user's social media activity.

[0045] The advertisement generation system can also use the user's location information to generate local advertisement content. For example, it can collect data on the user's current location and places the user has visited in the past and generate advertisements for nearby stores and services based on that data. It can also generate advertisements related to specific regions or events based on the user's location information. It can also analyze the user's movement patterns and generate advertisements that suggest services and products that can be used at the user's destination. This makes it possible to provide personalized advertisement content based on the user's location information.

[0046] The ad generation system can also analyze a user's browsing history and generate ad content based on the user's interests. For example, it can collect data on websites the user has previously visited and keywords the user has searched for, and generate relevant ads based on that data. It can also generate ads that suggest new products or services that the user might be interested in, based on the user's browsing history. It can also analyze the browsing history to generate ads tailored to specific times of day or days of the week. This makes it possible to provide personalized ad content based on the user's browsing history.

[0047] The advertisement generation system can also estimate a user's purchasing intent and generate advertising content based on the estimated purchasing intent. For example, based on data on products and services purchased in the past by the user, it can generate advertisements for products and services that are estimated to be of high purchasing intent. It can also adjust the frequency and timing of advertisement display according to the user's purchasing intent. For example, advertisements can be displayed frequently when the user's purchasing intent is high and not displayed when the user's purchasing intent is low. Furthermore, it is possible to monitor a user's purchasing intent in real time and dynamically change the content of advertisements according to the user's purchasing intent. This makes it possible to provide optimal advertising content based on the user's purchasing intent.

[0048] The advertisement generation system can also collect user feedback and improve advertisement content based on that feedback. For example, it can collect comments and ratings that users have made on advertisements and adjust the advertisement content based on that. It can also improve the creative elements and messages of advertisements based on user feedback. For example, it can emphasize elements for which users have provided positive feedback and remove elements for which users have provided negative feedback. It can also collect user feedback in real time and dynamically change advertisement content in response to the feedback. This makes it possible to provide optimal advertisement content based on user feedback.

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

[0050] Step 1: The dataset learning unit learns the dataset. For example, the dataset learning unit collects data from past advertising campaigns and learns consumer behavior data and market trends. It can also select and learn specific datasets based on the advertiser's needs. Furthermore, generative AI is used to analyze the dataset and extract the information necessary to generate advertising content. Step 2: The advertisement generation unit generates advertising content based on the data learned by the dataset learning unit. For example, the generation AI can be used to generate creative content tailored to the advertiser's needs and generate advertising content that takes into account the preferences and behavioral patterns of the target audience. Furthermore, the advertisement generation unit can also use the generation AI to adjust and improve the advertising content. Step 3: The adjustment unit adjusts the advertising content generated by the advertising generation unit based on performance data. For example, it analyzes advertising performance data in real time and automatically corrects ineffective parts. It can also optimize advertising content based on advertising click rates and conversion rates. It can also use generative AI to improve advertising content.

[0051] (Example 2) The advertisement generation system according to an embodiment of the present invention is a system that enables advertisers to significantly save time and money and provide highly personalized content to consumers, thereby generating unique advertisement content tailored to the advertiser's needs and optimizing advertisement performance.

