System

Abstract

An object of a system according to an embodiment is to distribute an advertisement at low cost by effectively utilizing an unpurchased advertisement frame of a local broadcasting station.SOLUTION: A system according to an embodiment includes an advertisement space purchase unit, an advertisement generation unit, and an advertisement distribution unit. An advertisement frame purchase part collectively purchases the unpurchased advertisement frames of the local broadcasting station. The advertisement generation unit generates an advertisement using the generation AI. The advertisement distribution unit distributes the advertisement generated by the advertisement generation unit to the advertisement frame of the local broadcasting station.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, there was a problem that there were many unpurchased advertising slots at local broadcasting stations, making it difficult for companies with limited advertising budgets to deliver effective advertisements.

[0005] The system according to the embodiment aims to effectively utilize unpurchased advertising space at local broadcasting stations and deliver advertisements at low cost. [Means for solving the problem]

[0006] The system according to the embodiment includes an advertising space purchasing unit, an advertising generation unit, and an advertising distribution unit. The advertising space purchasing unit purchases unpurchased advertising space from local broadcasting stations in bulk. The advertising generation unit generates advertisements using a generation AI. The advertising distribution unit distributes the advertisements generated by the advertising generation unit to the advertising space of the local broadcasting stations. [Effects of the Invention]

[0007] The system according to the embodiment can effectively utilize unpurchased advertising space at local broadcasting stations and deliver advertisements at low cost. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The advertisement distribution system according to an embodiment of the present invention is a system in which unpurchased advertisement slots of local broadcasting stations are purchased in bulk, and a generation AI creates and distributes advertisements at low cost. As a result, the advertisement distribution system can effectively utilize unpurchased advertisement slots of local broadcasting stations and distribute effective advertisements at low cost.

[0029] An advertising distribution system according to an embodiment includes an advertising space purchasing unit, an advertising generation unit, and an advertising distribution unit. The advertising space purchasing unit purchases all unpurchased advertising space from local broadcasting stations in bulk. For example, the advertising space purchasing unit purchases advertising space for which local broadcasting stations themselves accept inbound inquiries. The advertising space purchasing unit uses a generation AI to analyze the viewer demographics and viewing time periods for each advertising space and selects the most suitable advertising space. For example, the unit identifies time periods that are popular with specific age groups or genders and prioritizes purchasing those slots. The system collects viewer behavior data, and the generation AI analyzes the data to select advertising space based on the viewer's interests. For example, the system identifies time periods when many viewers prefer sports programs. Based on past viewing data, the generation AI analyzes viewer viewing patterns and selects the most effective advertising space. For example, the system purchases advertising space during time periods with high viewership or for specific programs. The advertising generation unit generates advertisements using the generation AI. For example, the generation AI generates advertisements based on prompts from advertisers. The generation AI generates advertisements using text generation AI (e.g., LLM). Generative AI can also generate ad content using multimodal generative AI. Generative AI analyzes ad content and automatically generates ads that combine still images and background music. For example, if a user inputs the prompt, "Please create an ad introducing a new product," the generative AI generates an ad that combines product images, product descriptions, and appropriate background music. The ad distribution unit distributes the ads generated by the ad generation unit to local broadcasting stations' ad slots. For example, the ad generated by the generative AI is distributed to local broadcasting stations' ad slots that have been purchased. The ad distribution unit optimizes the timing and frequency of ad distribution to maximize viewer response. For example, it distributes ads during times with high viewership or before and after specific programs. It also optimizes the frequency of ad distribution to maximize viewer response. For example, it distributes ads at appropriate intervals rather than distributing the same ad multiple times in a short period of time. Based on viewer behavior data, the generative AI adjusts the timing and frequency of ad distribution. For example, it distributes ads during times when viewers are most concentrated. As a result, the advertisement distribution system according to the embodiment can effectively utilize unpurchased advertisement slots of local broadcasting stations and distribute effective advertisements at low cost.For example, small and medium-sized businesses in rural areas can use generative AI to create advertisements at low cost and broadcast them on local broadcasting stations across the country. By analyzing the effectiveness of the advertisements and reflecting the results in the next advertisement, the effectiveness of the advertisements can be continuously improved.

[0030] The advertisement generation unit can generate advertisements based on the advertiser's prompts. The advertisement generation unit generates advertisements based on the advertiser's prompts, for example, using a generation AI. For example, when a prompt such as "Please create an advertisement introducing a new product" is input, the generation AI generates an advertisement that combines product images, descriptions, and appropriate background music. The generation AI generates advertisements using text generation AI (e.g., LLM). The generation AI can also generate advertisement content using a multimodal generation AI. The generation AI analyzes the advertisement content and automatically generates an advertisement that combines still images and background music. This makes it possible to generate customized advertisements based on the advertiser's instructions.

[0031] The ad slot purchasing unit can analyze the viewer demographics or viewing time slots for each ad slot and select the most suitable ad slot. For example, the ad slot purchasing unit uses generation AI to analyze the viewer demographics and viewing time slots of each local broadcasting station and select the most suitable ad slot. For example, it identifies time slots that are popular with specific age groups or genders and prioritizes purchasing those slots. It collects viewer behavior data and has generation AI analyze that data to select ad slots based on viewer interests. For example, it identifies time slots where many viewers prefer sports programs. Based on past viewing data, generation AI analyzes viewer viewing patterns and selects the most effective ad slot. For example, it purchases ad slots during time slots with high viewership or to coincide with specific programs. By selecting the most suitable ad slot, it is possible to maximize the effectiveness of advertising.

