System

Abstract

An object of a system according to an embodiment is to automate generation of advertisement content, selection of an advertisement medium, and a contract procedure.SOLUTION: A system includes a generation AI, an advertisement generation part, an advertisement media retrieval part, and a contract automatization part. The generation AI automatically generates the advertisement content using the generation AI. The advertisement generation unit generates an advertisement based on the advertisement content generated by the generation AI. The advertisement medium retrieval part retrieves an optimum advertisement medium for publishing the advertisement generated by the advertisement generation part. The contract automating part automates a contract procedure with the advertising medium selected by the advertising medium searching part.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, the process from creating advertising content to selecting advertising media and completing contract procedures was done manually, which was inefficient and time-consuming.

[0005] The system according to the embodiment aims to automate processes from the creation of advertising content to the selection of advertising media and contract procedures. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, an advertisement generation unit, an advertising medium search unit, and a contract automation unit. The generation AI automatically generates advertisement content using the generation AI. The advertisement generation unit generates advertisements based on the advertisement content generated by the generation AI. The advertising medium search unit searches for optimal advertising media for displaying the advertisements generated by the advertisement generation unit. The contract automation unit automates the contract procedures with the advertising media selected by the advertising medium search unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate processes from the generation of advertising content to the selection of advertising media and contract procedures. [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 digital advertisement automatic generation system according to an embodiment of the present invention is a platform that uses generation AI to automatically generate digital advertisements for private cars and enables advertisers to "search" for and "contract" with passenger car advertising media nationwide. As a result, the digital advertisement automatic generation system enables advertisers to efficiently create, distribute, and manage digital advertisements using private cars.

[0029] A digital advertisement automatic generation system according to an embodiment includes a generation AI, an advertisement generation unit, an advertising medium search unit, and a contract automation unit. The generation AI automatically generates advertisement content based on information provided by a user. For example, when a user inputs information about a product or service they want to advertise, the generation AI analyzes the information and generates attractive advertising copy and design. The generation AI generates advertisement content using a text generation AI (e.g., LLM) or a multimodal generation AI. The advertisement generation unit generates advertisements based on the advertisement content generated by the generation AI. For example, the advertisement generation unit creates text advertisements based on the advertisement copy generated by the generation AI. The advertisement generation unit can also create image advertisements or video advertisements based on the design generated by the generation AI. The advertising medium search unit searches for optimal advertising media for displaying advertisements generated by the generation AI. For example, the advertising medium search unit searches a database of passenger car advertising media nationwide to select a passenger car that is optimal for the advertisement's target demographic and region. The advertising medium search unit can also select optimal advertising media by analyzing real-time traffic data and social media activity. The contract automation unit automates contract procedures with advertising media selected by the advertising media search unit. For example, the contract automation unit automatically generates a contract for placing an advertisement and concludes a contract between the advertiser and the advertising media. The contract automation unit can also integrate an electronic signature function to completely digitize the contract procedures. As a result, the digital advertisement automatic generation system according to the embodiment enables advertisers to efficiently create, distribute, and manage digital advertisements using private cars. For example, advertisers can automatically generate attractive advertisements using generation AI, select optimal advertising media, and automate the contract procedures, thereby quickly and effectively distributing advertisements. In addition, the effectiveness of advertisements can be maximized by monitoring the display status of advertisements in real time.

[0030] Generative AI can analyze a user's past advertising history and generate optimal ad content based on past success stories. For example, generative AI analyzes a user's past advertising campaign data and extracts elements of successful ads. For example, it generates new ads based on the wording and design of ads that have recorded high click-through rates in the past. Generative AI also suggests optimal ad content based on performance data from ads the user has run in the past. For example, it incorporates elements from ads that were effective for a specific target demographic. Generative AI also learns from past advertising history and identifies patterns of success stories. For example, if an ad related to a specific season or event was successful, it can reflect those elements in a new ad. This maximizes the effectiveness of advertising by generating optimal ad content based on past success stories.

[0031] Generative AI can analyze a user's social media activity and generate advertising content based on the user's interests. For example, generative AI can analyze a user's social media posts to identify their interests. For example, it can generate advertising content based on topics and hashtags frequently mentioned by the user. Generative AI can also analyze the activities of a user's followers and friends on social media to generate advertisements aimed at groups with common interests. For example, it can create advertisements related to specific communities. Generative AI can also analyze a user's social media engagement data to generate advertisements that incorporate elements of posts that have generated the most positive responses. For example, it can use specific images or phrases. This allows for the generation of advertising content based on the user's interests, thereby providing effective advertising to the target demographic.

[0032] Generative AI can automatically generate ad content in different languages ​​to support international advertising campaigns. For example, generative AI can automatically generate ad content in multiple languages ​​based on user input. For example, it can create the same ad in English, Japanese, French, etc. Generative AI can also optimize ad content generated in different languages ​​to suit the culture and customs of each language. For example, it can incorporate expressions and designs preferred in specific language regions. Generative AI can also analyze ad performance data in each language to suggest optimal ad content to support international advertising campaigns. For example, it can adjust ads based on click-through rates and engagement rates in each country. This allows for effective support of international advertising campaigns by automatically generating ad content in different languages.

