system

Abstract

The system according to the embodiment aims to generate advertisements personalized for each viewer and increase the viewer's attention. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a distribution unit, an attribute acquisition unit, and an accuracy improvement unit. The reception unit inputs the viewer's name. The generation unit processes the celebrity's mouth movements and voice based on the name received by the reception unit. The distribution unit distributes the advertisements generated by the generation unit to the viewer. The attribute acquisition unit acquires viewer attribute information. The accuracy improvement unit improves the accuracy of the generation unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult to generate advertisements personalized for each viewer, and their effectiveness in attracting viewer attention is limited.

[0005] The system according to the embodiment aims to generate advertisements personalized for each viewer and increase the viewer's attention. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a distribution unit, an attribute acquisition unit, and an accuracy improvement unit. The reception unit inputs the name of the viewer. The generation unit processes the lip movements and voice of the entertainer based on the name received by the reception unit. The distribution unit distributes the advertisement generated by the generation unit to the viewer. The attribute acquisition unit acquires attribute information of the viewer. The accuracy improvement unit improves the accuracy of the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate advertisements personalized for each viewer and increase viewer attention. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) An advertisement generation system according to an embodiment of the present invention is a system in which a viewer's name is input, and a generation AI processes the celebrity's lip movements and voice to generate and distribute advertisements that call out different names for each viewer. The advertisement generation system inputs a viewer's name, and the generation AI processes the celebrity's lip movements and voice based on the input name. The generated advertisements will call out different names for each viewer. For example, the advertisement generation system inputs a viewer's name. For example, the name "Taro" is input. This information is input to the generation AI. The generation AI then processes the celebrity's lip movements and voice based on the input name. The generation AI analyzes the celebrity's lip movements and voice and processes them to match the input name. For example, the celebrity's lip movements and voice are changed to call out the name "Taro." The generated advertisements will call out different names for each viewer. For example, an advertisement that calls out each viewer's name, such as "Taro" for viewer A and "Hanako" for viewer B, can be generated. This can increase viewer attention. When viewers hear their names being called, they become more interested in the advertisement and spend more time watching it. Advertisers can also aim to increase viewing time and the number of connections to their sites. For example, this increases the probability that viewers will watch the ad to the end and visit the advertiser's site. This system is extremely useful in the advertising industry as a new method for maximizing advertising effectiveness. By providing personalized ads for each viewer, advertising effectiveness can be increased. This allows the ad generation system to input the viewer's name, and the generation AI can process the celebrity's mouth movements and voice to generate and deliver ads that call out different names for each viewer. For example, this can increase viewer attention, allowing advertisers to aim to increase viewing time and the number of connections to their sites.

[0029] An advertisement generation system according to an embodiment includes a reception unit, a generation unit, a distribution unit, an attribute acquisition unit, and an accuracy improvement unit. The reception unit inputs the viewer's name. The viewer's name may include, but is not limited to, a full name, a nickname, or a user ID. For example, the reception unit receives the viewer's name by inputting it into an input form. The reception unit can also input the viewer's name using voice input. For example, when the viewer speaks their name into a microphone, the name is converted into text data using voice recognition technology. The reception unit can also acquire the viewer's name by image input. For example, the viewer may upload an image of their handwritten name and convert the name into text data using image recognition technology. The generation unit uses a generation AI to process the celebrity's lip movements and voice based on the name received by the reception unit. For example, the generation unit uses voice synthesis technology to generate a voice that calls the input name. The generation unit can also use video editing technology to change the celebrity's lip movements to match the input name. For example, the generation AI analyzes the celebrity's lip movements and regenerates lip movements to match the input name. Furthermore, the generation unit can use the generation AI to process the celebrity's voice to match the input name. For example, the generation AI analyzes the celebrity's voice and changes the voice to call the input name. The distribution unit distributes the advertisement generated by the generation unit to the viewer. For example, the distribution unit distributes the advertisement in real time using streaming technology. The distribution unit can also provide the advertisement in a downloadable format. For example, the viewer can download the advertisement and watch it offline. The distribution unit can also distribute the advertisement in real time. For example, the generated advertisement is distributed in real time while the viewer is watching the advertisement. The attribute acquisition unit acquires attribute information of the viewer. The attribute information includes, but is not limited to, age, gender, and interests. For example, the viewer inputs the attribute information into an input form. The attribute acquisition unit can also acquire attribute information from the viewer's social media account. For example, when the viewer logs in to their social media account, the attribute acquisition unit acquires the viewer's profile information.Furthermore, the attribute acquisition unit can estimate attribute information from the viewer's behavioral history. For example, the attribute acquisition unit estimates the viewer's interests based on the content the viewer has previously viewed and their search history. The accuracy improvement unit improves the accuracy of the generation unit. For example, the accuracy improvement unit improves the accuracy of the generation AI using a machine learning algorithm. The accuracy improvement unit can also increase data. For example, the accuracy of generation can be improved by increasing the data set that the generation AI learns. Furthermore, the accuracy improvement unit can adjust the parameters of the generation AI. For example, the accuracy of generation can be improved by optimizing the hyperparameters of the generation AI. As a result, the advertising generation system according to the embodiment can input the viewer's name, and the generation AI can process the celebrity's mouth movements and voice to generate and deliver advertisements that call out different names for each viewer. For example, this can increase viewer attention, allowing advertisers to aim for increased viewing time and increased site connections.

[0030] The reception unit can analyze the viewer's past name input history and select an appropriate input method. For example, if the viewer has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the viewer has frequently used text input in the past, the reception unit can also set text input as the default input method. Furthermore, if the viewer has previously input their name during a specific time period, the reception unit can also prompt them to input during that time period. This makes it possible to provide the optimal input method based on the viewer's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the viewer's past name input history data into the generation AI and have the generation AI select the optimal input method.

[0031] When a name is input, the reception unit can present input candidates based on the viewer's interests. For example, the reception unit presents related name candidates based on keywords recently searched by the viewer. The reception unit can also present related name candidates based on content recently viewed by the viewer. The reception unit can also present names mentioned by the viewer on social media as candidates. This makes it possible to present appropriate name input candidates based on the viewer's interests. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input viewer interest data to a generation AI and cause the generation AI to present input candidates.

[0032] When entering a name, the reception unit can select an appropriate input means depending on the viewer's input method. For example, if the viewer selects voice input, the reception unit inputs the name using voice recognition technology. Furthermore, if the viewer selects text input, the reception unit can also provide keyboard input. Furthermore, if the viewer selects image input, the reception unit can also input the name using image recognition technology. This makes it possible to provide the optimal input means depending on the viewer's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the viewer's input method data into the generation AI and have the generation AI select the optimal input means.

[0033] When inputting a name, the reception unit can prioritize inputting a highly relevant name based on the viewer's geographical location information. For example, if the viewer is in a specific area, the reception unit can present names that are common in that area as candidates. Furthermore, if the viewer is traveling, the reception unit can present names related to the place names of the viewer's travel destination as candidates. Furthermore, if the viewer is participating in a specific event, the reception unit can present names related to the event as candidates. This allows highly relevant names to be input preferentially based on the viewer's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the viewer's geographical location information data to the generation AI and cause the generation AI to select highly relevant names.

[0034] When a name is entered, the reception unit can analyze the viewer's social media activity and enter a related name. For example, the reception unit can present names frequently mentioned by the viewer on social media as candidates. The reception unit can also present related names from the viewer's friend list as candidates. The reception unit can also present names related to groups or events in which the viewer participates as candidates. This makes it possible to enter a related name based on the viewer's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the viewer's social media activity data into the generation AI and cause the generation AI to select a related name.

