System

Abstract

An object of a system according to an embodiment is to automate an account design of a digital advertisement and realize an efficient advertisement operation.SOLUTION: In one embodiment, a system comprises an account designer, an ad manager, and a performance optimizer. The account design unit designs an account based on the input information of the user. The advertisement management unit performs an advertisement management based on the account designed by the account design unit. The performance optimization unit analyzes the data of the advertisement managed by the advertisement management unit and optimizes the advertisement performance.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] With conventional technology, designing digital advertising accounts required a lot of time and effort, making it difficult to operate efficiently.

[0005] The system according to the embodiment aims to automate the account design of digital advertising and realize efficient advertising operations. [Means for solving the problem]

[0006] The system according to the embodiment includes an account design unit, an advertising operations unit, and a performance optimization unit. The account design unit designs accounts based on information input by users. The advertising operations unit manages advertising based on the accounts designed by the account design unit. The performance optimization unit analyzes data on advertisements managed by the advertising operations unit and optimizes advertising performance. [Effects of the Invention]

[0007] The system according to the embodiment can automate the account design of digital advertising and realize efficient advertising management. [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 account design automation system according to an embodiment of the present invention is a system that performs account design based on user input information and realizes advertising operations and performance optimization. As a result, the account design automation system can efficiently perform account design based on user input information and realize advertising operations and performance optimization.

[0029] An account design automation system according to an embodiment includes an account design unit, an advertising operations unit, and a performance optimization unit. The account design unit designs accounts based on user input information. For example, a generation AI automatically generates optimal account structures, keywords, ad copy, and other information based on the user's input advertising objectives, targets, budget, and other information. The account design unit can also learn from data on past advertising operations and propose optimal account designs. The advertising operations unit manages ads based on the accounts designed by the account design unit. For example, by using the keywords and ad copy proposed by the generation AI as is, the labor required for advertising operations can be significantly reduced. The advertising operations unit can also optimize ad performance based on the account structure proposed by the generation AI. The performance optimization unit analyzes data on ads managed by the advertising operations unit and optimizes ad performance. For example, the generation AI analyzes which keywords and ad copy were effective based on past data and proposes new ad operations based on that analysis. The performance optimization unit can also monitor the progress of advertising operations in real time and automatically adjust as necessary. As a result, the account design automation system according to the embodiment can efficiently design accounts based on information input by users, and achieve advertising management and performance optimization.

[0030] The account design unit can analyze a user's past advertising history and propose an account design optimized for each individual user. For example, the generation AI in the account design unit analyzes a user's past advertising history and extracts specific patterns and success stories. For example, it proposes an account design optimized for each individual user based on keywords and ad copy that have performed well in the past. The account design unit also uses the generation AI to automatically design accounts based on the user's past advertising data. For example, it analyzes past campaign data and proposes optimal targeting settings and budget allocation. The account design unit also uses the generation AI to learn a user's past advertising history and propose optimal account designs for individual users in real time. For example, it automatically generates optimal ad groups and campaign structures based on past data. This makes it possible to propose optimal account designs based on the user's past advertising history.

[0031] The account design department can analyze market trends in real time and dynamically update account designs based on that. In the account design department, for example, the generation AI collects market trend data in real time and reflects it in the account design. For example, it analyzes the latest search trends and competitor trends and suggests optimal keywords and ad copy. The account design department also analyzes market trends in real time and the generation AI dynamically updates the account design. For example, when a new trend emerges, it instantly adjusts the account design to achieve optimal advertising operations. In the account design department, the generation AI also analyzes market trend data in real time and dynamically optimizes the account design. For example, it reflects trends according to seasons and events and suggests effective advertising operations. This allows account designs to be updated in real time based on market trends.

[0032] The account design unit is also compatible with different advertising platforms and can automate account design for multiple platforms. For example, the account design unit uses a generation AI to learn the specifications of different advertising platforms and automate account design for multiple platforms. For example, a system can be built that performs advertising settings for Facebook and Instagram all at once. The account design unit also uses a generation AI to automatically design accounts compatible with different advertising platforms. For example, it allows users to set optimal advertising settings for multiple platforms with a single input. The account design unit also uses a generation AI to analyze data from different advertising platforms and propose optimal account designs for multiple platforms. For example, it automatically generates ad copy and targeting settings that take into account the characteristics of each platform. This makes it possible to automate account design for different advertising platforms.

[0033] The account design department can propose long-term advertising strategies based on the user's business goals. For example, the generation AI analyzes the user's business goals and proposes long-term advertising strategies based on them. For example, it designs advertising campaigns aimed at sales targets or increasing brand awareness. The account design department also automatically proposes long-term advertising strategies based on the user's business goals. For example, it provides advertising operation plans tailored to annual plans or quarterly goals. The account design department also builds a system in which the generation AI designs long-term advertising strategies based on the user's business goals. For example, it automatically generates advertising campaigns aimed at growth strategies and market expansion. This makes it possible to propose long-term advertising strategies based on the user's business goals.