[0033] Generative AI can also automatically generate video ads and interactive ads, enabling multimedia advertising campaigns. For example, generative AI automatically generates video ads based on user input. For example, it creates product introduction videos and brand stories. To generate interactive ads, generative AI analyzes user input and incorporates elements that encourage users to take action on the ad. For example, it creates ads that include quizzes and surveys. Generative AI can also generate ads that combine still images, videos, and interactive elements to realize multimedia advertising campaigns. For example, it can embed clickable links within videos. This allows for the automatic generation of video ads and interactive ads, effectively realizing multimedia advertising campaigns.

[0034] The advertising medium search unit can analyze real-time traffic data and select a passenger vehicle that will maximize the exposure effect of an advertisement. For example, the advertising medium search unit uses a generation AI to collect real-time traffic data and select a passenger vehicle that travels on a route that will maximize the exposure effect of an advertisement. For example, the advertising medium search unit selects a passenger vehicle based on time periods and routes with heavy traffic. The advertising medium search unit also analyzes traffic data and identifies a passenger vehicle that will maximize the exposure effect of an advertisement in a specific area or time period. For example, the advertising medium search unit selects a passenger vehicle that travels on a route that is used by many people during rush hour. The advertising medium search unit also dynamically selects a passenger vehicle that will maximize the exposure effect of an advertisement based on real-time traffic data. For example, the advertising medium search unit reselects a passenger vehicle according to changes in traffic conditions. In this way, the effectiveness of an advertisement can be increased by analyzing real-time traffic data and selecting a passenger vehicle that will maximize the exposure effect of an advertisement.

[0035] The advertising medium search unit can analyze the social media activity of the car owner and select the car that is most suitable for the target demographic of the advertisement. In the advertising medium search unit, for example, the generation AI analyzes the social media activity of the car owner and selects the car that is most suitable for the target demographic of the advertisement. For example, if the owner is an influencer with many followers, that car is selected. The advertising medium search unit also identifies the car that is most suitable for the target demographic of the advertisement based on engagement data on social media. For example, it selects the car of an owner with many followers who share specific interests. In addition, the advertising medium search unit analyzes the social media activity of the owner using the generation AI and dynamically selects the car that is most suitable for the target demographic of the advertisement. For example, it selects the car when the owner is attending a specific event. In this way, by analyzing the social media activity of the car owner and selecting the car that is most suitable for the target demographic of the advertisement, the effectiveness of the advertisement can be increased.

[0036] The advertising media search unit can search for passenger car advertising media in different regions and countries to support global advertising campaigns. For example, the generation AI in the advertising media search unit searches for passenger car advertising media in different regions and countries to support global advertising campaigns. For example, it integrates advertising media databases from each country to select the most suitable passenger car. The advertising media search unit also analyzes advertising media data from different regions and countries to identify the most suitable passenger car for a global advertising campaign. For example, it selects a passenger car with high exposure in a specific region. The advertising media search unit also selects a passenger car suitable for a global advertising campaign by using the generation AI to consider the advertising regulations and culture of each country. For example, it selects a passenger car that complies with each country's advertising regulations. This allows the generation AI to search for passenger car advertising media in different regions and countries to effectively support global advertising campaigns.

[0037] The advertising medium search unit can search for transportation media other than passenger cars, thereby expanding the options for advertising media. For example, the generation AI in the advertising medium search unit searches for transportation media other than passenger cars (e.g., bicycles and scooters) to expand the options for advertising media. For example, it selects bicycles, which have a high advertising exposure effect in urban areas. The advertising medium search unit also analyzes a database of bicycles and scooters to identify the transportation media that is most suitable as an advertising medium. For example, it selects bicycles that are frequently used on specific routes. The generation AI in the advertising medium search unit can also dynamically select transportation media other than passenger cars, thereby expanding the options for advertising media. For example, it selects bicycles and scooters depending on specific events or seasons. This allows the search for transportation media other than passenger cars to expand the options for advertising media.

[0038] The contract automation unit can analyze past contract data and automatically generate optimal contract terms. For example, in the contract automation unit, the generation AI analyzes past advertising contract data and extracts the terms of successful contracts. For example, a new contract is generated based on contract terms that have proven highly effective in the past. The contract automation unit also has the generation AI propose optimal contract terms based on past contract data. For example, it sets effective contract terms for a specific target demographic or region. The contract automation unit also has the generation AI learn from past contract history and automatically generate optimal contract terms. For example, it optimizes contract periods and pricing based on past data. This makes it possible to analyze past contract data and automatically generate optimal contract terms, thereby increasing the effectiveness of contracts.

[0039] The contract automation department can analyze laws and regulations and automatically generate legally optimal contracts. For example, the generation AI in the contract automation department analyzes laws and regulations related to advertising contracts and automatically generates legally appropriate contracts. For example, it creates contracts that comply with each country's advertising regulations. The contract automation department also reflects changes in laws and regulations in real time, and the generation AI generates contracts based on the latest legal requirements. For example, contracts are updated immediately when new regulations come into effect. The contract automation department also uses the generation AI to propose contract terms that minimize legal risk and automatically generate legally optimal contracts. For example, it sets contract terms that are fair to both the advertiser and the advertising media. This minimizes the legal risk of contracts by analyzing laws and regulations and automatically generating legally optimal contracts.