[0052] An advertisement generation system according to an embodiment includes a dataset learning unit, an advertisement generation unit, and an adjustment unit. The dataset learning unit learns a dataset. For example, the dataset learning unit collects data from past advertising campaigns and learns consumer behavior data and market trends. The dataset learning unit can also select a specific dataset based on the needs of an advertiser and perform learning. For example, the dataset learning unit collects data related to a specific target demographic and performs learning based on the data. Furthermore, the dataset learning unit analyzes the dataset using a generation AI and extracts information necessary for generating advertisement content. The advertisement generation unit generates advertisement content based on the data learned by the dataset learning unit. For example, the advertisement generation unit generates creative content tailored to the needs of the advertiser using the generation AI. The advertisement generation unit can also generate advertisement content that takes into account the preferences and behavioral patterns of the target demographic. For example, the advertisement generation unit generates advertisements aimed at a specific target demographic and evaluates the effectiveness of the advertisements. Furthermore, the advertisement generation unit can also adjust and improve the advertisement content using the generation AI. The adjustment unit makes adjustments based on performance data of the advertisement content generated by the advertisement generation unit. For example, the adjustment unit analyzes advertisement performance data in real time and automatically corrects ineffective portions. The adjustment unit can also optimize advertisement content based on advertisement performance data. For example, the adjustment unit adjusts advertisement content based on advertisement click rates and conversion rates. Furthermore, the adjustment unit can improve advertisement content using a generation AI. As a result, the advertisement generation system according to the embodiment can significantly save advertisers time and money and provide consumers with more highly personalized content. For example, advertisers can shorten the preparation time and reduce costs for advertising campaigns. Furthermore, by providing consumers with highly personalized advertisements, the effectiveness of advertisements can be maximized. Furthermore, optimizing advertisement performance in real time can improve ROI.

[0053] The dataset learning unit can learn consumer emotional responses from data on past advertising campaigns and generate creative content based on those emotional responses. The dataset learning unit, for example, collects data on past advertising campaigns and analyzes consumer emotional responses. For example, it analyzes facial expressions and comments made while watching an advertisement and calculates an emotional score. The dataset learning unit also uses an emotion estimation function to identify factors that contribute to the success of an advertising campaign. For example, it extracts features of advertisements that have a high rate of positive emotional responses and uses these as learning data. The dataset learning unit also develops an emotion-based creative generation algorithm based on consumer emotional response data. For example, it generates advertisements that emphasize elements with a high emotional score. This makes it possible to generate creative content based on consumer emotions.

[0054] The dataset learning unit can add datasets specific to the advertiser's industry and generate advertising content that reflects the characteristics of each industry. For example, the dataset learning unit collects datasets specific to the advertiser's industry and trains the AI ​​engine. For example, it uses trend data from the fashion industry and consumer purchasing history. The dataset learning unit also develops algorithms specialized for specific industries to generate advertising content that reflects the characteristics of each industry. For example, it generates recipe video ads for the food industry. The dataset learning unit also analyzes consumer behavior patterns for each industry based on the datasets specific to the advertiser's industry and generates creative content based on that. For example, it uses data on popular travel destinations by season in the travel industry. This makes it possible to generate advertising content that reflects the characteristics of each industry.

[0055] The dataset learning unit can automatically detect noisy data included in a dataset and clean the noisy data. For example, the dataset learning unit develops an algorithm that automatically detects noisy data included in a dataset. For example, it identifies and removes outliers and missing values. The dataset learning unit also introduces automated tools for cleaning the noisy data. For example, it builds a system that normalizes and filters data. The dataset learning unit also develops an algorithm that detects and cleans noisy data in real time to improve the quality of the dataset. For example, it automatically removes noise when data is collected. This can improve the quality of the dataset.

[0056] The dataset learning unit can learn datasets from different languages ​​and cultures and generate advertising content from a global perspective. For example, the dataset learning unit collects datasets from different languages ​​and cultures and trains the AI ​​engine. For example, advertising data in English, French, Chinese, etc. is used. The dataset learning unit also develops cross-cultural understanding algorithms to generate advertising content from a global perspective. For example, it reflects culture-specific expressions and designs. The dataset learning unit also generates advertising content for the global market based on datasets from different languages ​​and cultures. For example, it creates advertising videos that support multiple languages. This makes it possible to generate advertising content from a global perspective.

[0057] The dataset learning unit can add audio data and image data to the dataset and generate creative content based on multimodal information. For example, the dataset learning unit adds audio data and image data to the dataset and trains the AI ​​engine. For example, it uses audio narration from an advertisement and product images. The dataset learning unit also develops a creative generation algorithm based on multimodal information. For example, it generates an advertising video that combines audio and images. The dataset learning unit also generates richer advertising content based on audio data and image data. For example, it uses voice recognition technology to generate advertisements in response to voice instructions. This makes it possible to generate creative content based on multimodal information.