[0035] The reception unit can customize the input method based on the viewer's past feedback when entering a name. For example, if the viewer has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the viewer has frequently used text input in the past, the reception unit can set text input as the default input method. Furthermore, if the viewer has previously entered a name during a specific time period, the reception unit can prompt the viewer to enter a name during that time period. This makes it possible to customize the input method based on the viewer's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the viewer's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0036] The generation unit can adjust the level of detail of the generation based on the importance of the name during generation. For example, if the viewer's name is important, the generation unit adjusts detailed mouth movements and voice. Furthermore, if the viewer's name is common, the generation unit can also adjust simplified mouth movements and voice. Furthermore, if the viewer's name is unusual, the generation unit can generate the name by adding special effects. This makes it possible to adjust the level of detail of the generation depending on the importance of the name. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input viewer's name data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0037] The generation unit can apply different generation algorithms depending on the name category during generation. For example, if the viewer's name is Japanese, the generation unit can apply a generation algorithm that corresponds to a pronunciation unique to Japanese. Furthermore, if the viewer's name is English, the generation unit can also apply a generation algorithm that corresponds to a pronunciation unique to English. Furthermore, if the viewer's name is multilingual, the generation unit can also apply a generation algorithm that corresponds to each language. This makes it possible to apply the optimal generation algorithm depending on the name category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the viewer's name data into the generation AI and cause the generation AI to apply the generation algorithm.

[0038] During generation, the generation unit can improve the accuracy of generation by referring to the viewer's past generation results. The generation unit generates advertisements by referring to, for example, the style of advertisements that the viewer has previously preferred. The generation unit can also improve the accuracy of generation based on data on advertisements that the viewer has previously viewed. The generation unit can also improve the accuracy of generation by reflecting the viewer's past feedback. This makes it possible to improve the accuracy of generation based on the viewer's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the viewer's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0039] During generation, the generation unit can determine the generation priority based on the time when the name was input. For example, if the name was input early, the generation unit can set the generation priority high. Also, if the name was input late, the generation unit can set the generation priority low. Also, if the name is related to a specific event, the generation unit can adjust the generation priority to match the event. This makes it possible to adjust the generation priority based on the time when the name was input. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the viewer's name was input into the generation AI and have the generation AI determine the generation priority.

[0040] The generation unit can adjust the order of generation based on the relevance of the names during generation. For example, if a name has high relevance to the content of the advertisement, the generation unit prioritizes the order of generation. Furthermore, if a name has low relevance to the content of the advertisement, the generation unit can also postpone the order of generation. Furthermore, if a name is associated with a specific target demographic, the generation unit can adjust the order of generation to suit that target demographic. In this way, the order of generation can be adjusted based on the relevance of the names. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the viewer's name relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0041] The generation unit can adjust the use of technical terminology in the generated advertisement according to the viewer's level of expertise during generation. For example, if the viewer has specialized knowledge, the generation unit can generate an advertisement that uses a lot of technical terminology. Furthermore, if the viewer has general knowledge, the generation unit can generate an advertisement that uses less technical terminology. Furthermore, if the viewer is a beginner, the generation unit can generate an advertisement that does not use technical terminology at all. This makes it possible to adjust the use of technical terminology optimally according to the viewer's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the viewer's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0042] At the time of distribution, the distribution unit can select the optimal distribution method by analyzing the viewer's past viewing history. For example, the distribution unit performs distribution by referring to the style of advertisements that the viewer has preferred to view in the past. The distribution unit can also select the optimal distribution method based on data on advertisements that the viewer has viewed in the past. The distribution unit can also customize the distribution method by reflecting the viewer's past feedback. This makes it possible to provide the optimal distribution method based on the viewer's past viewing history. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input the viewer's past viewing history data into a generation AI and have the generation AI select the optimal distribution method.

[0043] The distribution unit can customize the content of the distribution based on the viewer's current interests at the time of distribution. The distribution unit, for example, distributes relevant advertisements based on keywords recently searched by the viewer. The distribution unit can also distribute relevant advertisements based on content recently viewed by the viewer. The distribution unit can also distribute relevant advertisements based on content mentioned by the viewer on social media. This makes it possible to distribute optimal advertisements based on the viewer's interests. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input viewer interest data into a generation AI and have the generation AI customize the content of the distribution.

[0044] The distribution unit can improve the distribution method by reflecting viewer feedback during distribution. For example, the distribution unit performs distribution by referring to the style of advertisements that viewers have preferred to watch in the past. The distribution unit can also select the optimal distribution method based on data on advertisements that viewers have watched in the past. The distribution unit can also customize the distribution method by reflecting viewer feedback in the past. This makes it possible to improve the distribution method based on viewer feedback. Some or all of the above-described processing in the distribution unit may be performed using AI, for example, or may be performed without using AI. For example, the distribution unit can input viewer feedback data into a generation AI and have the generation AI improve the distribution method.

[0045] The distribution unit can select the optimal distribution method during distribution, taking into account the viewer's geographical location information. For example, if the viewer is in a specific area, the distribution unit can distribute advertisements related to that area. Furthermore, if the viewer is traveling, the distribution unit can distribute advertisements related to the name of the viewer's travel destination. Furthermore, if the viewer is participating in a specific event, the distribution unit can distribute advertisements related to the event. This makes it possible to distribute optimal advertisements based on the viewer's geographical location information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the viewer's geographical location information data into a generation AI and cause the generation AI to select the optimal distribution method.

[0046] The distribution unit can analyze the viewer's social media activity and suggest content for distribution during distribution. For example, the distribution unit can distribute advertisements related to content frequently mentioned by the viewer on social media. The distribution unit can also suggest relevant advertisements from the viewer's friend list. The distribution unit can also suggest advertisements related to groups or events in which the viewer participates. This makes it possible to suggest optimal advertisements based on the viewer's social media activity. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the viewer's social media activity data into a generation AI and have the generation AI suggest content for distribution.

[0047] The distribution unit can customize the distribution method by reflecting the viewer's past feedback during distribution. For example, the distribution unit performs distribution by referring to the style of advertisements that the viewer has previously preferred to watch. The distribution unit can also select the optimal distribution method based on data on advertisements that the viewer has previously viewed. The distribution unit can also customize the distribution method by reflecting the viewer's past feedback. This makes it possible to customize the distribution method based on the viewer's past feedback. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input the viewer's past feedback data into a generation AI and have the generation AI customize the delivery method.

[0048] When acquiring attribute information, the attribute acquisition unit can analyze the viewer's past attribute information and select the optimal acquisition method. The attribute acquisition unit selects the optimal acquisition method, for example, based on attribute information previously provided by the viewer. The attribute acquisition unit can also prioritize acquisition of related information from the viewer's past attribute information. The attribute acquisition unit can also analyze the viewer's past attribute information and select the most efficient acquisition method. This makes it possible to provide the optimal acquisition method based on the viewer's past attribute information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input the viewer's past attribute information data into the generation AI and cause the generation AI to select the optimal acquisition method.

[0049] When acquiring attribute information, the attribute acquisition unit can customize the acquired content based on the viewer's current interests. The attribute acquisition unit acquires related attribute information based on, for example, keywords recently searched by the viewer. The attribute acquisition unit can also acquire related attribute information based on content recently viewed by the viewer. The attribute acquisition unit can also acquire related attribute information based on content mentioned by the viewer on social media. This makes it possible to acquire optimal attribute information based on the viewer's interests. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input viewer interest data into the generation AI and cause the generation AI to customize the acquired content.