[0040] The contract automation department can automatically generate contracts in different languages ​​to support international advertising contracts. For example, the generation AI automatically generates contracts in multiple languages ​​based on user input. For example, the same contract can be created in English, Japanese, French, etc. The contract automation department also optimizes contracts generated in different languages ​​to conform to the laws and regulations of each language. For example, it can reflect legal requirements in specific language areas. To support international advertising contracts, the generation AI analyzes contract performance data for each language and proposes optimal contract terms. For example, it can adjust the contract based on the contract success rate in each country. This allows the automatic generation of contracts in different languages ​​to effectively support international advertising contracts.

[0041] The contract automation department can integrate electronic signature functions and completely digitize contract procedures. For example, generation AI integrates electronic signature functions to completely digitize the process from contract creation to signing. For example, contracts are shared online and electronic signatures are obtained. The contract automation department also uses electronic signature functions to build a system that performs contract procedures quickly and efficiently. For example, it automatically sends a notification when a contract is signed. The contract automation department also uses generation AI to enhance the security of electronic signatures and perform contract procedures safely. For example, it authenticates signers and prevents fraudulent signatures. In this way, by integrating electronic signature functions and completely digitizing contract procedures, contract procedures can be performed quickly and efficiently.

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

[0043] The advertisement generation unit can also provide a customization function to maximize the effectiveness of advertisements based on the advertisement content generated by the generation AI. For example, it can provide an interface that allows advertisers to customize advertisements for specific target demographics. The advertisement generation unit can also have a function that allows advertisers to provide real-time feedback on the advertisement content generated by the generation AI and adjust the advertisement content based on that feedback. Furthermore, the advertisement generation unit can provide multiple variations of the advertisement content generated by the generation AI, allowing advertisers to select the optimal variation. This allows advertisers to create more effective advertisements based on the advertisement content generated by the generation AI.

[0044] When analyzing a user's past advertising history, the generation AI can also take into account data on the time and location of ad display. For example, it can incorporate elements of ads that were highly effective during specific times or locations into new ads. The generation AI can also analyze the cost-effectiveness of a user's past advertising campaigns and suggest optimal advertising budgets. Furthermore, the generation AI can identify successful patterns of ads related to specific seasons or events based on past advertising history and incorporate those elements into new ads. This allows for a multifaceted analysis of past advertising history to generate more effective advertising content.

[0045] When analyzing a user's social media activity, the generative AI can also take into account the interests of the user's followers and friends. For example, it can generate ad content based on topics and hashtags frequently mentioned by the user's followers. The generative AI can also generate ads that incorporate elements of posts that have generated the most positive responses based on the user's social media engagement data. Furthermore, the generative AI can analyze the user's social media activity patterns and generate ads related to specific times of day or events. This allows for a multifaceted analysis of the user's social media activity and the generation of more effective ad content.

[0046] Generative AI can automatically generate ad content in different languages ​​to support international advertising campaigns. For example, it can automatically generate ad content in multiple languages ​​based on user input. For example, it can create the same ad in English, Japanese, French, etc. Generative AI can also optimize ad content generated in different languages ​​to suit the culture and customs of each language. For example, it can incorporate expressions and designs preferred in specific language regions. Generative AI can also analyze ad performance data in each language to suggest optimal ad content to support international advertising campaigns. For example, it can adjust ads based on click-through rates and engagement rates in each country. This allows automatic generation of ad content in different languages ​​to effectively support international advertising campaigns.

[0047] Generative AI can also automatically generate video ads and interactive ads, enabling multimedia advertising campaigns. For example, it can automatically generate video ads based on user input. For example, it can create product introduction videos and brand stories. To generate interactive ads, generative AI can analyze user input and incorporate elements that encourage users to take action on the ad. For example, it can create ads that include quizzes and surveys. Generative AI can also generate ads that combine still images, videos, and interactive elements to realize multimedia advertising campaigns. For example, it can embed clickable links within videos. This makes it possible to effectively realize multimedia advertising campaigns by automatically generating video ads and interactive ads.

[0048] The advertising medium search unit can analyze real-time traffic data and select passenger vehicles that will maximize the advertising exposure effect. For example, the generation AI collects real-time traffic data and selects passenger vehicles that drive routes that will maximize the advertising exposure effect. For example, it selects passenger vehicles based on time periods and routes with heavy traffic. The advertising medium search unit also analyzes traffic data and identifies passenger vehicles that will maximize the advertising exposure effect in specific areas and time periods. For example, it selects passenger vehicles that drive routes that are used by many people during rush hour. The advertising medium search unit also dynamically selects passenger vehicles that will maximize the advertising exposure effect based on real-time traffic data. For example, it reselects passenger vehicles according to changes in traffic conditions. In this way, the effectiveness of advertising can be increased by analyzing real-time traffic data and selecting passenger vehicles that will maximize the advertising exposure effect.