[0058] The dataset learning unit can use the emotion estimation function to identify elements from the dataset that elicit positive emotions and generate advertising content that emphasizes them. The dataset learning unit, for example, uses the emotion estimation function to identify elements from the dataset that elicit positive emotions. For example, elements that elicit emotions such as joy and surprise are extracted. The dataset learning unit also develops an algorithm that emphasizes the identified elements in order to generate advertising content that emphasizes positive emotions. For example, colors and music that elicit positive emotions are used. The dataset learning unit also uses the emotion estimation function to generate advertising content based on elements that elicit positive emotions. For example, an advertisement is created that incorporates elements with a high emotion score. This makes it possible to generate advertising content that elicits positive emotions.

[0059] The advertisement generation unit can predict the emotional response of the target demographic and generate creative content based on that. The advertisement generation unit, for example, uses an emotion estimation function to predict the emotional response of the target demographic. For example, it calculates an emotion score based on past response data of the target demographic. The advertisement generation unit also uses the emotion estimation function to predict the emotional response of the target demographic and generate creative content based on that. For example, it creates advertisements that elicit positive emotions. The advertisement generation unit also develops an emotion-based creative generation algorithm based on the emotional response data of the target demographic. For example, it generates advertisements that incorporate elements with high emotion scores. This makes it possible to generate creative content based on the emotional response of the target demographic.

[0060] The advertisement generation unit can analyze the advertiser's past advertisement performance data, extract the most effective elements, and reflect them in new advertisements. For example, the advertisement generation unit collects the advertiser's past advertisement performance data and extracts the most effective elements. For example, it identifies elements with high click-through rates and conversion rates. The advertisement generation unit also generates new advertisement content based on the extracted effective elements. For example, it creates creatives that incorporate elements of successful advertisements. The advertisement generation unit also analyzes advertisement performance data and develops algorithms that automatically extract effective elements. For example, it identifies effective elements using data mining technology. This makes it possible to generate effective advertisements based on past advertisement performance data.

[0061] The advertisement generation unit can monitor the behavioral patterns of the target demographic in real time and dynamically generate advertisement content based on that data. The advertisement generation unit, for example, builds a system that monitors the behavioral patterns of the target demographic in real time. For example, it collects website browsing history and purchase history. The advertisement generation unit also develops an algorithm that dynamically generates advertisement content based on the behavioral data collected in real time. For example, it generates advertisements that match the user's current interests. The advertisement generation unit also analyzes the behavioral patterns of the target demographic and dynamically generates advertisement content based on that data. For example, it creates advertisements that match the user's interests, which change in real time. This makes it possible to dynamically generate advertisement content based on the behavioral patterns of the target demographic.

[0062] The advertisement generation unit can learn the needs of advertisers in different industries and applications and generate highly versatile advertisement content. For example, the advertisement generation unit collects the needs of advertisers in different industries and applications and has the AI ​​engine learn them. For example, it collects advertising needs from the fashion and food industries. The advertisement generation unit also develops algorithms that reflect the needs of different industries in order to generate highly versatile advertisement content. For example, it creates advertisement templates that correspond to multiple industries. The advertisement generation unit also generates highly versatile advertisement content based on the needs of advertisers in different industries and applications. For example, it creates advertisements that incorporate industry-specific elements. This makes it possible to generate highly versatile advertisement content.

[0063] The advertisement generation unit can generate different types of advertisement content, such as video advertisements and interactive advertisements, according to the needs of the advertiser. For example, the advertisement generation unit develops an algorithm for generating different types of advertisement content, such as video advertisements and interactive advertisements, according to the needs of the advertiser. For example, the advertisement generation unit incorporates video editing technology and interactive elements. The advertisement generation unit also generates different types of advertisement content based on the needs of the advertiser. For example, the advertisement generation unit converts still image advertisements into video advertisements. The advertisement generation unit also builds a system that automatically generates different types of advertisement content according to the needs of the advertiser. For example, the advertisement generation unit creates interactive advertisements that change according to user operations. This makes it possible to generate different types of advertisement content.