[0050] When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition means depending on the viewer's input method. For example, if the viewer selects voice input, the attribute acquisition unit acquires the attribute information using voice recognition technology. Furthermore, if the viewer selects text input, the attribute acquisition unit can also provide keyboard input. Furthermore, if the viewer selects image input, the attribute acquisition unit can also acquire the attribute information using image recognition technology. This makes it possible to provide the optimal acquisition means depending on the viewer's input method. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input viewer input method data into the generation AI and cause the generation AI to select the optimal acquisition means.

[0051] When acquiring attribute information, the attribute acquisition unit can prioritize acquiring highly relevant information by taking into account the viewer's geographical location information. For example, if the viewer is in a specific area, the attribute acquisition unit can prioritize acquiring attribute information related to that area. Furthermore, if the viewer is traveling, the attribute acquisition unit can prioritize acquiring attribute information related to the place name of the viewer's travel destination. Furthermore, if the viewer is participating in a specific event, the attribute acquisition unit can prioritize acquiring attribute information related to the event. This makes it possible to prioritize acquiring highly relevant attribute information based on the viewer's geographical location information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, or without, AI. For example, the attribute acquisition unit can input the viewer's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant information.

[0052] The attribute acquisition unit can analyze the viewer's social media activity and acquire related information when acquiring the attribute information. For example, the attribute acquisition unit acquires attribute information related to content frequently mentioned by the viewer on social media. The attribute acquisition unit can also acquire related attribute information from the viewer's friend list. The attribute acquisition unit can also acquire attribute information related to groups and events in which the viewer participates. This makes it possible to acquire related attribute information based on the viewer's social media activity. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input the viewer's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0053] When acquiring attribute information, the attribute acquisition unit can customize the acquisition method by reflecting the viewer's past feedback. The attribute acquisition unit, for example, selects the optimal acquisition method based on feedback provided by the viewer in the past. The attribute acquisition unit can also preferentially acquire related attribute information from the viewer's past feedback. The attribute acquisition unit can also analyze the viewer's past feedback and select the most efficient acquisition method. This makes it possible to customize the acquisition method based on the viewer's past feedback. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input the viewer's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0054] When improving accuracy, the accuracy improvement unit can analyze the viewer's past generation results and select the optimal accuracy improvement method. For example, the accuracy improvement unit performs accuracy improvement by referring to the ad styles that the viewer previously preferred. The accuracy improvement unit can also select the optimal accuracy improvement method based on data on ads that the viewer previously viewed. The accuracy improvement unit can also select an accuracy improvement method by reflecting the viewer's past feedback. This makes it possible to provide the optimal accuracy improvement method based on the viewer's past generation results. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the accuracy improvement unit can input the viewer's past generation result data into the generation AI and cause the generation AI to select the optimal accuracy improvement method.

[0055] During accuracy improvement, the accuracy improvement unit can customize accuracy improvement measures based on the viewer's current interests. For example, the accuracy improvement unit customizes relevant accuracy improvement measures based on keywords recently searched by the viewer. The accuracy improvement unit can also customize relevant accuracy improvement measures based on content recently viewed by the viewer. The accuracy improvement unit can also customize relevant accuracy improvement measures based on content mentioned by the viewer on social media. This makes it possible to provide optimal accuracy improvement measures based on the viewer's interests. Some or all of the above-described processing in the accuracy improvement unit may be performed using AI, for example, or without AI. For example, the accuracy improvement unit can input viewer interest data into the generation AI and cause the generation AI to customize the accuracy improvement measures.

[0056] The accuracy improvement unit can improve the accuracy improvement method by reflecting viewer feedback when improving accuracy. For example, the accuracy improvement unit selects the optimal accuracy improvement method based on feedback provided by viewers in the past. The accuracy improvement unit can also preferentially acquire relevant accuracy improvement means from viewers' past feedback. The accuracy improvement unit can also analyze viewers' past feedback and select the most efficient accuracy improvement method. This makes it possible to improve the accuracy improvement method based on viewer feedback. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the accuracy improvement unit can input viewer feedback data into a generation AI and cause the generation AI to improve the accuracy improvement method.

[0057] When improving accuracy, the accuracy improvement unit can select the optimal accuracy improvement method by taking into account the viewer's geographical location information. For example, if the viewer is in a specific area, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to that area. Furthermore, if the viewer is traveling, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to the place name of the viewer's travel destination. Furthermore, if the viewer is participating in a specific event, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to the event. This makes it possible to provide the optimal accuracy improvement method based on the viewer's geographical location information. Some or all of the above-described processing in the accuracy improvement unit may be performed using, or without, AI, for example. For example, the accuracy improvement unit can input the viewer's geographical location information data into the generation AI and cause the generation AI to select the optimal accuracy improvement method.

[0058] During accuracy improvement, the accuracy improvement unit can analyze the viewer's social media activity and suggest accuracy improvement measures. For example, the accuracy improvement unit can suggest accuracy improvement measures related to content frequently mentioned by the viewer on social media. The accuracy improvement unit can also suggest relevant accuracy improvement measures from the viewer's friend list. The accuracy improvement unit can also suggest accuracy improvement measures related to groups or events in which the viewer participates. This makes it possible to suggest optimal accuracy improvement measures based on the viewer's social media activity. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using, or without, AI, for example. For example, the accuracy improvement unit can input the viewer's social media activity data into the generation AI and cause the generation AI to suggest accuracy improvement measures.

[0059] When improving accuracy, the accuracy improvement unit can customize the accuracy improvement method by reflecting the viewer's past feedback. For example, the accuracy improvement unit selects the optimal accuracy improvement method based on feedback provided by the viewer in the past. The accuracy improvement unit can also preferentially acquire relevant accuracy improvement means from the viewer's past feedback. The accuracy improvement unit can also analyze the viewer's past feedback and select the most efficient accuracy improvement method. This makes it possible to customize the accuracy improvement method based on the viewer's past feedback. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the accuracy improvement unit can input the viewer's past feedback data into the generation AI and cause the generation AI to customize the accuracy improvement method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] When a viewer enters a name, the reception unit can analyze the viewer's past input history and suggest the optimal input method. For example, if the viewer has preferred voice input in the past, the reception unit can preferentially suggest voice input. Also, if the viewer has frequently used text input in the past, text input can be set as the default input method. Furthermore, if the viewer has entered their name during a specific time period in the past, the reception unit can prompt them to enter their name during that time period. This makes it possible to provide the optimal input method based on the viewer's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the viewer's past name input history data into the generation AI and have the generation AI select the optimal input method.

[0062] When inputting a name, the reception unit can prioritize inputting highly relevant names based on the viewer's geographical location information. For example, if the viewer is in a specific area, names common in that area can be presented as candidates. If the viewer is traveling, names related to the place names of the viewer's travel destination can be presented as candidates. Furthermore, if the viewer is participating in a specific event, names related to the event can be presented as candidates. This allows highly relevant names to be preferentially input based on the viewer's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the viewer's geographical location information data to the generation AI and cause the generation AI to select highly relevant names.

[0063] During generation, the generation unit can adjust the level of detail of the generation based on the importance of the name. For example, if the viewer's name is important, detailed mouth movements and voice adjustments can be made. Also, if the viewer's name is common, simplified mouth movements and voice adjustments can be made. Furthermore, if the viewer's name is unusual, special effects can be added to the name. This allows the level of detail of the generation to be adjusted according to the importance of the name. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input viewer name data into the generation AI and have the generation AI adjust the level of detail of the generation.

[0064] During generation, the generation unit can apply different generation algorithms depending on the name category. For example, if the viewer's name is Japanese, a generation algorithm corresponding to Japanese-specific pronunciation can be applied. Also, if the viewer's name is English, a generation algorithm corresponding to English-specific pronunciation can be applied. Furthermore, if the viewer's name is multilingual, a generation algorithm corresponding to each language can be applied. This makes it possible to apply the optimal generation algorithm depending on the name category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the viewer's name data into the generation AI and cause the generation AI to apply the generation algorithm.