[0032] The ad space purchasing unit can analyze past advertising effectiveness data and select highly effective ad spaces. For example, the ad space purchasing unit collects past advertising effectiveness data, and the generation AI analyzes that data to identify highly effective ad spaces. For example, it selects ad spaces based on time slots and programs that received good viewer responses. Based on the advertising effectiveness data, the generation AI analyzes ad viewer ratings and click rates, and prioritizes the purchase of the most effective ad spaces. For example, it selects spaces that have recorded high click rates in the past. The generation AI analyzes past advertising effectiveness data and develops an algorithm to predict highly effective ad spaces. For example, it identifies highly effective spaces based on viewer behavior patterns. This makes it possible to select highly effective ad spaces based on past advertising effectiveness data.

[0033] The ad delivery unit can optimize the timing or frequency of ad delivery. The ad delivery unit, for example, uses generation AI to optimize the timing of ad delivery. For example, it delivers ads during times when viewership is high or before and after specific programs. It optimizes the frequency of ad delivery to maximize viewer response. For example, instead of delivering the same ad multiple times in a short period of time, it delivers it at appropriate intervals. Based on viewer behavior data, the generation AI adjusts the timing and frequency of ad delivery. For example, it delivers ads during times when viewers are most concentrated. This makes it possible to optimize the timing and frequency of ad delivery.

[0034] The ad delivery unit can instantly analyze viewing data after ad delivery and reflect it in the next delivery. In the ad delivery unit, for example, the generation AI analyzes viewing data after ad delivery in real time and reflects it in the next ad delivery. For example, it identifies time slots and programs that received a good viewer response. Based on the viewing data, the generation AI evaluates the effectiveness of the ad and optimizes the next delivery strategy. For example, it prioritizes selecting time slots with high viewer ratings. A system is built that analyzes viewing data in real time and has the generation AI reflect the results in the next ad delivery. For example, it adjusts the timing of ad delivery based on viewer behavior patterns. This makes it possible to analyze viewing data after ad delivery in real time and reflect it in the next delivery.

[0035] The ad distribution unit can distribute different versions of ads to each region and develop ads tailored to regional characteristics. The ad distribution unit, for example, uses generation AI to generate different versions of ads for each region and develop ads tailored to regional characteristics. For example, it generates ads tailored to the culture and language of the region. Based on viewer data for each region, the generation AI generates ads tailored to regional characteristics. For example, it generates ads tailored to regional events and trends. A system is built that distributes different versions of ads to each region and evaluates the effectiveness of the ads based on viewer responses. For example, it analyzes viewer ratings and emotional responses for each region. This allows different versions of ads to be distributed to each region and develop ads tailored to regional characteristics.

[0036] The advertisement generation unit can automatically generate customized advertisements that match the advertiser's brand image or target demographic. The advertisement generation unit, for example, uses generation AI to automatically generate customized advertisements that match the advertiser's brand image. For example, it generates advertisements that incorporate brand colors and logos. The generation AI automatically generates advertisements that match the target demographic. For example, it generates pop designs for young people and subdued designs for older people. The generation AI customizes the content of the advertisement based on the advertiser's brand image and target demographic. For example, it generates advertisements that emphasize the features and benefits of a product. This makes it possible to automatically generate customized advertisements that match the advertiser's brand image and target demographic.

[0037] The ad generation unit can analyze past advertising data and generate advertisements that incorporate highly effective elements. For example, the ad generation unit uses a generation AI to analyze past advertising data and generate advertisements that incorporate highly effective elements. For example, it can incorporate designs and messages that have recorded high click-through rates. Based on advertising effectiveness data, the generation AI extracts highly effective elements and reflects them in the advertisement. For example, it can generate advertisements that incorporate elements that have received a good response from viewers. The generation AI analyzes past advertising data and automatically generates advertisements that combine highly effective elements. For example, it can incorporate elements from successful advertising campaigns. This makes it possible to analyze past advertising data and generate advertisements that incorporate highly effective elements.

[0038] The ad generation unit can simultaneously generate not only video ads but also radio ads or internet banner ads. The ad generation unit, for example, uses generation AI to simultaneously generate not only video ads but also radio ads. For example, the same message is delivered as an audio ad. Internet banner ads are also generated simultaneously, and advertising is carried out both online and offline. For example, banner ads linked to television ads are posted on a website. A cross-media strategy is used to carry out video ads, radio ads, and internet banner ads with a consistent message. For example, the same campaign is carried out simultaneously in different media. This allows not only video ads but also radio ads and internet banner ads to be generated simultaneously.

[0039] The ad generation unit works in conjunction with the advertiser's social media accounts and can simultaneously generate advertising content for social media. The ad generation unit, for example, uses generation AI to work in conjunction with the advertiser's social media accounts and automatically generate advertising content for social media. For example, it generates short videos and images for Instagram and Twitter. The generation AI automatically generates advertising content tailored to the characteristics of the SNS. For example, it generates long-form ads for Facebook and short video ads for TikTok. In conjunction with the advertiser's social media accounts, the generation AI automatically generates advertising campaigns for social media. For example, it generates hashtag campaigns on social media and collaborative ads with influencers. This allows the unit to work in conjunction with the advertiser's social media accounts and simultaneously generate advertising content for social media.