[0064] The advertisement generation unit uses the emotion estimation function to predict a user's emotional response to creative content based on the advertiser's needs, and can propose optimal content. The advertisement generation unit, for example, uses the emotion estimation function to predict a user's emotional response to creative content based on the advertiser's needs. For example, elements that elicit positive emotions are identified. The advertisement generation unit also develops an algorithm that proposes optimal advertising content based on the user's emotional response data. For example, creative content with a high emotional score is preferentially proposed. The advertisement generation unit also uses the emotion estimation function to predict a user's emotional response to creative content based on the advertiser's needs, and generates optimal content. For example, an advertisement incorporating elements with a high emotional score is created. This makes it possible to propose optimal content based on the user's emotional response.

[0065] The adjustment unit can analyze the user's emotional response from the advertisement performance data and make adjustments based on the emotional response. The adjustment unit, for example, collects advertisement performance data and analyzes the user's emotional response using an emotion estimation function. For example, it analyzes facial expressions and comments made while viewing the advertisement and calculates an emotion score. In addition, the adjustment unit develops an algorithm to modify the advertisement content based on the emotion estimation data in order to make adjustments based on emotions. For example, it emphasizes elements that elicit positive emotions. Furthermore, the adjustment unit makes emotion-based adjustments based on the advertisement performance data. For example, it modifies parts with low emotion scores and adds elements that elicit positive emotions. This makes it possible to make adjustments based on the user's emotional response.

[0066] The adjustment unit can develop an algorithm that automatically corrects ineffective parts based on advertising performance data. The adjustment unit, for example, analyzes advertising performance data and develops an algorithm that identifies ineffective parts. For example, it extracts elements with low click-through rates or low conversion rates. The adjustment unit also builds an algorithm to automatically correct ineffective parts. For example, it replaces low-performing elements with high-performing elements. The adjustment unit also develops a system that automatically corrects ineffective parts based on advertising performance data. For example, it optimizes advertising content in real time. This makes it possible to automatically correct ineffective parts.

[0067] The adjustment unit can introduce a system that automates A / B testing of advertising content and selects the optimal version. The adjustment unit, for example, develops a system that automates A / B testing of advertising content. For example, it automatically delivers different versions of advertisements and collects performance data. The adjustment unit also builds an algorithm that selects the optimal version based on the results of the A / B testing. For example, it selects the version with a high click-through rate or conversion rate. The adjustment unit also introduces a system that conducts A / B testing of advertising content in real time and selects the optimal version. For example, it analyzes user responses in real time and delivers the optimal advertisement. This makes it possible to automatically select the optimal version.

[0068] The adjustment unit can compare advertising performance on different platforms and make adjustments for each optimal platform. The adjustment unit, for example, builds a system that collects and compares advertising performance data on different platforms. For example, it compares click-through rates and conversion rates on social media and search engines. The adjustment unit also develops an algorithm that generates optimal advertising content based on performance data for each platform. For example, it creates short video ads for social media and text ads for search engines. The adjustment unit also introduces a system that compares advertising performance on different platforms in real time and adjusts advertising content for each optimal platform. For example, it optimizes ads based on user behavior data for each platform. This makes it possible to make optimal adjustments for each platform.

[0069] The adjustment unit can adjust and improve advertising content to suit different devices. For example, the adjustment unit builds a system that collects and compares advertising performance data on different devices. For example, it compares click-through rates and conversion rates on smartphones and tablets. The adjustment unit also develops an algorithm that generates optimal advertising content based on performance data for each device. For example, it creates vertical video ads for smartphones and interactive ads for tablets. The adjustment unit also introduces a system that compares advertising performance on different devices in real time and adjusts advertising content for each optimal device. For example, it optimizes ads based on user behavior data for each device. This makes it possible to adjust and improve advertising content to suit different devices.