[0065] At the time of distribution, the distribution unit can select the optimal distribution method by analyzing the viewer's past viewing history. For example, distribution can be performed by referring to the style of advertisements that the viewer has preferred to watch in the past. The optimal distribution method can also be selected based on data on advertisements that the viewer has watched in the past. Furthermore, the distribution method can be customized by reflecting the viewer's past feedback. This makes it possible to provide the optimal distribution method based on the viewer's past viewing history. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the viewer's past viewing history data into a generation AI and have the generation AI select the optimal distribution method.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs the viewer's name. The viewer's name may include a full name, nickname, or user ID. The reception unit provides a method for the viewer to input their name into an input form, a method for inputting the viewer's name using voice input, or a method for acquiring the viewer's name using image input. Step 2: The generation unit uses AI to process the celebrity's mouth movements and voice based on the name received by the reception unit. Using voice synthesis and video editing technologies, the generation unit generates a voice that calls the input name and changes the celebrity's mouth movements to match the input name. Step 3: The distribution unit distributes the advertisements generated by the generation unit to viewers. The distribution unit provides a method for distributing advertisements in real time using streaming technology or a method for providing advertisements in a downloadable format. Step 4: The attribute acquisition unit acquires viewer attribute information. Attribute information includes age, gender, and interests. The attribute acquisition unit provides a method for viewers to enter attribute information into an input form, a method for acquiring attribute information from viewers' social media accounts, and a method for inferring attribute information from viewers' behavioral history. Step 5: The accuracy improvement unit improves the accuracy of the generation unit. The accuracy improvement unit provides methods for improving the accuracy of the generation AI using machine learning algorithms, methods for augmenting data, and methods for adjusting the parameters of the generation AI.

[0068] (Example 2) An advertisement generation system according to an embodiment of the present invention is a system in which a viewer's name is input, and a generation AI processes the celebrity's lip movements and voice to generate and distribute advertisements that call out different names for each viewer. The advertisement generation system inputs a viewer's name, and the generation AI processes the celebrity's lip movements and voice based on the input name. The generated advertisements will call out different names for each viewer. For example, the advertisement generation system inputs a viewer's name. For example, the name "Taro" is input. This information is input to the generation AI. The generation AI then processes the celebrity's lip movements and voice based on the input name. The generation AI analyzes the celebrity's lip movements and voice and processes them to match the input name. For example, the celebrity's lip movements and voice are changed to call out the name "Taro." The generated advertisements will call out different names for each viewer. For example, an advertisement that calls out each viewer's name, such as "Taro" for viewer A and "Hanako" for viewer B, can be generated. This can increase viewer attention. When viewers hear their names being called, they become more interested in the advertisement and spend more time watching it. Advertisers can also aim to increase viewing time and the number of connections to their sites. For example, this increases the probability that viewers will watch the ad to the end and visit the advertiser's site. This system is extremely useful in the advertising industry as a new method for maximizing advertising effectiveness. By providing personalized ads for each viewer, advertising effectiveness can be increased. This allows the ad generation system to input the viewer's name, and the generation AI can process the celebrity's mouth movements and voice to generate and deliver ads that call out different names for each viewer. For example, this can increase viewer attention, allowing advertisers to aim to increase viewing time and the number of connections to their sites.

[0069] An advertisement generation system according to an embodiment includes a reception unit, a generation unit, a distribution unit, an attribute acquisition unit, and an accuracy improvement unit. The reception unit inputs the viewer's name. The viewer's name may include, but is not limited to, a full name, a nickname, or a user ID. For example, the reception unit receives the viewer's name by inputting it into an input form. The reception unit can also input the viewer's name using voice input. For example, when the viewer speaks their name into a microphone, the name is converted into text data using voice recognition technology. The reception unit can also acquire the viewer's name by image input. For example, the viewer may upload an image of their handwritten name and convert the name into text data using image recognition technology. The generation unit uses a generation AI to process the celebrity's lip movements and voice based on the name received by the reception unit. For example, the generation unit uses voice synthesis technology to generate a voice that calls the input name. The generation unit can also use video editing technology to change the celebrity's lip movements to match the input name. For example, the generation AI analyzes the celebrity's lip movements and regenerates lip movements to match the input name. Furthermore, the generation unit can use the generation AI to process the celebrity's voice to match the input name. For example, the generation AI analyzes the celebrity's voice and changes the voice to call the input name. The distribution unit distributes the advertisement generated by the generation unit to the viewer. For example, the distribution unit distributes the advertisement in real time using streaming technology. The distribution unit can also provide the advertisement in a downloadable format. For example, the viewer can download the advertisement and watch it offline. The distribution unit can also distribute the advertisement in real time. For example, the generated advertisement is distributed in real time while the viewer is watching the advertisement. The attribute acquisition unit acquires attribute information of the viewer. The attribute information includes, but is not limited to, age, gender, and interests. For example, the viewer inputs the attribute information into an input form. The attribute acquisition unit can also acquire attribute information from the viewer's social media account. For example, when the viewer logs in to their social media account, the attribute acquisition unit acquires the viewer's profile information.Furthermore, the attribute acquisition unit can estimate attribute information from the viewer's behavioral history. For example, the attribute acquisition unit estimates the viewer's interests based on the content the viewer has previously viewed and their search history. The accuracy improvement unit improves the accuracy of the generation unit. For example, the accuracy improvement unit improves the accuracy of the generation AI using a machine learning algorithm. The accuracy improvement unit can also increase data. For example, the accuracy of generation can be improved by increasing the data set that the generation AI learns. Furthermore, the accuracy improvement unit can adjust the parameters of the generation AI. For example, the accuracy of generation can be improved by optimizing the hyperparameters of the generation AI. As a result, the advertising generation system according to the embodiment can input the viewer's name, and the generation AI can process the celebrity's mouth movements and voice to generate and deliver advertisements that call out different names for each viewer. For example, this can increase viewer attention, allowing advertisers to aim for increased viewing time and increased site connections.

[0070] The reception unit can estimate the viewer's emotions and determine the timing for name input based on the estimated viewer's emotions. For example, if the viewer is relaxed, the reception unit can delay the timing for prompting the viewer to input their name, allowing the viewer to concentrate on the advertisement. Furthermore, if the viewer is excited, the reception unit can prompt the viewer to input their name immediately, thereby maintaining the viewer's interest. Furthermore, if the viewer is tired, the reception unit can simplify the name input process so that it can be input in a short time. This allows the viewer to be prompted to input their name at the optimal timing depending on their emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or without an AI. For example, the reception unit can input the viewer's facial expression data into the generation AI and cause the generation AI to estimate the viewer's emotions.

[0071] The reception unit can analyze the viewer's past name input history and select an appropriate input method. For example, if the viewer has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the viewer has frequently used text input in the past, the reception unit can also set text input as the default input method. Furthermore, if the viewer has previously input their name during a specific time period, the reception unit can also prompt them to input during that time period. This makes it possible to provide the optimal input method based on the viewer's past input history. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the viewer's past name input history data into the generation AI and have the generation AI select the optimal input method.

[0072] When a name is input, the reception unit can present input candidates based on the viewer's interests. For example, the reception unit presents related name candidates based on keywords recently searched by the viewer. The reception unit can also present related name candidates based on content recently viewed by the viewer. The reception unit can also present names mentioned by the viewer on social media as candidates. This makes it possible to present appropriate name input candidates based on the viewer's interests. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input viewer interest data to a generation AI and cause the generation AI to present input candidates.