[0040] The advertising space purchasing unit can simultaneously purchase radio or internet advertising space to carry out cross-media advertising. For example, the advertising space purchasing unit purchases radio advertising space at the same time as local broadcast station advertising space to carry out cross-media advertising. For example, the same advertisement is broadcast simultaneously on television and radio. Internet advertising space can also be purchased at the same time to carry out online and offline advertising. For example, banner advertisements linked to television advertisements are displayed on a website. A cross-media strategy is used to carry out a consistent advertising campaign across television, radio, and internet media. For example, the same message is conveyed through different media. This allows radio and internet advertising space to be purchased at the same time to carry out cross-media advertising.

[0041] The ad space purchasing unit can purchase ad space specialized for specific content, such as local events or sports broadcasts. The ad space purchasing unit, for example, purchases ad space specialized for local events and distributes advertisements as a sponsor of the event. For example, it selects ad space for local festivals and festivals. It purchases ad space specialized for sports broadcasts and distributes advertisements aimed at sports fans. For example, it selects ad space for broadcasts of local high school baseball or soccer games. It purchases ad space specialized for specific content and distributes advertisements tailored to the interests of viewers. For example, it selects ad space for cooking shows or travel shows. This makes it possible to purchase ad space specialized for specific content, such as local events or sports broadcasts.

[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 advertising delivery system can further include an advertising effectiveness analysis unit. The advertising effectiveness analysis unit collects viewing data after the advertisement is delivered, and the generation AI analyzes that data to evaluate the effectiveness of the advertisement. For example, the effectiveness of the advertisement is measured based on the viewer's click rate and viewing time. The advertising effectiveness analysis unit can analyze the effectiveness of the advertisement in real time and reflect this in the next advertisement delivery. For example, it can identify time periods and programs that received a good response from viewers and optimize the next advertisement delivery strategy. This allows the advertising delivery system to continuously improve the effectiveness of the advertisement.

[0044] The ad generation unit works in conjunction with the advertiser's social media accounts to simultaneously generate advertising content for social media. For example, the generation AI is used to generate short videos and images for Instagram and Twitter. The generation AI automatically generates advertising content tailored to the characteristics of each social media platform. For example, it generates long-form ads for Facebook and short video ads for TikTok. In conjunction with the advertiser's social media accounts, the generation AI automatically generates advertising campaigns for social media. For example, it generates hashtag campaigns on social media and collaborative ads with influencers. This allows the unit to work in conjunction with the advertiser's social media accounts to simultaneously generate advertising content for social media.

[0045] The advertising space purchasing department can simultaneously purchase radio or internet advertising space to carry out cross-media advertising. For example, radio advertising space can be purchased at the same time as advertising space on local broadcasting stations to carry out cross-media advertising. For example, the same advertisement can be broadcast simultaneously on television and radio. Internet advertising space can also be purchased at the same time to carry out online and offline advertising. For example, banner advertisements linked to television advertisements can be displayed on a website. A cross-media strategy can be used to carry out a consistent advertising campaign across television, radio, and the internet. For example, the same message can be conveyed through different media. This allows radio and internet advertising space to be purchased at the same time to carry out cross-media advertising.

[0046] The ad space purchasing unit can purchase ad space specialized for specific content, such as local events or sports broadcasts. For example, it purchases ad space specialized for local events and distributes advertisements as a sponsor of the event. For example, it selects ad space for local festivals and festivals. It purchases ad space specialized for sports broadcasts and distributes advertisements aimed at sports fans. For example, it selects ad space for broadcasts of local high school baseball or soccer games. It purchases ad space specialized for specific content and distributes advertisements tailored to the interests of viewers. For example, it selects ad space for cooking shows or travel shows. This makes it possible to purchase ad space specialized for specific content, such as local events or sports broadcasts.

[0047] The ad generation unit can simultaneously generate not only video ads, but also radio ads or internet banner ads. For example, using generation AI, not only video ads but also radio ads can be generated at the same time. For example, the same message can be delivered as an audio ad. Internet banner ads can also be generated at the same time, and advertising can be deployed both online and offline. For example, banner ads linked to television ads can be posted on a website. A cross-media strategy can be used to deploy video ads, radio ads, and internet banner ads with a consistent message. For example, the same campaign can be run simultaneously across different media. This allows not only video ads but also radio ads and internet banner ads to be generated at the same time.

[0048] The ad generation unit can analyze past advertising data and generate advertisements that incorporate highly effective elements. For example, the generation AI analyzes past advertising data and generates advertisements that incorporate highly effective elements. For example, it incorporates designs and messages that have recorded high click-through rates. Based on advertising effectiveness data, the generation AI extracts highly effective elements and reflects them in the advertisement. For example, it generates advertisements that incorporate elements that have received a good response from viewers. The generation AI analyzes past advertising data and automatically generates advertisements that combine highly effective elements. For example, it incorporates elements from successful advertising campaigns. This makes it possible to analyze past advertising data and generate advertisements that incorporate highly effective elements.