[0070] The adjustment unit uses the emotion estimation function to reevaluate the user's emotional response after adjusting the advertising content, and can continuously make optimal improvements. The adjustment unit, for example, uses the emotion estimation function to build a system that reevaluates the user's emotional response after adjusting the advertising content. For example, it analyzes facial expressions and comments made while watching an advertisement and calculates an emotional score. The adjustment unit also develops an algorithm that continuously improves the advertising content based on the user's emotional response data. For example, it adds elements that elicit positive emotions. The adjustment unit also uses the emotion estimation function to introduce a system that monitors the user's emotional response in real time after adjusting the advertising content and continuously makes optimal improvements. For example, it immediately corrects parts with low emotional scores. This allows continuous optimal improvements based on the user's emotional response.

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

[0072] The advertisement generation system can also analyze a user's purchase history and generate advertisement content based on past purchase patterns. For example, it can collect data on products and services purchased by a user in the past and generate advertisements for related products and services based on that data. It can also generate advertisements suggesting new products and services that the user might be interested in based on the purchase history. It can also analyze the purchase history to generate advertisements related to specific seasons or events. This makes it possible to provide personalized advertisement content based on the user's purchase history.

[0073] The advertisement generation system can also estimate a user's emotions and generate advertisement content based on the estimated emotions. For example, it can analyze the user's facial expressions and voice while watching an advertisement and calculate an emotion score. It can also generate advertisements that emphasize elements that elicit positive emotions based on the user's emotions. It can also monitor the user's emotional responses in real time and dynamically adjust advertisement content according to the user's emotions. This makes it possible to provide advertisement content based on the user's emotions.

[0074] The ad generation system can also analyze a user's social media activity and generate ad content based on the user's interests. For example, it can collect posts that a user has shared on social media and content that the user has "liked," and generate relevant ads based on that information. It can also analyze the activities of the user's followers and friends to suggest ads that the user might be interested in. It can also monitor social media trends in real time and generate ads that match the trends. This makes it possible to provide personalized ad content based on the user's social media activity.

[0075] The advertisement generation system can also use the user's location information to generate local advertisement content. For example, it can collect data on the user's current location and places the user has visited in the past and generate advertisements for nearby stores and services based on that data. It can also generate advertisements related to specific regions or events based on the user's location information. It can also analyze the user's movement patterns and generate advertisements that suggest services and products that can be used at the user's destination. This makes it possible to provide personalized advertisement content based on the user's location information.

[0076] The ad generation system can also estimate the user's emotions and adjust the timing of ad display based on the estimated emotions. For example, by displaying ads when the user is relaxed or excited, the effectiveness of the ads can be maximized. The system can also adjust the frequency of ad display based on the user's emotions. For example, it can refrain from displaying ads when the user is feeling stressed. Furthermore, it can monitor the user's emotional reactions in real time and dynamically change the content of the ads displayed based on their emotions. This makes it possible to display ads optimally based on the user's emotions.

[0077] The ad generation system can also analyze a user's browsing history and generate ad content based on the user's interests. For example, it can collect data on websites the user has previously visited and keywords the user has searched for, and generate relevant ads based on that data. It can also generate ads that suggest new products or services that the user might be interested in, based on the user's browsing history. It can also analyze the browsing history to generate ads tailored to specific times of day or days of the week. This makes it possible to provide personalized ad content based on the user's browsing history.

[0078] The ad generation system can also estimate a user's emotions and adjust the creative elements of the ad based on the estimated emotions. For example, bright colors and cheerful music can be used when the user has positive emotions, and calm colors and gentle music can be used when the user has negative emotions. The system can also adjust the ad's message and catchy copy according to the user's emotions. For example, an energetic message can be used when the user is excited, and a gentle message can be used when the user is relaxed. Furthermore, the system can monitor the user's emotional responses in real time and dynamically change the creative elements of the ad according to the user's emotions. This enables optimal ad creative based on the user's emotions.