[0073] When entering a name, the reception unit can select an appropriate input means depending on the viewer's input method. For example, if the viewer selects voice input, the reception unit inputs the name using voice recognition technology. Furthermore, if the viewer selects text input, the reception unit can also provide keyboard input. Furthermore, if the viewer selects image input, the reception unit can also input the name using image recognition technology. This makes it possible to provide the optimal input means depending on the viewer's input method. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the viewer's input method data into the generation AI and have the generation AI select the optimal input means.

[0074] The reception unit can estimate the viewer's emotions and determine the priority of names to be input based on the estimated viewer's emotions. For example, if the viewer is excited, the reception unit can input the viewer's name as the highest priority. Also, if the viewer is relaxed, the reception unit can input other information first, leaving the viewer's name for later. Also, if the viewer is tired, the reception unit can input a simplified version of the viewer's name. This makes it possible to adjust the input priority of names according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input viewer's emotion data into the generation AI and have the generation AI determine the priority of names.

[0075] When inputting a name, the reception unit can prioritize inputting a highly relevant name based on the viewer's geographical location information. For example, if the viewer is in a specific area, the reception unit can present names that are common in that area as candidates. Furthermore, if the viewer is traveling, the reception unit can present names related to the place names of the viewer's travel destination as candidates. Furthermore, if the viewer is participating in a specific event, the reception unit can present names related to the event as candidates. This allows highly relevant names to be input preferentially based on the viewer's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the viewer's geographical location information data to the generation AI and cause the generation AI to select highly relevant names.

[0076] When a name is entered, the reception unit can analyze the viewer's social media activity and enter a related name. For example, the reception unit can present names frequently mentioned by the viewer on social media as candidates. The reception unit can also present related names from the viewer's friend list as candidates. The reception unit can also present names related to groups or events in which the viewer participates as candidates. This makes it possible to enter a related name based on the viewer's social media activity. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the viewer's social media activity data into the generation AI and cause the generation AI to select a related name.

[0077] The reception unit can customize the input method based on the viewer's past feedback when entering a name. For example, if the viewer has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the viewer has frequently used text input in the past, the reception unit can set text input as the default input method. Furthermore, if the viewer has previously entered a name during a specific time period, the reception unit can prompt the viewer to enter a name during that time period. This makes it possible to customize the input method based on the viewer's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, AI, for example. For example, the reception unit can input the viewer's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0078] The generation unit can estimate the viewer's emotions and determine the presentation method of the advertisement to be generated based on the estimated viewer's emotions. For example, if the viewer is relaxed, the generation unit can generate an advertisement that calls the viewer's name in a gentle tone. If the viewer is excited, the generation unit can also generate an advertisement that calls the viewer's name in an energetic tone. If the viewer is sad, the generation unit can also generate an advertisement that calls the viewer's name in a gentle tone. This allows the presentation method of the advertisement to be adjusted according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input viewer emotion data into the generation AI and have the generation AI determine the presentation method of the advertisement.

[0079] The generation unit can adjust the level of detail of the generation based on the importance of the name during generation. For example, if the viewer's name is important, the generation unit adjusts detailed mouth movements and voice. Furthermore, if the viewer's name is common, the generation unit can also adjust simplified mouth movements and voice. Furthermore, if the viewer's name is unusual, the generation unit can generate the name by adding special effects. This makes it possible to adjust the level of detail of the generation depending on the importance of the name. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input viewer's name data into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0080] The generation unit can apply different generation algorithms depending on the name category during generation. For example, if the viewer's name is Japanese, the generation unit can apply a generation algorithm that corresponds to a pronunciation unique to Japanese. Furthermore, if the viewer's name is English, the generation unit can also apply a generation algorithm that corresponds to a pronunciation unique to English. Furthermore, if the viewer's name is multilingual, the generation unit can also apply a generation algorithm that corresponds to each language. This makes it possible to apply the optimal generation algorithm depending on the name category. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the viewer's name data into the generation AI and cause the generation AI to apply the generation algorithm.

[0081] During generation, the generation unit can improve the accuracy of generation by referring to the viewer's past generation results. The generation unit generates advertisements by referring to, for example, the style of advertisements that the viewer has previously preferred. The generation unit can also improve the accuracy of generation based on data on advertisements that the viewer has previously viewed. The generation unit can also improve the accuracy of generation by reflecting the viewer's past feedback. This makes it possible to improve the accuracy of generation based on the viewer's past generation results. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the viewer's past generation result data into the generation AI and cause the generation AI to improve the accuracy of generation.

[0082] The generation unit can estimate the viewer's emotions and adjust the length of the advertisement to be generated based on the estimated viewer's emotions. For example, if the viewer is relaxed, the generation unit can generate a longer advertisement. If the viewer is in a hurry, the generation unit can also generate a shorter advertisement. If the viewer is excited, the generation unit can also generate an advertisement with a visually stimulating effect. This allows the advertisement length to be adjusted according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input viewer emotion data into the generation AI and cause the generation AI to adjust the advertisement length.

[0083] During generation, the generation unit can determine the generation priority based on the time when the name was input. For example, if the name was input early, the generation unit can set the generation priority high. Also, if the name was input late, the generation unit can set the generation priority low. Also, if the name is related to a specific event, the generation unit can adjust the generation priority to match the event. This makes it possible to adjust the generation priority based on the time when the name was input. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the time when the viewer's name was input into the generation AI and have the generation AI determine the generation priority.

[0084] The generation unit can adjust the order of generation based on the relevance of the names during generation. For example, if a name has high relevance to the content of the advertisement, the generation unit prioritizes the order of generation. Furthermore, if a name has low relevance to the content of the advertisement, the generation unit can also postpone the order of generation. Furthermore, if a name is associated with a specific target demographic, the generation unit can adjust the order of generation to suit that target demographic. In this way, the order of generation can be adjusted based on the relevance of the names. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the viewer's name relevance data into the generation AI and cause the generation AI to adjust the order of generation.

[0085] The generation unit can adjust the use of technical terminology in the generated advertisement according to the viewer's level of expertise during generation. For example, if the viewer has specialized knowledge, the generation unit can generate an advertisement that uses a lot of technical terminology. Furthermore, if the viewer has general knowledge, the generation unit can generate an advertisement that uses less technical terminology. Furthermore, if the viewer is a beginner, the generation unit can generate an advertisement that does not use technical terminology at all. This makes it possible to adjust the use of technical terminology optimally according to the viewer's level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the viewer's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0086] The distribution unit can estimate the viewer's emotions and adjust the timing of distribution based on the estimated viewer's emotions. For example, if the viewer is relaxed, the distribution unit can delay the timing of advertisement distribution. Furthermore, if the viewer is excited, the distribution unit can immediately distribute advertisements. Furthermore, if the viewer is tired, the distribution unit can refrain from distributing advertisements. This allows advertisements to be distributed at the optimal timing according to the viewer's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input viewer's emotion data into the generation AI and have the generation AI adjust the distribution timing.

[0087] At the time of distribution, the distribution unit can select the optimal distribution method by analyzing the viewer's past viewing history. For example, the distribution unit performs distribution by referring to the style of advertisements that the viewer has preferred to view in the past. The distribution unit can also select the optimal distribution method based on data on advertisements that the viewer has viewed in the past. The distribution unit can also customize the distribution method by reflecting the viewer's past feedback. This makes it possible to provide the optimal distribution method based on the viewer's past viewing history. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input the viewer's past viewing history data into a generation AI and have the generation AI select the optimal distribution method.

[0088] The distribution unit can customize the content of the distribution based on the viewer's current interests at the time of distribution. The distribution unit, for example, distributes relevant advertisements based on keywords recently searched by the viewer. The distribution unit can also distribute relevant advertisements based on content recently viewed by the viewer. The distribution unit can also distribute relevant advertisements based on content mentioned by the viewer on social media. This makes it possible to distribute optimal advertisements based on the viewer's interests. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input viewer interest data into a generation AI and have the generation AI customize the content of the distribution.