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

[0050] Step 1: The ad space purchasing unit purchases all unpurchased ad space from local broadcasters in bulk. For example, it targets ad space for which local broadcasters themselves accept inbound orders. The ad space purchasing unit uses generation AI to analyze the viewer demographics and viewing time slots for each ad space and selects the most suitable ad space. For example, it identifies time slots that are popular with specific age groups or genders and prioritizes purchasing those slots. It collects viewer behavior data, and the generation AI analyzes that data to select ad space based on viewer interests. For example, it identifies time slots where many viewers prefer sports programs. Based on past viewing data, the generation AI analyzes viewer viewing patterns and selects the most effective ad space. For example, it purchases ad space during time slots with high viewership or to coincide with specific programs. Step 2: The ad generation unit generates an advertisement using the generation AI. For example, the generation AI generates an advertisement based on the advertiser's prompt. The generation AI generates an advertisement using a text generation AI (e.g., LLM). The generation AI can also generate advertisement content using a multimodal generation AI. The generation AI analyzes the advertisement content and automatically generates an advertisement that combines still images and background music. For example, if the prompt is "Please create an advertisement to introduce a new product," the generation AI generates an advertisement that combines product images, descriptions, and appropriate background music. Step 3: The ad distribution unit distributes the ads generated by the ad generation unit to local broadcasting station ad slots. For example, the ad generated by the generation AI is distributed to the local broadcasting station's purchased ad slots. The ad distribution unit optimizes the timing and frequency of ad distribution to maximize viewer response. For example, it distributes ads during times with high viewership or before and after specific programs. It optimizes the frequency of ad distribution to maximize viewer response. For example, rather than distributing the same ad multiple times in a short period of time, it distributes ads at appropriate intervals. Based on viewer behavior data, the generation AI adjusts the timing and frequency of ad distribution. For example, it distributes ads during times when viewers are most concentrated.

[0051] (Example 2) The advertisement distribution system according to an embodiment of the present invention is a system in which unpurchased advertisement slots of local broadcasting stations are purchased in bulk, and a generation AI creates and distributes advertisements at low cost. As a result, the advertisement distribution system can effectively utilize unpurchased advertisement slots of local broadcasting stations and distribute effective advertisements at low cost.

[0052] An advertising distribution system according to an embodiment includes an advertising space purchasing unit, an advertising generation unit, and an advertising distribution unit. The advertising space purchasing unit purchases all unpurchased advertising space from local broadcasting stations in bulk. For example, the advertising space purchasing unit purchases advertising space for which local broadcasting stations themselves accept inbound inquiries. The advertising space purchasing unit uses a generation AI to analyze the viewer demographics and viewing time periods for each advertising space and selects the most suitable advertising space. For example, the unit identifies time periods that are popular with specific age groups or genders and prioritizes purchasing those slots. The system collects viewer behavior data, and the generation AI analyzes the data to select advertising space based on the viewer's interests. For example, the system identifies time periods when many viewers prefer sports programs. Based on past viewing data, the generation AI analyzes viewer viewing patterns and selects the most effective advertising space. For example, the system purchases advertising space during time periods with high viewership or for specific programs. The advertising generation unit generates advertisements using the generation AI. For example, the generation AI generates advertisements based on prompts from advertisers. The generation AI generates advertisements using text generation AI (e.g., LLM). Generative AI can also generate ad content using multimodal generative AI. Generative AI analyzes ad content and automatically generates ads that combine still images and background music. For example, if a user inputs the prompt, "Please create an ad introducing a new product," the generative AI generates an ad that combines product images, product descriptions, and appropriate background music. The ad distribution unit distributes the ads generated by the ad generation unit to local broadcasting stations' ad slots. For example, the ad generated by the generative AI is distributed to local broadcasting stations' ad slots that have been purchased. The ad distribution unit optimizes the timing and frequency of ad distribution to maximize viewer response. For example, it distributes ads during times with high viewership or before and after specific programs. It also optimizes the frequency of ad distribution to maximize viewer response. For example, it distributes ads at appropriate intervals rather than distributing the same ad multiple times in a short period of time. Based on viewer behavior data, the generative AI adjusts the timing and frequency of ad distribution. For example, it distributes ads during times when viewers are most concentrated. As a result, the advertisement distribution system according to the embodiment can effectively utilize unpurchased advertisement slots of local broadcasting stations and distribute effective advertisements at low cost.For example, small and medium-sized businesses in rural areas can use generative AI to create advertisements at low cost and broadcast them on local broadcasting stations across the country. By analyzing the effectiveness of the advertisements and reflecting the results in the next advertisement, the effectiveness of the advertisements can be continuously improved.

[0053] The advertisement generation unit can generate advertisements based on the advertiser's prompts. The advertisement generation unit generates advertisements based on the advertiser's prompts, for example, using a generation AI. For example, when a prompt such as "Please create an advertisement introducing a new product" is input, the generation AI generates an advertisement that combines product images, descriptions, and appropriate background music. The generation AI generates advertisements using text generation AI (e.g., LLM). The generation AI can also generate advertisement content using a multimodal generation AI. The generation AI analyzes the advertisement content and automatically generates an advertisement that combines still images and background music. This makes it possible to generate customized advertisements based on the advertiser's instructions.

[0054] The advertisement generation unit can analyze viewer emotional data and generate advertisements that incorporate elements that appeal to emotions. The advertisement generation unit, for example, uses an emotion estimation function to analyze viewer emotional data and generate advertisements that incorporate elements that appeal to emotions. For example, it generates advertisements that include moving stories and heartwarming messages. It collects viewer emotional data, and the generation AI analyzes the data to generate advertisements that incorporate elements that appeal to emotions. For example, it generates advertisements that include humor that will make the viewer smile. It uses the emotion estimation function to build a system that customizes advertisements based on the viewer's emotional score. For example, it generates advertisements that incorporate elements with a high emotional score. This makes it possible to generate advertisements that appeal to the viewer's emotions.