[0034] The ad operations department can monitor the progress of ad operations in real time and make automatic adjustments as necessary. For example, the ad operations department uses generation AI to monitor the progress of ad operations in real time and automatically make adjustments if performance declines. For example, ads with low click-through rates are automatically stopped and the budget is reallocated to other ads. The ad operations department also builds a system that analyzes the progress of ad operations in real time and allows generation AI to make optimal adjustments. For example, it adjusts the frequency of ad display depending on the budget consumption status. The ad operations department also uses generation AI to monitor the progress of ad operations and automatically make adjustments as necessary. For example, it sets the system to prioritize the display of ads with high performance during specific time periods. This allows the progress of ad operations to be monitored in real time and automatically adjusted as necessary.

[0035] The ad operations department can learn the user's operation history and propose the optimal operation procedure for the next ad operation. For example, the ad operations department will develop a system in which a generation AI learns the user's operation history and proposes the optimal operation procedure for the next ad operation. For example, it will present an efficient operation procedure based on past operation patterns. The ad operations department will also analyze the user's operation history and have the generation AI propose the optimal operation procedure. For example, it will prioritize displaying frequently used functions and settings. The ad operations department will also have the generation AI learn the user's operation history and propose the efficient operation procedure for the next ad operation. For example, it will automatically generate the optimal setting procedure based on past success cases. This will allow it to propose the optimal operation procedure for the next ad operation based on the user's operation history.

[0036] The advertising operations department can visualize the progress of advertising operations and provide a dashboard that users can intuitively understand. For example, the advertising operations department uses a generation AI to visualize the progress of advertising operations in real time and provide a dashboard that users can intuitively understand. For example, it displays advertising performance data in graphs and charts. The advertising operations department also builds a system in which a generation AI automatically generates a dashboard that visualizes the progress of advertising operations. For example, it provides an interface that allows users to check click rates and conversion rates at a glance. The advertising operations department also uses a generation AI to visualize the progress of advertising operations and provide a dashboard that users can intuitively understand. For example, it displays advertising budget consumption status and performance fluctuations in real time. This makes it possible to visualize the progress of advertising operations and provide a dashboard that users can intuitively understand.

[0037] The ad operations department can optimize resource allocation between different advertising campaigns and improve overall efficiency. For example, the ad operations department builds a system in which generation AI optimizes resource allocation between different advertising campaigns. For example, it prioritizes allocating budgets to campaigns with high performance. The ad operations department also has generation AI automatically optimize resource allocation between different advertising campaigns. For example, it dynamically adjusts budgets based on click rates and conversion rates. The ad operations department also has generation AI optimize resource allocation between advertising campaigns and improve overall efficiency. For example, it reduces the budget of campaigns with low performance and reallocates it to other campaigns. This optimizes resource allocation between different advertising campaigns and improves overall efficiency.

[0038] The performance optimization unit can analyze past advertising performance data and identify and propose the most effective advertising elements. In the performance optimization unit, for example, the generation AI analyzes past advertising performance data and identifies the most effective keywords and advertising copy. For example, it extracts elements with high click-through rates and conversion rates and reflects them in new advertising operations. The performance optimization unit also builds a system in which the generation AI proposes optimal advertising elements based on past advertising performance data. For example, it automatically generates effective advertising creatives for specific target demographics. In addition, the performance optimization unit can analyze past advertising performance data and identify and propose the most effective advertising elements. For example, it extracts effective advertising elements according to seasons or events and uses them in new campaigns. This makes it possible to identify and propose the most effective advertising elements based on past advertising performance data.

[0039] The performance optimization unit can analyze competitors' advertising performance and propose optimal strategies based on that. For example, the performance optimization unit builds a system in which a generation AI collects and analyzes competitors' advertising performance data. For example, it analyzes competitors' keywords and ad copy and proposes optimal strategies. The performance optimization unit also analyzes competitors' advertising performance and a generation AI proposes optimal strategies. For example, it provides an effective advertising operation plan based on competitors' success stories. The performance optimization unit also analyzes competitors' advertising performance data and a generation AI proposes optimal strategies based on that. For example, it monitors trends in competitors' advertising campaigns in real time and takes optimal measures. This makes it possible to propose optimal strategies based on competitors' advertising performance.

[0040] The performance optimization unit can analyze advertising performance in different regions and cultural spheres and propose the optimal advertising strategy for each region. For example, the performance optimization unit builds a system in which the generation AI collects and analyzes advertising performance data from different regions and cultural spheres. For example, it analyzes search trends and consumer behavior for each region and proposes the optimal advertising strategy. The performance optimization unit also analyzes advertising performance in different regions and cultural spheres and the generation AI proposes the optimal advertising strategy. For example, it automatically generates advertising creatives that take into account the characteristics of each region. The performance optimization unit also analyzes advertising performance data from different regions and cultural spheres and proposes the optimal advertising strategy for each region. For example, it provides advertising operation plans according to the seasons and events in each region. This makes it possible to analyze advertising performance in different regions and cultural spheres and propose the optimal advertising strategy for each region.