[0049] The advertising medium search unit can analyze the social media activity of car owners and select the car that is best suited to the target demographic of the advertisement. For example, the generation AI analyzes the social media activity of car owners and selects the car that is best suited to the target demographic of the advertisement. For example, if the owner is an influencer with many followers, that car is selected. The advertising medium search unit also identifies the car that is best suited to the target demographic of the advertisement based on engagement data on social media. For example, it selects the car of an owner with many followers who share specific interests. The advertising medium search unit also analyzes the social media activity of owners and dynamically selects the car that is best suited to the target demographic of the advertisement. For example, it selects the car when the owner is attending a specific event. In this way, by analyzing the social media activity of car owners and selecting the car that is best suited to the target demographic of the advertisement, the effectiveness of the advertisement can be increased.

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

[0051] Step 1: Generative AI automatically generates ad content based on information provided by the user. For example, when a user enters information about the product or service they want to advertise, the AI ​​analyzes that information and generates attractive ad copy and design. Generative AI generates ad content using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The ad generation unit generates an ad based on the ad content generated by the generation AI. For example, the ad generation unit creates a text ad based on the ad copy generated by the generation AI. It can also create image ads or video ads based on the design generated by the generation AI. Step 3: The advertising media search unit searches for the optimal advertising media for placing the advertisement generated by the generation AI. For example, the advertising media search unit searches a database of passenger car advertising media nationwide to select the optimal passenger car for the advertisement's target demographic and region. The advertising media search unit can also analyze real-time traffic data and social media activity to select the optimal advertising media. Step 4: The contract automation unit automates the contract procedures with the advertising media selected by the advertising media search unit. For example, the contract automation unit automatically generates a contract for placing an advertisement and concludes a contract between the advertiser and the advertising media. The contract automation unit can also integrate an electronic signature function to completely digitize the contract procedures.

[0052] (Example 2) The digital advertisement automatic generation system according to an embodiment of the present invention is a platform that uses generation AI to automatically generate digital advertisements for private cars and enables advertisers to "search" for and "contract" with passenger car advertising media nationwide. As a result, the digital advertisement automatic generation system enables advertisers to efficiently create, distribute, and manage digital advertisements using private cars.

[0053] A digital advertisement automatic generation system according to an embodiment includes a generation AI, an advertisement generation unit, an advertising medium search unit, and a contract automation unit. The generation AI automatically generates advertisement content based on information provided by a user. For example, when a user inputs information about a product or service they want to advertise, the generation AI analyzes the information and generates attractive advertising copy and design. The generation AI generates advertisement content using a text generation AI (e.g., LLM) or a multimodal generation AI. The advertisement generation unit generates advertisements based on the advertisement content generated by the generation AI. For example, the advertisement generation unit creates text advertisements based on the advertisement copy generated by the generation AI. The advertisement generation unit can also create image advertisements or video advertisements based on the design generated by the generation AI. The advertising medium search unit searches for optimal advertising media for displaying advertisements generated by the generation AI. For example, the advertising medium search unit searches a database of passenger car advertising media nationwide to select a passenger car that is optimal for the advertisement's target demographic and region. The advertising medium search unit can also select optimal advertising media by analyzing real-time traffic data and social media activity. The contract automation unit automates contract procedures with advertising media selected by the advertising media search unit. For example, the contract automation unit automatically generates a contract for placing an advertisement and concludes a contract between the advertiser and the advertising media. The contract automation unit can also integrate an electronic signature function to completely digitize the contract procedures. As a result, the digital advertisement automatic generation system according to the embodiment enables advertisers to efficiently create, distribute, and manage digital advertisements using private cars. For example, advertisers can automatically generate attractive advertisements using generation AI, select optimal advertising media, and automate the contract procedures, thereby quickly and effectively distributing advertisements. In addition, the effectiveness of advertisements can be maximized by monitoring the display status of advertisements in real time.

[0054] Generative AI can analyze a user's past advertising history and generate optimal ad content based on past success stories. For example, generative AI analyzes a user's past advertising campaign data and extracts elements of successful ads. For example, it generates new ads based on the wording and design of ads that have recorded high click-through rates in the past. Generative AI also suggests optimal ad content based on performance data from ads the user has run in the past. For example, it incorporates elements from ads that were effective for a specific target demographic. Generative AI also learns from past advertising history and identifies patterns of success stories. For example, if an ad related to a specific season or event was successful, it can reflect those elements in a new ad. This maximizes the effectiveness of advertising by generating optimal ad content based on past success stories.

[0055] Generative AI can analyze a user's social media activity and generate advertising content based on the user's interests. For example, generative AI can analyze a user's social media posts to identify their interests. For example, it can generate advertising content based on topics and hashtags frequently mentioned by the user. Generative AI can also analyze the activities of a user's followers and friends on social media to generate advertisements aimed at groups with common interests. For example, it can create advertisements related to specific communities. Generative AI can also analyze a user's social media engagement data to generate advertisements that incorporate elements of posts that have generated the most positive responses. For example, it can use specific images or phrases. This allows for the generation of advertising content based on the user's interests, thereby providing effective advertising to the target demographic.