[0055] The ad slot purchasing unit can analyze the viewer demographics or viewing time slots for each ad slot and select the most suitable ad slot. For example, the ad slot purchasing unit uses generation AI to analyze the viewer demographics and viewing time slots of each local broadcasting station and select the most suitable ad slot. For example, it identifies time slots that are popular with specific age groups or genders and prioritizes purchasing those slots. It collects viewer behavior data and has generation AI analyze that data to select ad slots based on viewer interests. For example, it identifies time slots where many viewers prefer sports programs. Based on past viewing data, generation AI analyzes viewer viewing patterns and selects the most effective ad slot. For example, it purchases ad slots during time slots with high viewership or to coincide with specific programs. By selecting the most suitable ad slot, it is possible to maximize the effectiveness of advertising.

[0056] The ad space purchasing unit can analyze past advertising effectiveness data and select highly effective ad spaces. For example, the ad space purchasing unit collects past advertising effectiveness data, and the generation AI analyzes that data to identify highly effective ad spaces. For example, it selects ad spaces based on time slots and programs that received good viewer responses. Based on the advertising effectiveness data, the generation AI analyzes ad viewer ratings and click rates, and prioritizes the purchase of the most effective ad spaces. For example, it selects spaces that have recorded high click rates in the past. The generation AI analyzes past advertising effectiveness data and develops an algorithm to predict highly effective ad spaces. For example, it identifies highly effective spaces based on viewer behavior patterns. This makes it possible to select highly effective ad spaces based on past advertising effectiveness data.

[0057] The ad space purchasing unit can use the emotion estimation function to select ad space that elicits a positive emotional response from viewers. For example, the ad space purchasing unit uses the emotion estimation function to analyze viewers' emotional responses and identify ad space that elicits a high number of positive responses. For example, it selects time periods when viewers feel joy or excitement. It collects viewer emotion data, and the generation AI analyzes that data to prioritize the purchase of ad space that elicits a positive emotional response. For example, it selects ad space for programs that make viewers smile. It uses the emotion estimation function to build a system that selects ad space based on viewers' emotion scores. For example, it identifies time periods and programs with high emotion scores and purchases those slots. This makes it possible to select ad space that elicits a positive emotional response from viewers.

[0058] The ad delivery unit can optimize the timing or frequency of ad delivery. The ad delivery unit, for example, uses generation AI to optimize the timing of ad delivery. For example, it delivers ads during times when viewership is high or before and after specific programs. It optimizes the frequency of ad delivery to maximize viewer response. For example, instead of delivering the same ad multiple times in a short period of time, it delivers it at appropriate intervals. Based on viewer behavior data, the generation AI adjusts the timing and frequency of ad delivery. For example, it delivers ads during times when viewers are most concentrated. This makes it possible to optimize the timing and frequency of ad delivery.

[0059] The ad delivery unit can instantly analyze viewing data after ad delivery and reflect it in the next delivery. In the ad delivery unit, for example, the generation AI analyzes viewing data after ad delivery in real time and reflects it in the next ad delivery. For example, it identifies time slots and programs that received a good viewer response. Based on the viewing data, the generation AI evaluates the effectiveness of the ad and optimizes the next delivery strategy. For example, it prioritizes selecting time slots with high viewer ratings. A system is built that analyzes viewing data in real time and has the generation AI reflect the results in the next ad delivery. For example, it adjusts the timing of ad delivery based on viewer behavior patterns. This makes it possible to analyze viewing data after ad delivery in real time and reflect it in the next delivery.

[0060] The ad delivery unit can instantly monitor viewers' emotional responses and evaluate the effectiveness of ad delivery. The ad delivery unit, for example, uses an emotion estimation function to monitor viewers' emotional responses in real time and evaluate the effectiveness of ad delivery. For example, it identifies the moment when the viewer smiles. Viewer emotional data is collected and the generation AI analyzes the data to evaluate the effectiveness of ad delivery. For example, it measures the effectiveness of ads based on the viewer's emotional score. It builds a system that monitors viewers' emotional responses in real time and allows the generation AI to evaluate the effectiveness of ad delivery. For example, it identifies time periods and programs with high emotional scores. This makes it possible to monitor viewers' emotional responses in real time and evaluate the effectiveness of ad delivery.

[0061] The ad distribution unit can distribute different versions of ads to each region and develop ads tailored to regional characteristics. The ad distribution unit, for example, uses generation AI to generate different versions of ads for each region and develop ads tailored to regional characteristics. For example, it generates ads tailored to the culture and language of the region. Based on viewer data for each region, the generation AI generates ads tailored to regional characteristics. For example, it generates ads tailored to regional events and trends. A system is built that distributes different versions of ads to each region and evaluates the effectiveness of the ads based on viewer responses. For example, it analyzes viewer ratings and emotional responses for each region. This allows different versions of ads to be distributed to each region and develop ads tailored to regional characteristics.