[0041] The performance optimization unit can build a predictive model of advertising performance, predict future performance, and propose an optimal strategy. For example, the performance optimization unit develops a system in which a generative AI builds a predictive model of advertising performance and predicts future performance. For example, it predicts future click-through rates and conversion rates based on past data. The performance optimization unit also builds a predictive model of advertising performance using a generative AI and proposes an optimal strategy. For example, it adjusts the advertising budget and targeting settings based on the predicted performance. The performance optimization unit also builds a predictive model of advertising performance using a generative AI, predicts future performance, and proposes an optimal strategy. For example, it provides an effective advertising operation plan using a predictive model according to seasons and events. This makes it possible to build a predictive model of advertising performance, predict future performance, and propose an optimal strategy.

[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 account design unit designs accounts based on user input information. For example, the generation AI automatically generates optimal account structures, keywords, ad copy, etc. based on information such as the advertising objectives, targets, and budget entered by the user. The account design unit can also learn from data on past advertising operations and propose optimal account designs. The ad operations unit manages ads based on the accounts designed by the account design unit. For example, by using the keywords and ad copy proposed by the generation AI as is, the labor hours for advertising operations can be significantly reduced. The ad operations unit can also optimize ad performance based on the account structure proposed by the generation AI. The performance optimization unit analyzes data on ads managed by the ad operations unit and optimizes ad performance. For example, the generation AI analyzes which keywords and ad copy were effective based on past data and proposes new ad operations based on that analysis. The performance optimization unit can also monitor the progress of ad operations in real time and automatically adjust as necessary. As a result, the account design automation system according to the embodiment can efficiently design accounts based on user input information and achieve ad operations and performance optimization.

[0044] The account design unit analyzes a user's past advertising history and can propose account designs optimized for individual users. For example, the generation AI analyzes a user's past advertising history and extracts specific patterns and success stories. For example, it proposes an account design optimized for individual users based on keywords and ad copy that have performed well in the past. The account design unit also automatically designs accounts based on the user's past advertising data. For example, it analyzes past campaign data and proposes optimal targeting settings and budget allocation. The account design unit also learns the user's past advertising history and proposes optimal account designs for individual users in real time. For example, it automatically generates optimal ad groups and campaign structures based on past data. This makes it possible to propose optimal account designs based on the user's past advertising history.

[0045] The account design department can analyze market trends in real time and dynamically update the account design based on that. For example, the generation AI collects market trend data in real time and reflects it in the account design. For example, it analyzes the latest search trends and competitor trends and suggests optimal keywords and ad copy. The account design department also analyzes market trends in real time and the generation AI dynamically updates the account design. For example, when a new trend emerges, it instantly adjusts the account design to achieve optimal advertising operations. The account design department also analyzes market trend data in real time and dynamically optimizes the account design. For example, it reflects trends according to seasons and events and suggests effective advertising operations. This allows the account design to be updated in real time based on market trends.

[0046] The account design unit is also compatible with different advertising platforms and can automate account design for multiple platforms. For example, the generation AI learns the specifications of different advertising platforms and automates account design for multiple platforms. For example, a system can be built that performs advertising settings for Facebook and Instagram all at once. The account design unit also uses the generation AI to automatically design accounts compatible with different advertising platforms. For example, it allows users to set optimal advertising settings for multiple platforms with a single input. The account design unit also uses the generation AI to analyze data from different advertising platforms and propose optimal account designs for multiple platforms. For example, it automatically generates ad copy and targeting settings that take into account the characteristics of each platform. This makes it possible to automate account design for different advertising platforms.

[0047] The account design department can propose long-term advertising strategies based on the user's business goals. For example, the generation AI analyzes the user's business goals and proposes a long-term advertising strategy based on them. For example, it designs an advertising campaign aimed at sales targets or increasing brand awareness. The account design department also uses the generation AI to automatically propose long-term advertising strategies based on the user's business goals. For example, it provides advertising operation plans tailored to annual plans or quarterly goals. The account design department also builds a system in which the generation AI designs long-term advertising strategies based on the user's business goals. For example, it automatically generates advertising campaigns aimed at growth strategies and market expansion. This makes it possible to propose long-term advertising strategies based on the user's business goals.