[0089] The distribution unit can improve the distribution method by reflecting viewer feedback during distribution. For example, the distribution unit performs distribution by referring to the style of advertisements that viewers have preferred to watch in the past. The distribution unit can also select the optimal distribution method based on data on advertisements that viewers have watched in the past. The distribution unit can also customize the distribution method by reflecting viewer feedback in the past. This makes it possible to improve the distribution method based on viewer feedback. Some or all of the above-described processing in the distribution unit may be performed using AI, for example, or may be performed without using AI. For example, the distribution unit can input viewer feedback data into a generation AI and have the generation AI improve the distribution method.

[0090] The distribution unit can estimate the viewer's emotions and determine the priority of advertisements to be delivered based on the estimated viewer's emotions. For example, if the viewer is relaxed, the distribution unit can prioritize delivering advertisements that match the viewer's interests. Furthermore, if the viewer is excited, the distribution unit can prioritize delivering visually stimulating advertisements. Furthermore, if the viewer is tired, the distribution unit can prioritize delivering short and concise advertisements. This allows the priority of advertisements to be adjusted according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the distribution unit can be performed using, for example, AI, or without AI. For example, the distribution unit can input viewer emotion data into the generation AI and have the generation AI determine the priority of advertisements.

[0091] The distribution unit can select the optimal distribution method during distribution, taking into account the viewer's geographical location information. For example, if the viewer is in a specific area, the distribution unit can distribute advertisements related to that area. Furthermore, if the viewer is traveling, the distribution unit can distribute advertisements related to the name of the viewer's travel destination. Furthermore, if the viewer is participating in a specific event, the distribution unit can distribute advertisements related to the event. This makes it possible to distribute optimal advertisements based on the viewer's geographical location information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the viewer's geographical location information data into a generation AI and cause the generation AI to select the optimal distribution method.

[0092] The distribution unit can analyze the viewer's social media activity and suggest content for distribution during distribution. For example, the distribution unit can distribute advertisements related to content frequently mentioned by the viewer on social media. The distribution unit can also suggest relevant advertisements from the viewer's friend list. The distribution unit can also suggest advertisements related to groups or events in which the viewer participates. This makes it possible to suggest optimal advertisements based on the viewer's social media activity. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the viewer's social media activity data into a generation AI and have the generation AI suggest content for distribution.

[0093] The distribution unit can customize the distribution method by reflecting the viewer's past feedback during distribution. For example, the distribution unit performs distribution by referring to the style of advertisements that the viewer has previously preferred to watch. The distribution unit can also select the optimal distribution method based on data on advertisements that the viewer has previously viewed. The distribution unit can also customize the distribution method by reflecting the viewer's past feedback. This makes it possible to customize the distribution method based on the viewer's past feedback. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input the viewer's past feedback data into a generation AI and have the generation AI customize the delivery method.

[0094] The attribute acquisition unit can estimate the viewer's emotions and adjust the timing of acquiring attribute information based on the estimated viewer's emotions. For example, the attribute acquisition unit delays the acquisition of attribute information when the viewer is relaxed. The attribute acquisition unit can also immediately acquire attribute information when the viewer is excited. The attribute acquisition unit can also simplify the acquisition of attribute information when the viewer is tired. This allows attribute information to be acquired at the optimal timing depending on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the attribute acquisition unit can input viewer emotion data to the generation AI and cause the generation AI to adjust the timing of acquiring attribute information.

[0095] When acquiring attribute information, the attribute acquisition unit can analyze the viewer's past attribute information and select the optimal acquisition method. The attribute acquisition unit selects the optimal acquisition method, for example, based on attribute information previously provided by the viewer. The attribute acquisition unit can also prioritize acquisition of related information from the viewer's past attribute information. The attribute acquisition unit can also analyze the viewer's past attribute information and select the most efficient acquisition method. This makes it possible to provide the optimal acquisition method based on the viewer's past attribute information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input the viewer's past attribute information data into the generation AI and cause the generation AI to select the optimal acquisition method.

[0096] When acquiring attribute information, the attribute acquisition unit can customize the acquired content based on the viewer's current interests. The attribute acquisition unit acquires related attribute information based on, for example, keywords recently searched by the viewer. The attribute acquisition unit can also acquire related attribute information based on content recently viewed by the viewer. The attribute acquisition unit can also acquire related attribute information based on content mentioned by the viewer on social media. This makes it possible to acquire optimal attribute information based on the viewer's interests. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input viewer interest data into the generation AI and cause the generation AI to customize the acquired content.

[0097] When acquiring attribute information, the attribute acquisition unit can select the optimal acquisition means depending on the viewer's input method. For example, if the viewer selects voice input, the attribute acquisition unit acquires the attribute information using voice recognition technology. Furthermore, if the viewer selects text input, the attribute acquisition unit can also provide keyboard input. Furthermore, if the viewer selects image input, the attribute acquisition unit can also acquire the attribute information using image recognition technology. This makes it possible to provide the optimal acquisition means depending on the viewer's input method. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input viewer input method data into the generation AI and cause the generation AI to select the optimal acquisition means.

[0098] The attribute acquisition unit can estimate the viewer's emotions and determine the priority of attribute information to be acquired based on the estimated viewer's emotions. For example, if the viewer is excited, the attribute acquisition unit can prioritize acquiring attribute information related to the viewer's interests. Furthermore, if the viewer is relaxed, the attribute acquisition unit can prioritize acquiring the viewer's basic attribute information. Furthermore, if the viewer is tired, the attribute acquisition unit can prioritize acquiring the viewer's simplified attribute information. This allows the priority of attribute information to be adjusted according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the attribute acquisition unit can be performed using, for example, an AI, or without an AI. For example, the attribute acquisition unit can input viewer emotion data into the generation AI and have the generation AI determine the priority of the attribute information.

[0099] When acquiring attribute information, the attribute acquisition unit can prioritize acquiring highly relevant information by taking into account the viewer's geographical location information. For example, if the viewer is in a specific area, the attribute acquisition unit can prioritize acquiring attribute information related to that area. Furthermore, if the viewer is traveling, the attribute acquisition unit can prioritize acquiring attribute information related to the place name of the viewer's travel destination. Furthermore, if the viewer is participating in a specific event, the attribute acquisition unit can prioritize acquiring attribute information related to the event. This makes it possible to prioritize acquiring highly relevant attribute information based on the viewer's geographical location information. Some or all of the above-described processing in the attribute acquisition unit may be performed using, or without, AI. For example, the attribute acquisition unit can input the viewer's geographical location information data to the generation AI and cause the generation AI to acquire highly relevant information.

[0100] The attribute acquisition unit can analyze the viewer's social media activity and acquire related information when acquiring the attribute information. For example, the attribute acquisition unit acquires attribute information related to content frequently mentioned by the viewer on social media. The attribute acquisition unit can also acquire related attribute information from the viewer's friend list. The attribute acquisition unit can also acquire attribute information related to groups and events in which the viewer participates. This makes it possible to acquire related attribute information based on the viewer's social media activity. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input the viewer's social media activity data into the generation AI and cause the generation AI to acquire related information.

[0101] When acquiring attribute information, the attribute acquisition unit can customize the acquisition method by reflecting the viewer's past feedback. The attribute acquisition unit, for example, selects the optimal acquisition method based on feedback provided by the viewer in the past. The attribute acquisition unit can also preferentially acquire related attribute information from the viewer's past feedback. The attribute acquisition unit can also analyze the viewer's past feedback and select the most efficient acquisition method. This makes it possible to customize the acquisition method based on the viewer's past feedback. Some or all of the above-described processing in the attribute acquisition unit may be performed using, for example, AI, or may be performed without using AI. For example, the attribute acquisition unit can input the viewer's past feedback data into the generation AI and cause the generation AI to customize the acquisition method.