[0062] The ad delivery unit can adjust the timing of ad delivery based on the viewer's emotions and deliver the ad at an appropriate time. The ad delivery unit, for example, uses an emotion estimation function to adjust the timing of ad delivery based on the viewer's emotions. For example, deliver ads during times when the viewer is most relaxed. Viewer emotion data is collected, and a generation AI analyzes the data to identify the optimal delivery timing. For example, deliver ads during times when viewers are most concentrated. A system is built that uses the emotion estimation function to adjust the timing of ad delivery based on the viewer's emotion score. For example, deliver ads during times when the emotion score is high. This allows the timing of ad delivery to be adjusted based on the viewer's emotions and delivered at the optimal time.

[0063] The advertisement generation unit can automatically generate customized advertisements that match the advertiser's brand image or target demographic. The advertisement generation unit, for example, uses generation AI to automatically generate customized advertisements that match the advertiser's brand image. For example, it generates advertisements that incorporate brand colors and logos. The generation AI automatically generates advertisements that match the target demographic. For example, it generates pop designs for young people and subdued designs for older people. The generation AI customizes the content of the advertisement based on the advertiser's brand image and target demographic. For example, it generates advertisements that emphasize the features and benefits of a product. This makes it possible to automatically generate customized advertisements that match the advertiser's brand image and target demographic.

[0064] The ad generation unit can analyze past advertising data and generate advertisements that incorporate highly effective elements. For example, the ad generation unit uses a generation AI to analyze past advertising data and generate advertisements that incorporate highly effective elements. For example, it can incorporate designs and messages that have recorded high click-through rates. Based on advertising effectiveness data, the generation AI extracts highly effective elements and reflects them in the advertisement. For example, it can generate advertisements that incorporate elements that have received a good response from viewers. The generation AI analyzes past advertising data and automatically generates advertisements that combine highly effective elements. For example, it can incorporate elements from successful advertising campaigns. This makes it possible to analyze past advertising data and generate advertisements that incorporate highly effective elements.

[0065] The advertisement generation unit can use the emotion estimation function to generate advertisements that incorporate elements that appeal to the viewer's emotions. The advertisement generation unit, for example, uses the emotion estimation function to generate advertisements that incorporate elements that appeal to the viewer's emotions. For example, it generates advertisements that include moving stories and heartwarming messages. It collects viewer emotion data, and the generation AI analyzes the data to generate advertisements that incorporate elements that appeal to the emotions. For example, it generates advertisements that include humor that will make the viewer smile. It uses the emotion estimation function to build a system that customizes advertisements based on the viewer's emotion score. For example, it generates advertisements that incorporate elements that have a high emotion score. This makes it possible to generate advertisements that incorporate elements that appeal to the viewer's emotions.

[0066] The ad generation unit can simultaneously generate not only video ads but also radio ads or internet banner ads. The ad generation unit, for example, uses generation AI to simultaneously generate not only video ads but also radio ads. For example, the same message is delivered as an audio ad. Internet banner ads are also generated simultaneously, and advertising is carried out both online and offline. For example, banner ads linked to television ads are posted on a website. A cross-media strategy is used to carry out video ads, radio ads, and internet banner ads with a consistent message. For example, the same campaign is carried out simultaneously in different media. This allows not only video ads but also radio ads and internet banner ads to be generated simultaneously.

[0067] The ad generation unit works in conjunction with the advertiser's social media accounts and can simultaneously generate advertising content for social media. The ad generation unit, for example, uses generation AI to work in conjunction with the advertiser's social media accounts and automatically generate advertising content for social media. For example, it generates short videos and images for Instagram and Twitter. The generation AI automatically generates advertising content tailored to the characteristics of the SNS. For example, it generates long-form ads for Facebook and short video ads for TikTok. In conjunction with the advertiser's social media accounts, the generation AI automatically generates advertising campaigns for social media. For example, it generates hashtag campaigns on social media and collaborative ads with influencers. This allows the unit to work in conjunction with the advertiser's social media accounts and simultaneously generate advertising content for social media.

[0068] The advertisement generation unit can generate multiple advertisement variations based on the viewer's emotions and select the appropriate one. The advertisement generation unit uses, for example, an emotion estimation function to have a generation AI generate multiple advertisement variations based on the viewer's emotions. For example, it generates an emotional version or a version that includes humor. The generation AI analyzes the viewer's emotional data and generates advertisement variations based on emotions. For example, it generates different versions of an advertisement according to the viewer's emotional score. A system is built that generates multiple advertisement variations and selects the most appropriate one based on the viewer's emotional response. For example, it selects the version with the highest emotional score. This allows multiple advertisement variations to be generated based on the viewer's emotions and the most appropriate one to be selected.

[0069] The advertising space purchasing unit can simultaneously purchase radio or internet advertising space to carry out cross-media advertising. For example, the advertising space purchasing unit purchases radio advertising space at the same time as local broadcast station advertising space to carry out cross-media advertising. For example, the same advertisement is broadcast simultaneously on television and radio. Internet advertising space can also be purchased at the same time to carry out online and offline advertising. For example, banner advertisements linked to television advertisements are displayed on a website. A cross-media strategy is used to carry out a consistent advertising campaign across television, radio, and internet media. For example, the same message is conveyed through different media. This allows radio and internet advertising space to be purchased at the same time to carry out cross-media advertising.