[0048] The ad operations department can monitor the progress of ad operations in real time and make automatic adjustments as needed. For example, the generation AI can monitor the progress of ad operations in real time and automatically make adjustments if performance declines. For example, it can automatically stop ads with low click-through rates and reallocate the budget to other ads. The ad operations department also builds a system that analyzes the progress of ad operations in real time and allows the generation AI to make optimal adjustments. For example, it can adjust the frequency of ad display depending on the budget consumption status. The ad operations department also monitors the progress of ad operations with the generation AI and automatically makes adjustments as needed. For example, it can set it to prioritize the display of ads with high performance during specific time periods. This allows the progress of ad operations to be monitored in real time and automatically adjusted as needed.

[0049] The ad operations department can learn the user's operation history and propose the optimal operation procedure for the next ad operation. For example, a system can be developed in which a generation AI learns the user's operation history and proposes the optimal operation procedure for the next ad operation. For example, an efficient operation procedure can be proposed based on past operation patterns. The ad operations department can also analyze the user's operation history and have the generation AI propose the optimal operation procedure. For example, frequently used functions and settings can be displayed preferentially. The ad operations department can also have the generation AI learn the user's operation history and propose the efficient operation procedure for the next ad operation. For example, the optimal setting procedure can be automatically generated based on past success stories. This makes it possible to propose the optimal operation procedure for the next ad operation based on the user's operation history.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The account design department designs an account based on the information entered by the user. For example, based on information entered by the user, such as the advertising objectives, targets, and budget, the generation AI automatically generates the optimal account structure, keywords, ad copy, etc. The account design department can also learn from data on past advertising operations and propose optimal account designs. Step 2: The ad operations department runs ads based on the account designed by the account design department. For example, by using the keywords and ad copy suggested by the generation AI as is, the man-hours required for ad operations can be significantly reduced. The ad operations department can also optimize ad performance based on the account structure suggested by the generation AI. Step 3: The performance optimization department analyzes the data of the ads operated by the ad operations department and optimizes ad performance. For example, the generative AI analyzes which keywords and ad copy were effective based on past data, and then proposes new ad operations based on that. The performance optimization department can also monitor the progress of ad operations in real time and make automatic adjustments as necessary.

[0052] (Example 2) The account design automation system according to an embodiment of the present invention is a system that performs account design based on user input information and realizes advertising operations and performance optimization. As a result, the account design automation system can efficiently perform account design based on user input information and realize advertising operations and performance optimization.

[0053] An account design automation system according to an embodiment includes an account design unit, an advertising operations unit, and a performance optimization unit. The account design unit designs accounts based on user input information. For example, a generation AI automatically generates optimal account structures, keywords, ad copy, and other information based on the user's input advertising objectives, targets, budget, and other information. The account design unit can also learn from data on past advertising operations and propose optimal account designs. The advertising operations unit manages ads based on the accounts designed by the account design unit. For example, by using the keywords and ad copy proposed by the generation AI as is, the labor required for advertising operations can be significantly reduced. The advertising operations unit can also optimize ad performance based on the account structure proposed by the generation AI. The performance optimization unit analyzes data on ads managed by the advertising operations unit and optimizes ad performance. For example, the generation AI analyzes which keywords and ad copy were effective based on past data and proposes new ad operations based on that analysis. The performance optimization unit can also monitor the progress of advertising operations in real time and automatically adjust as necessary. As a result, the account design automation system according to the embodiment can efficiently design accounts based on information input by users, and achieve advertising management and performance optimization.

[0054] The account design unit can analyze a user's past advertising history and propose an account design optimized for each individual user. For example, the generation AI in the account design unit analyzes a user's past advertising history and extracts specific patterns and success stories. For example, it proposes an account design optimized for each individual user based on keywords and ad copy that have performed well in the past. The account design unit also uses the generation AI to automatically design accounts based on the user's past advertising data. For example, it analyzes past campaign data and proposes optimal targeting settings and budget allocation. The account design unit also uses the generation AI to learn a user's past advertising history and propose optimal account designs for individual users in real time. For example, it automatically generates optimal ad groups and campaign structures based on past data. This makes it possible to propose optimal account designs based on the user's past advertising history.

[0055] The account design department can analyze market trends in real time and dynamically update account designs based on that. In the account design department, for example, the generation AI collects market trend data in real time and reflects it in the account design. For example, it analyzes the latest search trends and competitor trends and suggests optimal keywords and ad copy. The account design department also analyzes market trends in real time and the generation AI dynamically updates the account design. For example, when a new trend emerges, it instantly adjusts the account design to achieve optimal advertising operations. In the account design department, the generation AI also analyzes market trend data in real time and dynamically optimizes the account design. For example, it reflects trends according to seasons and events and suggests effective advertising operations. This allows account designs to be updated in real time based on market trends.