[0079] The advertisement generation system can also estimate a user's purchasing intent and generate advertising content based on the estimated purchasing intent. For example, based on data on products and services purchased in the past by the user, it can generate advertisements for products and services that are estimated to be of high purchasing intent. It can also adjust the frequency and timing of advertisement display according to the user's purchasing intent. For example, advertisements can be displayed frequently when the user's purchasing intent is high and not displayed when the user's purchasing intent is low. Furthermore, it is possible to monitor a user's purchasing intent in real time and dynamically change the content of advertisements according to the user's purchasing intent. This makes it possible to provide optimal advertising content based on the user's purchasing intent.

[0080] The ad generation system can also estimate the user's emotions and target ads based on the estimated emotions. For example, new products or high-priced products are suggested when the user has positive emotions, and discounted products or special offers are suggested when the user has negative emotions. It can also fine-tune ad targeting based on the user's emotions. For example, relaxation-related products are suggested when the user is relaxed, and activity-related products are suggested when the user is excited. Furthermore, it can monitor the user's emotional responses in real time and dynamically change ad targeting based on emotions. This enables optimal ad targeting based on the user's emotions.

[0081] The advertisement generation system can also collect user feedback and improve advertisement content based on that feedback. For example, it can collect comments and ratings that users have made on advertisements and adjust the advertisement content based on that. It can also improve the creative elements and messages of advertisements based on user feedback. For example, it can emphasize elements for which users have provided positive feedback and remove elements for which users have provided negative feedback. It can also collect user feedback in real time and dynamically change advertisement content in response to the feedback. This makes it possible to provide optimal advertisement content based on user feedback.

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

[0083] Step 1: The dataset learning unit learns the dataset. For example, the dataset learning unit collects data from past advertising campaigns and learns consumer behavior data and market trends. It can also select and learn specific datasets based on the advertiser's needs. Furthermore, generative AI is used to analyze the dataset and extract the information necessary to generate advertising content. Step 2: The advertisement generation unit generates advertising content based on the data learned by the dataset learning unit. For example, the generation AI can be used to generate creative content tailored to the advertiser's needs and generate advertising content that takes into account the preferences and behavioral patterns of the target audience. Furthermore, the advertisement generation unit can also use the generation AI to adjust and improve the advertising content. Step 3: The adjustment unit adjusts the advertising content generated by the advertising generation unit based on performance data. For example, it analyzes advertising performance data in real time and automatically corrects ineffective parts. It can also optimize advertising content based on advertising click rates and conversion rates. It can also use generative AI to improve advertising content.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

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

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

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0095] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. 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.

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

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

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

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

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

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

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

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

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

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

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

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

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

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

[0126] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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.

[0128] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

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

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

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

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

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

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

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

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

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

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

[0142] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

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

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

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

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

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

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

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

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

Claims

1. a dataset learning unit that learns a dataset; an advertisement generation unit that generates advertisement content based on the data learned by the dataset learning unit; an adjustment unit that performs adjustment based on performance data of the advertising content generated by the advertising generation unit; A system characterized by:

2. The dataset learning unit Learns consumer emotional responses from data on past advertising campaigns and generates creative content based on those responses 2. The system of claim 1.

3. The dataset learning unit Adding an industry-specific dataset of the advertiser and generating the advertising content that reflects the characteristics of each industry based on the industry-specific dataset.

2. The system of claim 1.

4. The dataset learning unit Automatically detecting noise data contained in the data set and cleaning the noise data.

2. The system of claim 1.

5. The dataset learning unit Learning from the datasets of different languages ​​and cultures to generate advertising content from a global perspective 2. The system of claim 1.

6. The dataset learning unit Add audio and image data to the dataset to generate creative content based on multimodal information.

2. The system of claim 1.

7. The dataset learning unit Identifying elements that evoke positive emotions from the dataset and generating advertising content that emphasizes those elements 2. The system of claim 1.

8. The advertisement generation unit Predicting the emotional responses of the target audience and generating creative content based on that.

2. The system of claim 1.

Images