[0070] The ad space purchasing unit can purchase ad space specialized for specific content, such as local events or sports broadcasts. The ad space purchasing unit, for example, purchases ad space specialized for local events and distributes advertisements as a sponsor of the event. For example, it selects ad space for local festivals and festivals. It purchases ad space specialized for sports broadcasts and distributes advertisements aimed at sports fans. For example, it selects ad space for broadcasts of local high school baseball or soccer games. It purchases ad space specialized for specific content and distributes advertisements tailored to the interests of viewers. For example, it selects ad space for cooking shows or travel shows. This makes it possible to purchase ad space specialized for specific content, such as local events or sports broadcasts.

[0071] The advertising space purchasing unit can identify specific programs or time slots that will heighten viewer emotions and purchase those slots. The advertising space purchasing unit can, for example, use an emotion estimation function to identify specific programs or time slots that will heighten viewer emotions and purchase those slots. For example, it selects time slots for dramas or movies that will move viewers. Viewer emotion data is collected, and a generation AI analyzes the data to identify time slots that will heighten emotions. For example, it selects time slots for sports broadcasts that will excite viewers. Using the emotion estimation function, a system can be built that selects advertising slots based on viewer emotion scores. For example, it can identify programs or time slots with high emotion scores and purchase those slots. This makes it possible to identify specific programs or time slots that will heighten viewer emotions and purchase those slots.

[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 advertising delivery system can further include an advertising effectiveness analysis unit. The advertising effectiveness analysis unit collects viewing data after the advertisement is delivered, and the generation AI analyzes that data to evaluate the effectiveness of the advertisement. For example, the effectiveness of the advertisement is measured based on the viewer's click rate and viewing time. The advertising effectiveness analysis unit can analyze the effectiveness of the advertisement in real time and reflect this in the next advertisement delivery. For example, it can identify time periods and programs that received a good response from viewers and optimize the next advertisement delivery strategy. This allows the advertising delivery system to continuously improve the effectiveness of the advertisement.

[0074] The ad generation unit works in conjunction with the advertiser's social media accounts to simultaneously generate advertising content for social media. For example, the generation AI is used to generate short videos and images for Instagram and Twitter. The generation AI automatically generates advertising content tailored to the characteristics of each social media platform. For example, it generates long-form ads for Facebook and short video ads for TikTok. In conjunction with the advertiser's social media accounts, the generation AI automatically generates advertising campaigns for social media. For example, it generates hashtag campaigns on social media and collaborative ads with influencers. This allows the unit to work in conjunction with the advertiser's social media accounts to simultaneously generate advertising content for social media.

[0075] The ad generation unit can generate multiple ad variations based on the viewer's emotions and select the appropriate one. For example, using an emotion estimation function, the generation AI generates multiple ad variations based on the viewer's emotions. For example, it generates an emotional version or a version that includes humor. The generation AI analyzes the viewer's emotional data and generates ad variations based on emotions. For example, it generates different versions of an ad according to the viewer's emotional score. A system is built that generates multiple ad variations and selects the most appropriate one based on the viewer's emotional response. For example, it selects the version with the highest emotional score. This allows multiple ad variations to be generated based on the viewer's emotions and the most appropriate one to be selected.

[0076] The advertising space purchasing department can simultaneously purchase radio or internet advertising space to carry out cross-media advertising. For example, radio advertising space can be purchased at the same time as advertising space on local broadcasting stations to carry out cross-media advertising. For example, the same advertisement can be broadcast simultaneously on television and radio. Internet advertising space can also be purchased at the same time to carry out online and offline advertising. For example, banner advertisements linked to television advertisements can be displayed on a website. A cross-media strategy can be used to carry out a consistent advertising campaign across television, radio, and the internet. For example, the same message can be conveyed through different media. This allows radio and internet advertising space to be purchased at the same time to carry out cross-media advertising.

[0077] The ad space purchasing unit can purchase ad space specialized for specific content, such as local events or sports broadcasts. For example, it purchases ad space specialized for local events and distributes advertisements as a sponsor of the event. For example, it selects ad space for local festivals and festivals. It purchases ad space specialized for sports broadcasts and distributes advertisements aimed at sports fans. For example, it selects ad space for broadcasts of local high school baseball or soccer games. It purchases ad space specialized for specific content and distributes advertisements tailored to the interests of viewers. For example, it selects ad space for cooking shows or travel shows. This makes it possible to purchase ad space specialized for specific content, such as local events or sports broadcasts.

[0078] The advertising space purchasing unit can identify specific programs or time slots that heighten viewer emotions and purchase those slots. For example, it can use the emotion estimation function to identify specific programs or time slots that heighten viewer emotions and purchase those slots. For example, it can select time slots for dramas or movies that move viewers. It collects viewer emotion data and has the generation AI analyze the data to identify time slots that heighten emotions. For example, it can select time slots for sports broadcasts that excite viewers. It can use the emotion estimation function to build a system that selects advertising slots based on viewer emotion scores. For example, it can identify programs or time slots with high emotion scores and purchase those slots. This makes it possible to identify specific programs or time slots that heighten viewer emotions and purchase those slots.

[0079] The ad delivery unit can adjust the timing of ad delivery based on the viewer's emotions and deliver them at the appropriate time. For example, an emotion estimation function is used to adjust the timing of ad delivery based on the viewer's emotions. For example, ads can be delivered during times when the viewer is most relaxed. Viewer emotion data is collected, and a generation AI analyzes the data to identify the optimal delivery timing. For example, ads can be delivered during times when viewers are most concentrated. A system is built that uses the emotion estimation function to adjust the timing of ad delivery based on the viewer's emotion score. For example, ads can be delivered during times when the emotion score is high. This allows the timing of ad delivery to be adjusted based on the viewer's emotions and delivered at the optimal time.