[0056] The account design unit can use the emotion estimation function to analyze the user's emotional state and propose an account design to reduce stress. For example, the account design unit uses the emotion estimation function to analyze the user's emotional state in real time and propose an account design to reduce stress. For example, if the user is feeling stressed, a simple and intuitive account design is proposed. The account design unit also analyzes the user's emotional state, and the generative AI designs an account to reduce stress. For example, when the user is relaxed, an account design that avoids complicated settings and requires simple operations is proposed. The account design unit also uses the emotion estimation function to propose an account design according to the user's emotional state. For example, when the user is feeling positive, a challenging setting is proposed, and when the user is feeling negative, a simple setting is proposed. This makes it possible to propose an account design that reduces stress based on the user's emotional state.

[0057] The account design unit is also compatible with different advertising platforms and can automate account design for multiple platforms. For example, the account design unit uses a generation AI to learn the specifications of different advertising platforms and automate account design for multiple platforms. For example, a system can be built that performs advertising settings for Facebook and Instagram all at once. The account design unit also uses a generation AI to automatically design accounts compatible with different advertising platforms. For example, it allows users to set optimal advertising settings for multiple platforms with a single input. The account design unit also uses a generation AI to analyze data from different advertising platforms and propose optimal account designs for multiple platforms. For example, it automatically generates ad copy and targeting settings that take into account the characteristics of each platform. This makes it possible to automate account design for different advertising platforms.

[0058] The account design department can propose long-term advertising strategies based on the user's business goals. For example, the generation AI analyzes the user's business goals and proposes long-term advertising strategies based on them. For example, it designs advertising campaigns aimed at sales targets or increasing brand awareness. The account design department also automatically proposes long-term advertising strategies based on the user's business goals. For example, it provides advertising operation plans tailored to annual plans or quarterly goals. The account design department also builds a system in which the generation AI designs long-term advertising strategies based on the user's business goals. For example, it automatically generates advertising campaigns aimed at growth strategies and market expansion. This makes it possible to propose long-term advertising strategies based on the user's business goals.

[0059] The account design department can use the emotion estimation function to automatically generate advertising creatives that evoke the most positive emotions in users. For example, the account design department will use the emotion estimation function to develop a system that automatically generates advertising creatives that evoke the most positive emotions in users. For example, it will select optimal images and text based on the user's emotion data. The account design department will also analyze the user's emotional state, and the generation AI will suggest advertising creatives that elicit positive emotions. For example, it will automatically generate advertisements that combine elements with high emotion scores. The account design department will also use the emotion estimation function to generate advertising creatives in real time that evoke the most positive emotions in users. For example, it will dynamically adjust the advertising content according to changes in the user's emotions. This will enable the automatic generation of advertising creatives that evoke the most positive emotions in users.

[0060] The ad operations department can monitor the progress of ad operations in real time and make automatic adjustments as necessary. For example, the ad operations department uses generation AI to monitor the progress of ad operations in real time and automatically make adjustments if performance declines. For example, ads with low click-through rates are automatically stopped and the budget is reallocated to other ads. The ad operations department also builds a system that analyzes the progress of ad operations in real time and allows generation AI to make optimal adjustments. For example, it adjusts the frequency of ad display depending on the budget consumption status. The ad operations department also uses generation AI to monitor the progress of ad operations and automatically make adjustments as necessary. For example, it sets the system to prioritize the display of ads with high performance during specific time periods. This allows the progress of ad operations to be monitored in real time and automatically adjusted as necessary.

[0061] The ad operations department can learn the user's operation history and propose the optimal operation procedure for the next ad operation. For example, the ad operations department will develop a system in which a generation AI learns the user's operation history and proposes the optimal operation procedure for the next ad operation. For example, it will present an efficient operation procedure based on past operation patterns. The ad operations department will also analyze the user's operation history and have the generation AI propose the optimal operation procedure. For example, it will prioritize displaying frequently used functions and settings. The ad operations department will also have the generation AI learn the user's operation history and propose the efficient operation procedure for the next ad operation. For example, it will automatically generate the optimal setting procedure based on past success cases. This will allow it to propose the optimal operation procedure for the next ad operation based on the user's operation history.

[0062] The advertising operations department can visualize the progress of advertising operations and provide a dashboard that users can intuitively understand. For example, the advertising operations department uses a generation AI to visualize the progress of advertising operations in real time and provide a dashboard that users can intuitively understand. For example, it displays advertising performance data in graphs and charts. The advertising operations department also builds a system in which a generation AI automatically generates a dashboard that visualizes the progress of advertising operations. For example, it provides an interface that allows users to check click rates and conversion rates at a glance. The advertising operations department also uses a generation AI to visualize the progress of advertising operations and provide a dashboard that users can intuitively understand. For example, it displays advertising budget consumption status and performance fluctuations in real time. This makes it possible to visualize the progress of advertising operations and provide a dashboard that users can intuitively understand.