[0056] The generation AI can use its emotion estimation function to analyze the emotions of users when they enter ad content and generate ad content that elicits positive emotions. For example, the generation AI can analyze the user's facial expressions and voice when entering ad content and calculate an emotion score. For example, if the user is smiling when entering ad content, it can generate a positive ad that reflects that emotion. The generation AI can also use its emotion estimation function to provide real-time feedback on the ad content entered by the user and make suggestions to elicit positive emotions. For example, it can display encouraging messages. The generation AI can also suggest ad copy and designs that elicit positive emotions based on the user's emotion data. For example, it can generate ads that incorporate elements with a high emotion score. This allows the effectiveness of advertising to be increased by analyzing user emotions and generating ad content that elicits positive emotions.

[0057] Generative AI can automatically generate ad content in different languages ​​to support international advertising campaigns. For example, generative AI can automatically generate ad content in multiple languages ​​based on user input. For example, it can create the same ad in English, Japanese, French, etc. Generative AI can also optimize ad content generated in different languages ​​to suit the culture and customs of each language. For example, it can incorporate expressions and designs preferred in specific language regions. Generative AI can also analyze ad performance data in each language to suggest optimal ad content to support international advertising campaigns. For example, it can adjust ads based on click-through rates and engagement rates in each country. This allows for effective support of international advertising campaigns by automatically generating ad content in different languages.

[0058] Generative AI can also automatically generate video ads and interactive ads, enabling multimedia advertising campaigns. For example, generative AI automatically generates video ads based on user input. For example, it creates product introduction videos and brand stories. To generate interactive ads, generative AI analyzes user input and incorporates elements that encourage users to take action on the ad. For example, it creates ads that include quizzes and surveys. Generative AI can also generate ads that combine still images, videos, and interactive elements to realize multimedia advertising campaigns. For example, it can embed clickable links within videos. This allows for the automatic generation of video ads and interactive ads, effectively realizing multimedia advertising campaigns.

[0059] The generation AI can use its emotion estimation function to analyze the target user's emotional response to advertising content in real time and optimize the advertising content. For example, the generation AI uses its emotion estimation function to analyze the facial expressions and voice of the target user when viewing an advertisement and calculate an emotion score. For example, if the user is smiling while viewing an advertisement, the advertising content will be strengthened. The generation AI also optimizes the advertising content based on the emotion data collected in real time. For example, if there are many negative reactions, the advertising copy and design will be changed. The generation AI also analyzes the target user's emotional response and generates advertising content that elicits positive emotions. For example, it creates an advertisement that incorporates elements with a high emotion score. This allows the target user's emotional response to be analyzed in real time and the advertising content to be optimized, maximizing the effectiveness of the advertisement.

[0060] The advertising medium search unit can analyze real-time traffic data and select a passenger vehicle that will maximize the exposure effect of an advertisement. For example, the advertising medium search unit uses a generation AI to collect real-time traffic data and select a passenger vehicle that travels on a route that will maximize the exposure effect of an advertisement. For example, the advertising medium search unit selects a passenger vehicle based on time periods and routes with heavy traffic. The advertising medium search unit also analyzes traffic data and identifies a passenger vehicle that will maximize the exposure effect of an advertisement in a specific area or time period. For example, the advertising medium search unit selects a passenger vehicle that travels on a route that is used by many people during rush hour. The advertising medium search unit also dynamically selects a passenger vehicle that will maximize the exposure effect of an advertisement based on real-time traffic data. For example, the advertising medium search unit reselects a passenger vehicle according to changes in traffic conditions. In this way, the effectiveness of an advertisement can be increased by analyzing real-time traffic data and selecting a passenger vehicle that will maximize the exposure effect of an advertisement.

[0061] The advertising medium search unit can analyze the social media activity of the car owner and select the car that is most suitable for the target demographic of the advertisement. In the advertising medium search unit, for example, the generation AI analyzes the social media activity of the car owner and selects the car that is most suitable for the target demographic of the advertisement. For example, if the owner is an influencer with many followers, that car is selected. The advertising medium search unit also identifies the car that is most suitable for the target demographic of the advertisement based on engagement data on social media. For example, it selects the car of an owner with many followers who share specific interests. In addition, the advertising medium search unit analyzes the social media activity of the owner using the generation AI and dynamically selects the car that is most suitable for the target demographic of the advertisement. For example, it selects the car when the owner is attending a specific event. In this way, by analyzing the social media activity of the car owner and selecting the car that is most suitable for the target demographic of the advertisement, the effectiveness of the advertisement can be increased.

[0062] The advertising medium search unit can use the emotion estimation function to analyze the emotions of car owners and preferentially select cars owned by owners with positive emotions. The advertising medium search unit, for example, uses the emotion estimation function to analyze the emotions of car owners and select cars owned by owners with positive emotions. For example, if the owner has positive emotions toward an advertisement, the car is selected. The advertising medium search unit also builds a system that preferentially selects cars owned by owners with positive emotions based on the owner's emotion data. For example, if the owner shows high satisfaction with the advertisement, the car is selected. The advertising medium search unit also uses the emotion estimation function to analyze the owner's emotions in real time and dynamically select cars owned by owners with positive emotions. For example, if the owner provides positive feedback toward the advertisement, the car is selected. In this way, the effectiveness of the advertisement can be enhanced by preferentially selecting cars owned by owners with positive emotions using the emotion estimation function.