[0102] The accuracy improvement unit can estimate the viewer's emotions and adjust the accuracy improvement method based on the estimated viewer's emotions. For example, if the viewer is relaxed, the accuracy improvement unit can adjust the accuracy improvement method to a calm tone. If the viewer is excited, the accuracy improvement unit can also adjust the accuracy improvement method to an energetic tone. If the viewer is sad, the accuracy improvement unit can also adjust the accuracy improvement method to a gentle tone. This allows accuracy improvement to be performed in an optimal manner depending on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the accuracy improvement unit can be performed using AI, for example, or without AI. For example, the accuracy improvement unit can input viewer emotion data into the generation AI and cause the generation AI to adjust the accuracy improvement method.

[0103] When improving accuracy, the accuracy improvement unit can analyze the viewer's past generation results and select the optimal accuracy improvement method. For example, the accuracy improvement unit performs accuracy improvement by referring to the ad styles that the viewer previously preferred. The accuracy improvement unit can also select the optimal accuracy improvement method based on data on ads that the viewer previously viewed. The accuracy improvement unit can also select an accuracy improvement method by reflecting the viewer's past feedback. This makes it possible to provide the optimal accuracy improvement method based on the viewer's past generation results. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using, for example, AI, or may be performed without using AI. For example, the accuracy improvement unit can input the viewer's past generation result data into the generation AI and cause the generation AI to select the optimal accuracy improvement method.

[0104] During accuracy improvement, the accuracy improvement unit can customize accuracy improvement measures based on the viewer's current interests. For example, the accuracy improvement unit customizes relevant accuracy improvement measures based on keywords recently searched by the viewer. The accuracy improvement unit can also customize relevant accuracy improvement measures based on content recently viewed by the viewer. The accuracy improvement unit can also customize relevant accuracy improvement measures based on content mentioned by the viewer on social media. This makes it possible to provide optimal accuracy improvement measures based on the viewer's interests. Some or all of the above-described processing in the accuracy improvement unit may be performed using AI, for example, or without AI. For example, the accuracy improvement unit can input viewer interest data into the generation AI and cause the generation AI to customize the accuracy improvement measures.

[0105] The accuracy improvement unit can improve the accuracy improvement method by reflecting viewer feedback when improving accuracy. For example, the accuracy improvement unit selects the optimal accuracy improvement method based on feedback provided by viewers in the past. The accuracy improvement unit can also preferentially acquire relevant accuracy improvement means from viewers' past feedback. The accuracy improvement unit can also analyze viewers' past feedback and select the most efficient accuracy improvement method. This makes it possible to improve the accuracy improvement method based on viewer feedback. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the accuracy improvement unit can input viewer feedback data into a generation AI and cause the generation AI to improve the accuracy improvement method.

[0106] The accuracy improvement unit can estimate the viewer's emotions and determine the priority of accuracy improvement based on the estimated viewer's emotions. For example, if the viewer is excited, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to the viewer's interests. Furthermore, if the viewer is relaxed, the accuracy improvement unit can prioritize acquiring basic accuracy improvement measures for the viewer. Furthermore, if the viewer is tired, the accuracy improvement unit can prioritize acquiring simplified accuracy improvement measures for the viewer. This allows the accuracy improvement priority to be adjusted according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the accuracy improvement unit can be performed using, for example, an AI, or without an AI. For example, the accuracy improvement unit can input viewer emotion data into the generation AI and have the generation AI determine the priority of accuracy improvement.

[0107] When improving accuracy, the accuracy improvement unit can select the optimal accuracy improvement method by taking into account the viewer's geographical location information. For example, if the viewer is in a specific area, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to that area. Furthermore, if the viewer is traveling, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to the place name of the viewer's travel destination. Furthermore, if the viewer is participating in a specific event, the accuracy improvement unit can prioritize acquiring accuracy improvement measures related to the event. This makes it possible to provide the optimal accuracy improvement method based on the viewer's geographical location information. Some or all of the above-described processing in the accuracy improvement unit may be performed using, or without, AI, for example. For example, the accuracy improvement unit can input the viewer's geographical location information data into the generation AI and cause the generation AI to select the optimal accuracy improvement method.

[0108] During accuracy improvement, the accuracy improvement unit can analyze the viewer's social media activity and suggest accuracy improvement measures. For example, the accuracy improvement unit can suggest accuracy improvement measures related to content frequently mentioned by the viewer on social media. The accuracy improvement unit can also suggest relevant accuracy improvement measures from the viewer's friend list. The accuracy improvement unit can also suggest accuracy improvement measures related to groups or events in which the viewer participates. This makes it possible to suggest optimal accuracy improvement measures based on the viewer's social media activity. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using, or without, AI, for example. For example, the accuracy improvement unit can input the viewer's social media activity data into the generation AI and cause the generation AI to suggest accuracy improvement measures.

[0109] When improving accuracy, the accuracy improvement unit can customize the accuracy improvement method by reflecting the viewer's past feedback. For example, the accuracy improvement unit selects the optimal accuracy improvement method based on feedback provided by the viewer in the past. The accuracy improvement unit can also preferentially acquire relevant accuracy improvement means from the viewer's past feedback. The accuracy improvement unit can also analyze the viewer's past feedback and select the most efficient accuracy improvement method. This makes it possible to customize the accuracy improvement method based on the viewer's past feedback. Some or all of the above-mentioned processing in the accuracy improvement unit may be performed using AI, for example, or may be performed without using AI. For example, the accuracy improvement unit can input the viewer's past feedback data into the generation AI and cause the generation AI to customize the accuracy improvement method. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, generation unit, distribution unit, attribute acquisition unit, and accuracy improvement unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the viewer's name. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and processes the celebrity's mouth movements and voice using the generation AI. The distribution unit distributes advertisements to viewers via the communication I / F 44 of the smart device 14. The attribute acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires viewer attribute information. The accuracy improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the accuracy of the generation AI. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, generation unit, distribution unit, attribute acquisition unit, and accuracy improvement unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the viewer's name. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and processes the celebrity's mouth movements and voice using a generation AI. The distribution unit distributes advertisements to viewers via the communication I / F 44 of the smart glasses 214. The attribute acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires viewer attribute information. The accuracy improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the accuracy of the generation AI. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, distribution unit, attribute acquisition unit, and accuracy improvement unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the viewer's name. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and processes the celebrity's mouth movements and voice using a generation AI. The distribution unit distributes advertisements to viewers via the communication I / F 44 of the headset type terminal 314. The attribute acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires viewer attribute information. The accuracy improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the accuracy of the generation AI. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, distribution unit, attribute acquisition unit, and accuracy improvement unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the viewer's name. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and processes the celebrity's mouth movements and voice using the generation AI. The distribution unit distributes advertisements to viewers via the communication I / F 44 of the robot 414. The attribute acquisition unit is realized by the specific processing unit 290 of the data processing device 12 and acquires viewer attribute information. The accuracy improvement unit is realized by the specific processing unit 290 of the data processing device 12 and improves the accuracy of the generation AI.

[0110] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0111] When a viewer enters a name, the reception unit can analyze the viewer's past input history and suggest the optimal input method. For example, if the viewer has preferred voice input in the past, the reception unit can preferentially suggest voice input. Also, if the viewer has frequently used text input in the past, text input can be set as the default input method. Furthermore, if the viewer has entered their name during a specific time period in the past, the reception unit can prompt them to enter their name during that time period. This makes it possible to provide the optimal input method based on the viewer's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the viewer's past name input history data into the generation AI and have the generation AI select the optimal input method.