[0063] The ad operations department can optimize resource allocation between different advertising campaigns and improve overall efficiency. For example, the ad operations department builds a system in which generation AI optimizes resource allocation between different advertising campaigns. For example, it prioritizes allocating budgets to campaigns with high performance. The ad operations department also has generation AI automatically optimize resource allocation between different advertising campaigns. For example, it dynamically adjusts budgets based on click rates and conversion rates. The ad operations department also has generation AI optimize resource allocation between advertising campaigns and improve overall efficiency. For example, it reduces the budget of campaigns with low performance and reallocates it to other campaigns. This optimizes resource allocation between different advertising campaigns and improves overall efficiency.

[0064] The ad operations department can use the emotion estimation function to identify the time periods when a user can work most efficiently and make suggestions to concentrate work during those time periods. The ad operations department, for example, uses the emotion estimation function to build a system that identifies the time periods when a user can work most efficiently. For example, it suggests optimal work times based on the user's emotion data. The ad operations department also analyzes the user's emotional state and suggests the time periods when a generation AI can work most efficiently. For example, it concentrates work during times when the user is relaxed. The ad operations department also uses the emotion estimation function to identify the time periods when a user can work most efficiently and makes suggestions to concentrate work during those time periods. For example, it adjusts the work schedule according to the user's emotional changes. This identifies the time periods when a user can work most efficiently and makes suggestions to concentrate work during those time periods.

[0065] The performance optimization unit can analyze past advertising performance data and identify and propose the most effective advertising elements. In the performance optimization unit, for example, the generation AI analyzes past advertising performance data and identifies the most effective keywords and advertising copy. For example, it extracts elements with high click-through rates and conversion rates and reflects them in new advertising operations. The performance optimization unit also builds a system in which the generation AI proposes optimal advertising elements based on past advertising performance data. For example, it automatically generates effective advertising creatives for specific target demographics. In addition, the performance optimization unit can analyze past advertising performance data and identify and propose the most effective advertising elements. For example, it extracts effective advertising elements according to seasons or events and uses them in new campaigns. This makes it possible to identify and propose the most effective advertising elements based on past advertising performance data.

[0066] The performance optimization unit can analyze competitors' advertising performance and propose optimal strategies based on that. For example, the performance optimization unit builds a system in which a generation AI collects and analyzes competitors' advertising performance data. For example, it analyzes competitors' keywords and ad copy and proposes optimal strategies. The performance optimization unit also analyzes competitors' advertising performance and a generation AI proposes optimal strategies. For example, it provides an effective advertising operation plan based on competitors' success stories. The performance optimization unit also analyzes competitors' advertising performance data and a generation AI proposes optimal strategies based on that. For example, it monitors trends in competitors' advertising campaigns in real time and takes optimal measures. This makes it possible to propose optimal strategies based on competitors' advertising performance.

[0067] The performance optimization unit can use the emotion estimation function to make suggestions to optimize advertising performance based on the user's emotional state. For example, the performance optimization unit uses the emotion estimation function to analyze the user's emotional state in real time and make suggestions to optimize advertising performance. For example, when the user has positive emotions, it suggests effective advertising creatives. The performance optimization unit also builds a system in which a generative AI makes suggestions to optimize advertising performance based on the user's emotional state. For example, when the user is feeling stressed, it suggests simple advertising settings. The performance optimization unit also uses the emotion estimation function to make suggestions to optimize advertising performance based on the user's emotional state. For example, when the user is relaxed, it suggests detailed advertising settings. In this way, it makes suggestions to optimize advertising performance based on the user's emotional state.

[0068] The performance optimization unit can analyze advertising performance in different regions and cultural spheres and propose the optimal advertising strategy for each region. For example, the performance optimization unit builds a system in which the generation AI collects and analyzes advertising performance data from different regions and cultural spheres. For example, it analyzes search trends and consumer behavior for each region and proposes the optimal advertising strategy. The performance optimization unit also analyzes advertising performance in different regions and cultural spheres and the generation AI proposes the optimal advertising strategy. For example, it automatically generates advertising creatives that take into account the characteristics of each region. The performance optimization unit also analyzes advertising performance data from different regions and cultural spheres and proposes the optimal advertising strategy for each region. For example, it provides advertising operation plans according to the seasons and events in each region. This makes it possible to analyze advertising performance in different regions and cultural spheres and propose the optimal advertising strategy for each region.

[0069] The performance optimization unit can build a predictive model of advertising performance, predict future performance, and propose an optimal strategy. For example, the performance optimization unit develops a system in which a generative AI builds a predictive model of advertising performance and predicts future performance. For example, it predicts future click-through rates and conversion rates based on past data. The performance optimization unit also builds a predictive model of advertising performance using a generative AI and proposes an optimal strategy. For example, it adjusts the advertising budget and targeting settings based on the predicted performance. The performance optimization unit also builds a predictive model of advertising performance using a generative AI, predicts future performance, and proposes an optimal strategy. For example, it provides an effective advertising operation plan using a predictive model according to seasons and events. This makes it possible to build a predictive model of advertising performance, predict future performance, and propose an optimal strategy.