[0063] The advertising media search unit can search for passenger car advertising media in different regions and countries to support global advertising campaigns. For example, the generation AI in the advertising media search unit searches for passenger car advertising media in different regions and countries to support global advertising campaigns. For example, it integrates advertising media databases from each country to select the most suitable passenger car. The advertising media search unit also analyzes advertising media data from different regions and countries to identify the most suitable passenger car for a global advertising campaign. For example, it selects a passenger car with high exposure in a specific region. The advertising media search unit also selects a passenger car suitable for a global advertising campaign by using the generation AI to consider the advertising regulations and culture of each country. For example, it selects a passenger car that complies with each country's advertising regulations. This allows the generation AI to search for passenger car advertising media in different regions and countries to effectively support global advertising campaigns.

[0064] The advertising medium search unit can search for transportation media other than passenger cars, thereby expanding the options for advertising media. For example, the generation AI in the advertising medium search unit searches for transportation media other than passenger cars (e.g., bicycles and scooters) to expand the options for advertising media. For example, it selects bicycles, which have a high advertising exposure effect in urban areas. The advertising medium search unit also analyzes a database of bicycles and scooters to identify the transportation media that is most suitable as an advertising medium. For example, it selects bicycles that are frequently used on specific routes. The generation AI in the advertising medium search unit can also dynamically select transportation media other than passenger cars, thereby expanding the options for advertising media. For example, it selects bicycles and scooters depending on specific events or seasons. This allows the search for transportation media other than passenger cars to expand the options for advertising media.

[0065] The advertising medium search unit can use the emotion estimation function to analyze the emotional response of the target user to the advertising medium and select the optimal advertising medium. For example, the advertising medium search unit uses the emotion estimation function to analyze the emotion that the target user has toward the advertising medium and select the optimal advertising medium. For example, the advertising medium search unit selects a medium for which the user has positive emotions. The advertising medium search unit also builds a system that identifies the optimal advertising medium based on the emotion data of the target user. For example, the advertising medium search unit selects a medium for which the user shows high satisfaction. The advertising medium search unit also uses the emotion estimation function to analyze the emotional response of the target user in real time and dynamically select the optimal advertising medium. For example, if the user provides positive feedback on the advertising medium, the advertising medium search unit selects that medium. In this way, the effectiveness of advertising can be improved by using the emotion estimation function to analyze the emotional response of the target user and select the optimal advertising medium.

[0066] The contract automation unit can analyze past contract data and automatically generate optimal contract terms. For example, in the contract automation unit, the generation AI analyzes past advertising contract data and extracts the terms of successful contracts. For example, a new contract is generated based on contract terms that have proven highly effective in the past. The contract automation unit also has the generation AI propose optimal contract terms based on past contract data. For example, it sets effective contract terms for a specific target demographic or region. The contract automation unit also has the generation AI learn from past contract history and automatically generate optimal contract terms. For example, it optimizes contract periods and pricing based on past data. This makes it possible to analyze past contract data and automatically generate optimal contract terms, thereby increasing the effectiveness of contracts.

[0067] The contract automation department can analyze laws and regulations and automatically generate legally optimal contracts. For example, the generation AI in the contract automation department analyzes laws and regulations related to advertising contracts and automatically generates legally appropriate contracts. For example, it creates contracts that comply with each country's advertising regulations. The contract automation department also reflects changes in laws and regulations in real time, and the generation AI generates contracts based on the latest legal requirements. For example, contracts are updated immediately when new regulations come into effect. The contract automation department also uses the generation AI to propose contract terms that minimize legal risk and automatically generate legally optimal contracts. For example, it sets contract terms that are fair to both the advertiser and the advertising media. This minimizes the legal risk of contracts by analyzing laws and regulations and automatically generating legally optimal contracts.

[0068] The contract automation unit can use the emotion estimation function to analyze the emotions of both parties during contract negotiations and propose contract terms that elicit positive emotions. For example, the contract automation unit uses the emotion estimation function to analyze the emotions of advertisers and advertising media during contract negotiations in real time and propose contract terms that elicit positive emotions. For example, it finds conditions that satisfy both parties. The contract automation unit also makes specific proposals based on emotion data during contract negotiations to enable the generative AI to elicit positive emotions. For example, it proposes compromises when negotiations are proving difficult. The contract automation unit also uses the emotion estimation function to monitor changes in emotions during contract negotiations and dynamically adjust contract terms to maintain positive emotions. For example, it flexibly changes terms as the negotiations progress. This allows contract negotiations to proceed smoothly by using the emotion estimation function to analyze the emotions of both parties during contract negotiations and propose contract terms that elicit positive emotions.

[0069] The contract automation department can automatically generate contracts in different languages ​​to support international advertising contracts. For example, the generation AI automatically generates contracts in multiple languages ​​based on user input. For example, the same contract can be created in English, Japanese, French, etc. The contract automation department also optimizes contracts generated in different languages ​​to conform to the laws and regulations of each language. For example, it can reflect legal requirements in specific language areas. To support international advertising contracts, the generation AI analyzes contract performance data for each language and proposes optimal contract terms. For example, it can adjust the contract based on the contract success rate in each country. This allows the automatic generation of contracts in different languages ​​to effectively support international advertising contracts.