[0112] The reception unit can estimate the viewer's emotions and determine the timing for name input based on the estimated viewer's emotions. For example, if the viewer is relaxed, the timing for prompting the viewer to input their name can be delayed to allow the viewer to concentrate on the advertisement. Also, if the viewer is excited, the timing for prompting the viewer to input their name can be delayed to maintain the viewer's interest. Furthermore, if the viewer is tired, the name input process can be simplified to allow the viewer to input their name in a short time. This allows the viewer to be prompted to input their name at the optimal timing depending on their emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input the viewer's facial expression data into the generation AI and cause the generation AI to estimate the viewer's emotions.

[0113] The generation unit can estimate the viewer's emotions and determine the display method of the advertisement to be generated based on the estimated viewer's emotions. For example, if the viewer is relaxed, an advertisement can be generated that calls the viewer's name in a gentle tone. If the viewer is excited, an advertisement can be generated that calls the viewer's name in an energetic tone. Furthermore, if the viewer is sad, an advertisement can be generated that calls the viewer's name in a gentle tone. This allows the advertisement display method to be adjusted according to the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input viewer's emotion data into the generation AI and have the generation AI determine the display method of the advertisement.

[0114] The distribution unit can estimate the viewer's emotions and adjust the timing of distribution based on the estimated viewer's emotions. For example, if the viewer is relaxed, the timing of advertisement distribution can be delayed. Also, if the viewer is excited, the timing of advertisement distribution can be delayed. Furthermore, if the viewer is tired, the timing of advertisement distribution can be refrained from. This allows advertisements to be distributed at the optimal timing according to the viewer's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit can be performed using, for example, AI, or without AI. For example, the distribution unit can input viewer's emotion data into the generation AI and have the generation AI adjust the timing of distribution.

[0115] The attribute acquisition unit can estimate the viewer's emotions and adjust the timing of attribute information acquisition based on the estimated viewer's emotions. For example, if the viewer is relaxed, the acquisition of attribute information can be delayed. Also, if the viewer is excited, the attribute information can be acquired immediately. Furthermore, if the viewer is tired, the acquisition of attribute information can be simplified. This allows attribute information to be acquired at the optimal timing depending on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the attribute acquisition unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the attribute acquisition unit can input viewer emotion data into the generation AI and cause the generation AI to adjust the timing of attribute information acquisition.

[0116] When inputting a name, the reception unit can prioritize inputting highly relevant names based on the viewer's geographical location information. For example, if the viewer is in a specific area, names common in that area can be presented as candidates. If the viewer is traveling, names related to the place names of the viewer's travel destination can be presented as candidates. Furthermore, if the viewer is participating in a specific event, names related to the event can be presented as candidates. This allows highly relevant names to be preferentially input based on the viewer's geographical location information. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the viewer's geographical location information data to the generation AI and cause the generation AI to select highly relevant names.

[0117] During generation, the generation unit can adjust the level of detail of the generation based on the importance of the name. For example, if the viewer's name is important, detailed mouth movements and voice adjustments can be made. Also, if the viewer's name is common, simplified mouth movements and voice adjustments can be made. Furthermore, if the viewer's name is unusual, special effects can be added to the name. This allows the level of detail of the generation to be adjusted according to the importance of the name. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input viewer name data into the generation AI and have the generation AI adjust the level of detail of the generation.

[0118] During generation, the generation unit can apply different generation algorithms depending on the name category. For example, if the viewer's name is Japanese, a generation algorithm corresponding to Japanese-specific pronunciation can be applied. Also, if the viewer's name is English, a generation algorithm corresponding to English-specific pronunciation can be applied. Furthermore, if the viewer's name is multilingual, a generation algorithm corresponding to each language can be applied. This makes it possible to apply the optimal generation algorithm depending on the name category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input the viewer's name data into the generation AI and cause the generation AI to apply the generation algorithm.

[0119] At the time of distribution, the distribution unit can select the optimal distribution method by analyzing the viewer's past viewing history. For example, distribution can be performed by referring to the style of advertisements that the viewer has preferred to watch in the past. The optimal distribution method can also be selected based on data on advertisements that the viewer has watched in the past. Furthermore, the distribution method can be customized by reflecting the viewer's past feedback. This makes it possible to provide the optimal distribution method based on the viewer's past viewing history. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the viewer's past viewing history data into a generation AI and have the generation AI select the optimal distribution method.

[0120] The accuracy improvement unit can estimate the viewer's emotions and adjust the accuracy improvement method based on the estimated viewer's emotions. For example, if the viewer is relaxed, the accuracy improvement method can be adjusted to a calm tone. If the viewer is excited, the accuracy improvement method can be adjusted to an energetic tone. Furthermore, if the viewer is sad, the accuracy improvement method can be adjusted to a gentle tone. This allows accuracy improvement to be performed in an optimal manner depending on the viewer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the accuracy improvement unit can be performed using, for example, AI, or without AI. For example, the accuracy improvement unit can input viewer emotion data into the generation AI and cause the generation AI to adjust the accuracy improvement method.

[0121] The processing flow of the second embodiment will be briefly explained below.

[0122] Step 1: The reception unit inputs the viewer's name. The viewer's name may include a full name, nickname, or user ID. The reception unit provides a method for the viewer to input their name into an input form, a method for inputting the viewer's name using voice input, or a method for acquiring the viewer's name using image input. Step 2: The generation unit uses AI to process the celebrity's mouth movements and voice based on the name received by the reception unit. Using voice synthesis and video editing technologies, the generation unit generates a voice that calls the input name and changes the celebrity's mouth movements to match the input name. Step 3: The distribution unit distributes the advertisements generated by the generation unit to viewers. The distribution unit provides a method for distributing advertisements in real time using streaming technology or a method for providing advertisements in a downloadable format. Step 4: The attribute acquisition unit acquires viewer attribute information. Attribute information includes age, gender, and interests. The attribute acquisition unit provides a method for viewers to enter attribute information into an input form, a method for acquiring attribute information from viewers' social media accounts, and a method for inferring attribute information from viewers' behavioral history. Step 5: The accuracy improvement unit improves the accuracy of the generation unit. The accuracy improvement unit provides methods for improving the accuracy of the generation AI using machine learning algorithms, methods for augmenting data, and methods for adjusting the parameters of the generation AI.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0153] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0160] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0170] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception section for inputting the viewer's name; a generation unit that processes the mouth movements and voice of the entertainer based on the name accepted by the acceptance unit; a distribution unit that distributes the advertisement generated by the generation unit to viewers; an attribute acquisition unit that acquires attribute information of viewers; an accuracy improvement unit that improves the accuracy of the generation unit; A system characterized by:

2. The reception unit Estimate the viewer's emotions and determine the timing of name input based on the estimated viewer emotions 2. The system of claim 1.

3. The reception unit Analyze the viewer's past name entry history and select the appropriate entry method 2. The system of claim 1.

4. The reception unit When typing a name, suggestions based on the viewer's interests are provided 2. The system of claim 1.

5. The reception unit When entering a name, choose the appropriate input method depending on how the viewer types the name 2. The system of claim 1.

6. The reception unit Estimate the viewer's sentiment and prioritize the names to be entered based on the estimated viewer's sentiment 2. The system of claim 1.

7. The reception unit When entering names, prioritize names that are relevant based on the viewer's geographic location 2. The system of claim 1.

8. The reception unit When entering a name, analyze your audience's social media activity and enter an appropriate name 2. The system of claim 1.

Images