[0070] The performance optimization unit can use the emotion estimation function to automatically generate advertising creatives that evoke the most positive emotions in users. The performance optimization unit, for example, uses the emotion estimation function to develop a system that automatically generates advertising creatives that evoke the most positive emotions in users. For example, it selects optimal images and text based on user emotion data. The performance optimization unit also analyzes the user's emotional state, and the generation AI suggests advertising creatives that elicit positive emotions. For example, it automatically generates advertisements that combine elements with high emotion scores. The performance optimization unit also uses the emotion estimation function to generate advertising creatives that evoke the most positive emotions in users in real time. For example, it dynamically adjusts the advertising content according to changes in the user's emotions. This makes it possible to automatically generate advertising creatives that evoke the most positive emotions in users.

[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 account design unit designs accounts based on user input information. For example, the generation AI automatically generates optimal account structures, keywords, ad copy, etc. based on information such as the advertising objectives, targets, and budget entered by the user. The account design unit can also learn from data on past advertising operations and propose optimal account designs. The ad operations unit manages ads based on the accounts designed by the account design unit. For example, by using the keywords and ad copy proposed by the generation AI as is, the labor hours for advertising operations can be significantly reduced. The ad operations unit can also optimize ad performance based on the account structure proposed by the generation AI. The performance optimization unit analyzes data on ads managed by the ad operations unit and optimizes ad performance. For example, the generation AI analyzes which keywords and ad copy were effective based on past data and proposes new ad operations based on that analysis. The performance optimization unit can also monitor the progress of ad operations in real time and automatically adjust as necessary. As a result, the account design automation system according to the embodiment can efficiently design accounts based on user input information and achieve ad operations and performance optimization.

[0073] The account design unit analyzes a user's past advertising history and can propose account designs optimized for individual users. For example, the generation AI analyzes a user's past advertising history and extracts specific patterns and success stories. For example, it proposes an account design optimized for individual users based on keywords and ad copy that have performed well in the past. The account design unit also automatically designs accounts based on the user's past advertising data. For example, it analyzes past campaign data and proposes optimal targeting settings and budget allocation. The account design unit also learns the user's past advertising history and proposes optimal account designs for individual users in real time. For example, it automatically generates optimal ad groups and campaign structures based on past data. This makes it possible to propose optimal account designs based on the user's past advertising history.

[0074] The account design department can analyze market trends in real time and dynamically update the account design based on that. For example, the generation AI collects market trend data in real time and reflects it in the account design. For example, it analyzes the latest search trends and competitor trends and suggests optimal keywords and ad copy. The account design department also analyzes market trends in real time and the generation AI dynamically updates the account design. For example, when a new trend emerges, it instantly adjusts the account design to achieve optimal advertising operations. The account design department also analyzes market trend data in real time and dynamically optimizes the account design. For example, it reflects trends according to seasons and events and suggests effective advertising operations. This allows the account design to be updated in real time based on market trends.

[0075] The account design unit can use the emotion estimation function to analyze the user's emotional state and propose an account design to reduce stress. For example, the emotion estimation function can be used to analyze the user's emotional state in real time and propose an account design to reduce stress. For example, if the user is feeling stressed, a simple and intuitive account design can be proposed. The account design unit also analyzes the user's emotional state and the generative AI designs an account to reduce stress. For example, when the user is relaxed, the account design unit proposes an account design that avoids complicated settings and requires simple operations. The account design unit also uses the emotion estimation function to propose an account design that corresponds to the user's emotional state. For example, when the user is feeling positive, the account design unit proposes challenging settings, and when the user is feeling negative, the account design unit proposes simple settings. This makes it possible to propose an account design that reduces stress based on the user's emotional state.

[0076] The account design unit is also compatible with different advertising platforms and can automate account design for multiple platforms. For example, the generation AI learns the specifications of different advertising platforms and automates account design for multiple platforms. For example, a system can be built that performs advertising settings for Facebook and Instagram all at once. The account design unit also uses the generation AI to automatically design accounts compatible with different advertising platforms. For example, it allows users to set optimal advertising settings for multiple platforms with a single input. The account design unit also uses the generation AI to analyze data from different advertising platforms and propose optimal account designs for multiple platforms. For example, it automatically generates ad copy and targeting settings that take into account the characteristics of each platform. This makes it possible to automate account design for different advertising platforms.

[0077] The account design department can propose long-term advertising strategies based on the user's business goals. For example, the generation AI analyzes the user's business goals and proposes a long-term advertising strategy based on them. For example, it designs an advertising campaign aimed at sales targets or increasing brand awareness. The account design department also uses the generation AI to automatically propose long-term advertising strategies based on the user's business goals. For example, it provides advertising operation plans tailored to annual plans or quarterly goals. The account design department also builds a system in which the generation AI designs long-term advertising strategies based on the user's business goals. For example, it automatically generates advertising campaigns aimed at growth strategies and market expansion. This makes it possible to propose long-term advertising strategies based on the user's business goals.