[0070] The contract automation department can integrate electronic signature functions and completely digitize contract procedures. For example, generation AI integrates electronic signature functions to completely digitize the process from contract creation to signing. For example, contracts are shared online and electronic signatures are obtained. The contract automation department also uses electronic signature functions to build a system that performs contract procedures quickly and efficiently. For example, it automatically sends a notification when a contract is signed. The contract automation department also uses generation AI to enhance the security of electronic signatures and perform contract procedures safely. For example, it authenticates signers and prevents fraudulent signatures. In this way, by integrating electronic signature functions and completely digitizing contract procedures, contract procedures can be performed quickly and efficiently.

[0071] The contract automation unit can use the emotion estimation function to analyze satisfaction after a contract is concluded and optimize future contract terms. For example, the contract automation unit uses the emotion estimation function to analyze satisfaction between advertisers and advertising media after a contract is concluded and optimize future contract terms. For example, it proposes a new contract based on terms with high satisfaction. The contract automation unit also makes specific proposals for the generative AI to improve satisfaction based on emotion data after a contract is concluded. For example, it identifies areas for improvement in contract terms. The contract automation unit also uses the emotion estimation function to monitor changes in emotions after a contract is concluded and dynamically adjust future contract terms. For example, it reviews terms if satisfaction declines. In this way, the effectiveness of the contract can be increased by using the emotion estimation function to analyze satisfaction after a contract is concluded and optimize future contract terms.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The advertisement generation unit can also provide a customization function to maximize the effectiveness of advertisements based on the advertisement content generated by the generation AI. For example, it can provide an interface that allows advertisers to customize advertisements for specific target demographics. The advertisement generation unit can also have a function that allows advertisers to provide real-time feedback on the advertisement content generated by the generation AI and adjust the advertisement content based on that feedback. Furthermore, the advertisement generation unit can provide multiple variations of the advertisement content generated by the generation AI, allowing advertisers to select the optimal variation. This allows advertisers to create more effective advertisements based on the advertisement content generated by the generation AI.

[0074] When analyzing a user's past advertising history, the generation AI can also take into account data on the time and location of ad display. For example, it can incorporate elements of ads that were highly effective during specific times or locations into new ads. The generation AI can also analyze the cost-effectiveness of a user's past advertising campaigns and suggest optimal advertising budgets. Furthermore, the generation AI can identify successful patterns of ads related to specific seasons or events based on past advertising history and incorporate those elements into new ads. This allows for a multifaceted analysis of past advertising history to generate more effective advertising content.

[0075] When analyzing a user's social media activity, the generative AI can also take into account the interests of the user's followers and friends. For example, it can generate ad content based on topics and hashtags frequently mentioned by the user's followers. The generative AI can also generate ads that incorporate elements of posts that have generated the most positive responses based on the user's social media engagement data. Furthermore, the generative AI can analyze the user's social media activity patterns and generate ads related to specific times of day or events. This allows for a multifaceted analysis of the user's social media activity and the generation of more effective ad content.

[0076] The generation AI can use its emotion estimation function to analyze the emotions of users when they enter ad content and generate ad content that elicits positive emotions. For example, it can analyze the user's facial expressions and voice when they enter content and calculate an emotion score. For example, if the user is smiling when entering content, it can generate a positive ad that reflects that emotion. The generation AI also uses its emotion estimation function to provide real-time feedback on the ad content entered by the user and make suggestions to elicit positive emotions. For example, it can display an encouraging message. The generation AI can also suggest ad copy and designs that elicit positive emotions based on the user's emotion data. For example, it can generate ads that incorporate elements with a high emotion score. This allows the effectiveness of advertising to be increased by analyzing user emotions and generating ad content that elicits positive emotions.

[0077] Generative AI can automatically generate ad content in different languages ​​to support international advertising campaigns. For example, it can automatically generate ad content in multiple languages ​​based on user input. For example, it can create the same ad in English, Japanese, French, etc. Generative AI can also optimize ad content generated in different languages ​​to suit the culture and customs of each language. For example, it can incorporate expressions and designs preferred in specific language regions. Generative AI can also analyze ad performance data in each language to suggest optimal ad content to support international advertising campaigns. For example, it can adjust ads based on click-through rates and engagement rates in each country. This allows automatic generation of ad content in different languages ​​to effectively support international advertising campaigns.

[0078] Generative AI can also automatically generate video ads and interactive ads, enabling multimedia advertising campaigns. For example, it can automatically generate video ads based on user input. For example, it can create product introduction videos and brand stories. To generate interactive ads, generative AI can analyze user input and incorporate elements that encourage users to take action on the ad. For example, it can create ads that include quizzes and surveys. Generative AI can also generate ads that combine still images, videos, and interactive elements to realize multimedia advertising campaigns. For example, it can embed clickable links within videos. This makes it possible to effectively realize multimedia advertising campaigns by automatically generating video ads and interactive ads.

[0079] Using its emotion estimation function, the generation AI can analyze the target user's emotional response to advertising content in real time and optimize it. For example, the emotion estimation function can be used to analyze the facial expressions and voice of the target user when viewing an advertisement and calculate an emotion score. For example, if the user is smiling while viewing an advertisement, the ad content can be strengthened. The generation AI can also optimize advertising content based on the emotion data collected in real time. For example, if there are a lot of negative reactions, the ad copy or design can be changed. The generation AI can also analyze the target user's emotional response and generate advertising content that elicits positive emotions. For example, it can create an advertisement that incorporates elements with a high emotion score. This allows the target user's emotional response to be analyzed in real time and the advertising content optimized, maximizing the effectiveness of the advertisement.