[0080] The ad generation unit can simultaneously generate not only video ads, but also radio ads or internet banner ads. For example, using generation AI, not only video ads but also radio ads can be generated at the same time. For example, the same message can be delivered as an audio ad. Internet banner ads can also be generated at the same time, and advertising can be deployed both online and offline. For example, banner ads linked to television ads can be posted on a website. A cross-media strategy can be used to deploy video ads, radio ads, and internet banner ads with a consistent message. For example, the same campaign can be run simultaneously across different media. This allows not only video ads but also radio ads and internet banner ads to be generated at the same time.

[0081] The ad delivery unit can instantly monitor viewers' emotional responses and evaluate the effectiveness of ad delivery. For example, it uses an emotion estimation function to monitor viewers' emotional responses in real time and evaluate the effectiveness of ad delivery. For example, it identifies the moment when the viewer smiles. Viewer emotional data is collected and the generation AI analyzes the data to evaluate the effectiveness of ad delivery. For example, it measures the effectiveness of ads based on the viewer's emotional score. It builds a system that monitors viewers' emotional responses in real time and has the generation AI evaluate the effectiveness of ad delivery. For example, it identifies time periods and programs with high emotional scores. This makes it possible to monitor viewers' emotional responses in real time and evaluate the effectiveness of ad delivery.

[0082] The ad generation unit can analyze past advertising data and generate advertisements that incorporate highly effective elements. For example, the generation AI analyzes past advertising data and generates advertisements that incorporate highly effective elements. For example, it incorporates designs and messages that have recorded high click-through rates. Based on advertising effectiveness data, the generation AI extracts highly effective elements and reflects them in the advertisement. For example, it generates advertisements that incorporate elements that have received a good response from viewers. The generation AI analyzes past advertising data and automatically generates advertisements that combine highly effective elements. For example, it incorporates elements from successful advertising campaigns. This makes it possible to analyze past advertising data and generate advertisements that incorporate highly effective elements.

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

[0084] Step 1: The ad space purchasing unit purchases all unpurchased ad space from local broadcasters in bulk. For example, it targets ad space for which local broadcasters themselves accept inbound orders. The ad space purchasing unit uses generation AI to analyze the viewer demographics and viewing time slots for each ad space and selects the most suitable ad space. For example, it identifies time slots that are popular with specific age groups or genders and prioritizes purchasing those slots. It collects viewer behavior data, and the generation AI analyzes that data to select ad space based on viewer interests. For example, it identifies time slots where many viewers prefer sports programs. Based on past viewing data, the generation AI analyzes viewer viewing patterns and selects the most effective ad space. For example, it purchases ad space during time slots with high viewership or to coincide with specific programs. Step 2: The ad generation unit generates an advertisement using the generation AI. For example, the generation AI generates an advertisement based on the advertiser's prompt. The generation AI generates an advertisement using a text generation AI (e.g., LLM). The generation AI can also generate advertisement content using a multimodal generation AI. The generation AI analyzes the advertisement content and automatically generates an advertisement that combines still images and background music. For example, if the prompt is "Please create an advertisement to introduce a new product," the generation AI generates an advertisement that combines product images, descriptions, and appropriate background music. Step 3: The ad distribution unit distributes the ads generated by the ad generation unit to local broadcasting station ad slots. For example, the ad generated by the generation AI is distributed to the local broadcasting station's purchased ad slots. The ad distribution unit optimizes the timing and frequency of ad distribution to maximize viewer response. For example, it distributes ads during times with high viewership or before and after specific programs. It optimizes the frequency of ad distribution to maximize viewer response. For example, rather than distributing the same ad multiple times in a short period of time, it distributes ads at appropriate intervals. Based on viewer behavior data, the generation AI adjusts the timing and frequency of ad distribution. For example, it distributes ads during times when viewers are most concentrated.

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

[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 the 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 type 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0113] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0129] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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. An advertising space purchasing department that purchases unpurchased advertising space from local broadcasting stations in bulk; an advertisement generation unit that generates advertisements using a generation AI; an advertisement distribution unit that distributes the advertisement generated by the advertisement generation unit to an advertisement slot of a local broadcasting station; A system characterized by:

2. The advertisement generation unit Analyze viewers' emotional data and generate ads that incorporate emotional elements 2. The system of claim 1.

3. The advertising space purchasing unit Select ad slots that evoke a positive emotional response from viewers 2. The system of claim 1.

4. The advertisement distribution unit Real-time monitoring of viewers' emotional responses and evaluating the effectiveness of ad delivery 2. The system of claim 1.

5. The advertisement generation unit Automatically generate customized ads tailored to the advertiser's brand image or target audience 2. The system of claim 1.

6. The advertising space purchasing unit Radio or internet advertising space is also purchased at the same time, allowing for cross-media advertising.

2. The system of claim 1.

7. The advertisement distribution unit Advertisements are distributed simultaneously not only on local broadcast stations but also on internet streaming services or social media.

2. The system of claim 1.

8. The advertising space purchasing unit Identify specific programs or timeslots that generate high emotional responses and purchase those slots.

2. The system of claim 1.

Images