[0078] The account design department can use the emotion estimation function to automatically generate ad creatives that evoke the most positive emotions in users. For example, we will develop a system that uses the emotion estimation function to automatically generate ad creatives that evoke the most positive emotions in users. For example, we will select optimal images and text based on the user's emotion data. The account design department will also analyze the user's emotional state, and the generation AI will suggest ad creatives that elicit positive emotions. For example, we will automatically generate ads that combine elements with high emotion scores. The account design department will also use the emotion estimation function to generate ad creatives in real time that evoke the most positive emotions in users. For example, we will dynamically adjust the ad content according to changes in the user's emotions. This will enable us to automatically generate ad creatives that evoke the most positive emotions in users.

[0079] The ad operations department can monitor the progress of ad operations in real time and make automatic adjustments as needed. For example, the generation AI can monitor the progress of ad operations in real time and automatically make adjustments if performance declines. For example, it can automatically stop ads with low click-through rates and reallocate the budget to other ads. The ad operations department also builds a system that analyzes the progress of ad operations in real time and allows the generation AI to make optimal adjustments. For example, it can adjust the frequency of ad display depending on the budget consumption status. The ad operations department also monitors the progress of ad operations with the generation AI and automatically makes adjustments as needed. For example, it can set it to prioritize the display of ads with high performance during specific time periods. This allows the progress of ad operations to be monitored in real time and automatically adjusted as needed.

[0080] The ad operations department can learn the user's operation history and propose the optimal operation procedure for the next ad operation. For example, a system can be developed in which a generation AI learns the user's operation history and proposes the optimal operation procedure for the next ad operation. For example, an efficient operation procedure can be proposed based on past operation patterns. The ad operations department can also analyze the user's operation history and have the generation AI propose the optimal operation procedure. For example, frequently used functions and settings can be displayed preferentially. The ad operations department can also have the generation AI learn the user's operation history and propose the efficient operation procedure for the next ad operation. For example, the optimal setting procedure can be automatically generated based on past success stories. This makes it possible to propose the optimal operation procedure for the next ad operation based on the user's operation history.

[0081] The ad operations department can use the emotion estimation function to identify the time periods when a user can work most efficiently and make suggestions to concentrate work during those time periods. For example, the emotion estimation function can be used to build a system that identifies the time periods when a user can work most efficiently. For example, the optimal work time can be suggested based on the user's emotion data. The ad operations department can also analyze the user's emotional state and have a generation AI suggest the time periods when the user can work most efficiently. For example, concentrating work during times when the user is relaxed. The ad operations department can also use the emotion estimation function to identify the time periods when a user can work most efficiently and make suggestions to concentrate work during those time periods. For example, the work schedule can be adjusted according to the user's emotional changes. This allows the time periods when the user can work most efficiently to be identified and suggestions to concentrate work during those time periods.

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

[0083] Step 1: The account design department designs an account based on the information entered by the user. For example, based on information entered by the user, such as the advertising objectives, targets, and budget, the generation AI automatically generates the optimal account structure, keywords, ad copy, etc. The account design department can also learn from data on past advertising operations and propose optimal account designs. Step 2: The ad operations department runs ads based on the account designed by the account design department. For example, by using the keywords and ad copy suggested by the generation AI as is, the man-hours required for ad operations can be significantly reduced. The ad operations department can also optimize ad performance based on the account structure suggested by the generation AI. Step 3: The performance optimization department analyzes the data of the ads operated by the ad operations department and optimizes ad performance. For example, the generative AI analyzes which keywords and ad copy were effective based on past data, and then proposes new ad operations based on that. The performance optimization department can also monitor the progress of ad operations in real time and make automatic adjustments as necessary.

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

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

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

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

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

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

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

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

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

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

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

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

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

[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. an account design unit that designs an account based on information input by a user; an advertising management unit that manages advertising based on the account designed by the account design unit; a performance optimization unit that analyzes data on advertisements operated by the advertisement operation unit and optimizes advertisement performance. A system characterized by:

2. The account design unit Analyze the user's past advertising history and propose an account design optimized for each individual user.

2. The system of claim 1.

3. The account design unit Analyze market trends in real time and dynamically update the account design accordingly 2. The system of claim 1.

4. The account design unit Analyzing the emotional state of the user and proposing a design for the account to reduce stress 2. The system of claim 1.

5. The account design unit Supports different advertising platforms and automates the design of multi-platform accounts.

2. The system of claim 1.

6. The account design unit Propose long-term advertising strategies based on the user's business goals 2. The system of claim 1.

7. The account design unit Automatically generate advertising creative that gives the user the most positive feelings 2. The system of claim 1.

8. The advertising operations department Monitor the progress of the advertising operations in real time and make automatic adjustments as needed 2. The system of claim 1.

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