[0080] The advertising medium search unit can analyze real-time traffic data and select passenger vehicles that will maximize the advertising exposure effect. For example, the generation AI collects real-time traffic data and selects passenger vehicles that drive routes that will maximize the advertising exposure effect. For example, it selects passenger vehicles based on time periods and routes with heavy traffic. The advertising medium search unit also analyzes traffic data and identifies passenger vehicles that will maximize the advertising exposure effect in specific areas and time periods. For example, it selects passenger vehicles that drive routes that are used by many people during rush hour. The advertising medium search unit also dynamically selects passenger vehicles that will maximize the advertising exposure effect based on real-time traffic data. For example, it reselects passenger vehicles according to changes in traffic conditions. In this way, the effectiveness of advertising can be increased by analyzing real-time traffic data and selecting passenger vehicles that will maximize the advertising exposure effect.

[0081] The advertising medium search unit can analyze the social media activity of car owners and select the car that is best suited to the target demographic of the advertisement. For example, the generation AI analyzes the social media activity of car owners and selects the car that is best suited to the target demographic of the advertisement. For example, if the owner is an influencer with many followers, that car is selected. The advertising medium search unit also identifies the car that is best suited to the target demographic of the advertisement based on engagement data on social media. For example, it selects the car of an owner with many followers who share specific interests. The advertising medium search unit also analyzes the social media activity of owners and dynamically selects the car that is best suited to the target demographic of the advertisement. For example, it selects the car when the owner is attending a specific event. In this way, by analyzing the social media activity of car owners and selecting the car that is best suited to the target demographic of the advertisement, the effectiveness of the advertisement can be increased.

[0082] The advertising medium search unit can use the emotion estimation function to analyze the emotions of car owners and preferentially select cars owned by owners with positive emotions. For example, the emotion estimation function is used to analyze the emotions of car owners and select cars owned by owners with positive emotions. For example, if the owner has positive emotions toward an advertisement, that car is selected. The advertising medium search unit also builds a system that preferentially selects cars owned by owners with positive emotions based on the owner's emotion data. For example, if the owner shows high satisfaction with the advertisement, that car is selected. The advertising medium search unit also uses the emotion estimation function to analyze the owner's emotions in real time and dynamically select cars owned by owners with positive emotions. For example, if the owner provides positive feedback toward the advertisement, that car is selected. In this way, the effectiveness of the advertisement can be enhanced by using the emotion estimation function to preferentially select cars owned by owners with positive emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: Generative AI automatically generates ad content based on information provided by the user. For example, when a user enters information about the product or service they want to advertise, the AI ​​analyzes that information and generates attractive ad copy and design. Generative AI generates ad content using text generation AI (e.g., LLM) or multimodal generation AI. Step 2: The ad generation unit generates an ad based on the ad content generated by the generation AI. For example, the ad generation unit creates a text ad based on the ad copy generated by the generation AI. It can also create image ads or video ads based on the design generated by the generation AI. Step 3: The advertising media search unit searches for the optimal advertising media for placing the advertisement generated by the generation AI. For example, the advertising media search unit searches a database of passenger car advertising media nationwide to select the optimal passenger car for the advertisement's target demographic and region. The advertising media search unit can also analyze real-time traffic data and social media activity to select the optimal advertising media. Step 4: The contract automation unit automates the contract procedures with the advertising media selected by the advertising media search unit. For example, the contract automation unit automatically generates a contract for placing an advertisement and concludes a contract between the advertiser and the advertising media. The contract automation unit can also integrate an electronic signature function to completely digitize the contract procedures.

[0085] 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.

[0086] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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]

[0152] 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. Generative AI that automatically generates advertising content using generative AI, an advertisement generation unit that generates an advertisement based on the advertisement content generated by the generation AI; an advertising medium search unit that searches for an optimal advertising medium for placing the advertisement generated by the advertisement generation unit; a contract automation unit that automates contract procedures with the advertising media selected by the advertising media search unit. A system characterized by:

2. The generated AI is Analyzes the user's past advertising history and generates optimal advertising content based on past success stories 2. The system of claim 1.

3. The generated AI is Analyzing users' social media activity and generating advertising content based on the users' interests 2. The system of claim 1.

4. The generated AI is Analyzes the emotions users feel when entering ad content and generates ad content that elicits positive emotions 2. The system of claim 1.

5. The generated AI is Auto-generate advertising content in different languages ​​to support international advertising campaigns 2. The system of claim 1.

6. The generated AI is Automatically generate video ads and interactive ads to create multimedia advertising campaigns 2. The system of claim 1.

7. The generated AI is Analyzing the emotional response of the target user to the advertisement content in real time and optimizing the advertisement content.

2. The system of claim 1.

8. The advertising medium search unit Analyze real-time traffic data to select the vehicle that will maximize advertising exposure.

2. The system of claim 1.

Images